{ "name": "Datanest Digital Products \u2014 machine catalogue", "base_url": "https://datanest-stores.com", "license": "Catalogue metadata is public; products are MIT-licensed digital downloads.", "endpoints": { "search": "https://datanest-stores.com/api/agent/v1/search?q=", "product": "https://datanest-stores.com/api/agent/v1/products/{id_or_slug}", "mcp": "https://datanest-stores.com/mcp" }, "count": 509, "products": [ { "product_id": "ai-llm-toolkit:openai-anthropic-migration-kit", "slug": "openai-anthropic-migration-kit", "store": "ai-llm-toolkit", "store_name": "AI Builder Pro", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "adapter", "ai", "ai agents", "anthropic", "fine-tuning", "guardrails", "llm", "migration", "openai", "prompt engineering", "rag", "typescript", "vector database" ], "title": "OpenAI to Anthropic Migration Kit", "short_description": "A concept map + tested neutral adapter for migrating between (or supporting both) the OpenAI and Anthropic APIs: system prompts, required max_tokens, tool calling, stop reasons, streaming.", "detailed_description": "A concept map + a thin adapter for moving code between the OpenAI and Anthropic APIs (or supporting both behind one interface). This is a task agents get constantly (\"switch us from GPT to Claude\", \"add a fallback provider\") and the two SDKs differ in enough places \u2014 message shape, system prompts, token params, tool calling, streaming, stop reasons \u2014 to break a naive swap. Includes: CONCEPT-MAP; src/types; src/openaiadapter; src/anthropicadapter; src/provider; test/adapters.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": "2026-08-02", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [ "Node.js 18+", "TypeScript 5+" ], "supported_versions": [ "Node 18+" ], "supported_technologies": [ "TypeScript" ], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md", "guide/01-overview.md", "guide/02-migration-walkthrough.md", "package.json", "src/anthropic_adapter.ts", "src/openai_adapter.ts", "src/provider.ts", "src/types.ts", "test/adapters.test.ts" ], "file_count": 9, "download_size_bytes": 6520, "download_size_human": "6.4 KB", "product_url": "https://datanest-stores.com/ai-llm-toolkit/#product-openai-anthropic-migration-kit", "preview_url": "https://datanest-stores.com/ai-llm-toolkit/preview-openai-anthropic-migration-kit", "demo_url": null }, { "product_id": "ai-llm-toolkit:prompt-injection-defense-kit", "slug": "prompt-injection-defense-kit", "store": "ai-llm-toolkit", "store_name": "AI Builder Pro", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "agents", "ai", "ai agents", "fine-tuning", "guardrails", "llm", "prompt engineering", "prompt-injection", "rag", "security", "typescript", "vector database" ], "title": "Prompt-Injection Defense Kit", "short_description": "Layered, tested defenses against prompt injection for LLM apps and agents: input isolation, a heuristic injection detector, an action gate, and 16+ attack tests proving what each layer stops.", "detailed_description": "Practical, testable defenses against prompt injection \u2014 the 1 security issue for LLM apps and agents. When your app feeds untrusted text (web pages, documents, emails, tool outputs) into a model, that text can carry instructions that hijack your agent. This kit gives you layered, code-level mitigations and a battery of attack tests to prove they work. Honest framing: prompt injection is not \"solved\". No single trick makes a model immune. Defense is layered risk reduction \u2014 this kit implements the layers that measurably help and shows what each does and does not stop. Includes: src/isolate; src/detect; src/gate; test/attacks; ATTACK-CATALOG.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": "2026-08-02", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [ "Node.js 18+", "TypeScript 5+" ], "supported_versions": [ "Node 18+" ], "supported_technologies": [ "TypeScript" ], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "ATTACK-CATALOG.md", "README.md", "guide/01-overview.md", "package.json", "src/detect.ts", "src/gate.ts", "src/isolate.ts", "test/attacks.test.ts" ], "file_count": 8, "download_size_bytes": 8216, "download_size_human": "8.0 KB", "product_url": "https://datanest-stores.com/ai-llm-toolkit/#product-prompt-injection-defense-kit", "preview_url": "https://datanest-stores.com/ai-llm-toolkit/preview-prompt-injection-defense-kit", "demo_url": null }, { "product_id": "ai-llm-toolkit:mcp-conformance-test-kit", "slug": "mcp-conformance-test-kit", "store": "ai-llm-toolkit", "store_name": "AI Builder Pro", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "agents", "ai", "ai agents", "ci", "conformance", "fine-tuning", "guardrails", "llm", "mcp", "prompt engineering", "rag", "testing", "typescript", "vector database" ], "title": "MCP Server Conformance Test Kit", "short_description": "A black-box conformance suite for remote MCP (Streamable HTTP) servers: verifies initialize, tools/list schema validity, tools/call contracts, and error handling. CI-ready, self-tested.", "detailed_description": "A black-box conformance suite for remote MCP servers. Point it at your Streamable-HTTP /mcp endpoint and it verifies the server behaves per the Model Context Protocol: initialize handshake, tools/list shape, tools/call success + error contracts, JSON-RPC framing, and tool-schema validity. Run it in CI so a refactor can't silently break your agent integration. Includes: src/client; src/conformance; src/run; test/conformance.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": "2026-08-02", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [ "Node.js 18+" ], "supported_versions": [ "Node 18+" ], "supported_technologies": [ "TypeScript" ], "compatibility_notes": null, "preview_snippets": [ "npm install\nMCP_URL=https://your-server.example/mcp npm run check # gates on conformance" ], "included_files": [ "README.md", "guide/01-overview.md", "guide/02-what-it-checks.md", "package.json", "src/client.ts", "src/conformance.ts", "src/run.ts", "test/conformance.test.ts" ], "file_count": 8, "download_size_bytes": 6456, "download_size_human": "6.3 KB", "product_url": "https://datanest-stores.com/ai-llm-toolkit/#product-mcp-conformance-test-kit", "preview_url": "https://datanest-stores.com/ai-llm-toolkit/preview-mcp-conformance-test-kit", "demo_url": null }, { "product_id": "ai-llm-toolkit:rag-pipeline-framework", "slug": "rag-pipeline-framework", "store": "ai-llm-toolkit", "store_name": "AI Builder Pro", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "ai agents", "fine-tuning", "guardrails", "llm", "prompt engineering", "rag", "vector database" ], "title": "RAG Pipeline Framework", "short_description": "Complete retrieval-augmented generation pipeline with document ingestion, chunking strategies, vector store integration, and evaluation.", "detailed_description": "A modular retrieval-augmented generation framework with pluggable loaders, chunking strategies, embedding backends, vector stores, hybrid search, reranking, and a full evaluation suite. Build a RAG pipeline that actually works. Not a toy demo \u2014 a framework with four chunking strategies, BM25+dense hybrid search, reranking, and RAGAS-style evaluation metrics, all runnable locally with the included in-memory vector store. --- Includes: Document loaders for plain text, Markdown, HTML, and PDF with metadata extraction; Four chunking strategies \u2014 fixed-size, recursive text splitting, semantic (embedding-based topic detection), and sentence-window; Embedding abstraction with OpenAI adapter and a deterministic demo embedder for offline testing; Embedding cache that persists to disk so re-indexing unchanged documents costs nothing; In-memory vector store with full BM25 keyword search \u2014 runs the complete pipeline with zero external services; Hybrid search combining dense similarity and BM25 via Reciprocal Rank Fusion.", "price_usd": 59, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "openai", "pypdf", "pytest" ], "supported_versions": [], "supported_technologies": [ "OpenAI", "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "pip install -r requirements.txt", "from src.loaders.text_loader import TextLoader, MarkdownLoader\nfrom src.chunking.strategies import RecursiveChunker\nfrom src.embeddings import DemoEmbedder\nfrom src.vector_store import InMemoryVectorStore\nfrom src.orchestrator import RAGOrchestrator\n\n# Load documents\nloader = MarkdownLoader()\ndocs = loader.load_directory(\"./my_docs/\", recursive=True)\n\n# Chunk with recursive strategy\nchunker = RecursiveChunker(chunk_size=1000, chunk_overlap=200)\nall_chunks = []\nfor doc in docs:\n\u2026" ], "included_files": [ "README.md", "configs/pipeline_config.yaml", "guide/01-chunking-strategy-guide.md", "guide/02-retrieval-tuning-guide.md", "guides/chunking-strategy-guide.md", "guides/retrieval-tuning-guide.md", "requirements.txt", "src/__init__.py", "src/chunking/__init__.py", "src/chunking/strategies.py", "src/embeddings.py", "src/evaluation/__init__.py", "src/loaders/__init__.py", "src/loaders/base_loader.py", "src/loaders/html_loader.py", "src/loaders/pdf_loader.py", "src/loaders/text_loader.py", "src/models.py", "src/orchestrator.py", "src/retrieval/__init__.py", "src/retrieval/reranker.py", "src/vector_store.py", "tests/conftest.py", "tests/test_pipeline.py" ], "file_count": 24, "download_size_bytes": 119141, "download_size_human": "116.3 KB", "product_url": "https://datanest-stores.com/ai-llm-toolkit/#product-rag-pipeline-framework", "preview_url": "https://datanest-stores.com/ai-llm-toolkit/preview-rag-pipeline-framework", "demo_url": "https://datanest-stores.com/ai-llm-toolkit/demo-rag-pipeline-framework/" }, { "product_id": "ai-llm-toolkit:llm-prompt-engineering-kit", "slug": "llm-prompt-engineering-kit", "store": "ai-llm-toolkit", "store_name": "AI Builder Pro", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "ai agents", "fine-tuning", "guardrails", "llm", "prompt engineering", "rag", "vector database" ], "title": "LLM Prompt Engineering Kit", "short_description": "Prompt template library, chain-of-thought patterns, few-shot examples, prompt versioning system, and A/B testing framework.", "detailed_description": "A complete prompt engineering toolkit: 30 battle-tested prompt templates, a Python-based template engine with variable substitution and few-shot support, a versioned prompt registry for tracking changes, an A/B testing harness for comparing prompt variants, and a semantic diff tool for reviewing prompt edits.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "from src.template_engine import TemplateEngine\nfrom pathlib import Path\n\nengine = TemplateEngine()\nengine.load_directory(Path(\"prompts/\"))\n\n# Get a template and render it\ntemplate = engine.get(\"extraction.entity_extraction\")\nmessages = template.build_messages(\n input_text=\"Acme Corp announced a $50M acquisition of DataCo in Austin, Texas.\"\n)\n\n# messages is ready to send to any OpenAI-compatible API:\n# [\n\u2026", "from src.registry import PromptRegistry\n\nregistry = PromptRegistry()\n\n# Version 1\nregistry.add(\n prompt_id=\"my.custom_prompt\",\n name=\"My Custom Prompt\",\n category=\"extraction\",\n content={\n \"system_message\": \"You extract product mentions from reviews.\",\n \"user_template\": \"Find products in: {review_text}\",\n },\n tags=[\"products\", \"reviews\"],\n\u2026" ], "included_files": [ "README.md", "guide/01-prompt-patterns.md", "guide/02-versioning-workflow.md", "guides/prompt-patterns.md", "guides/versioning-workflow.md", "prompts/agents/guardrail.yaml", "prompts/agents/planning_prompt.yaml", "prompts/agents/react_prompt.yaml", "prompts/agents/self_reflection.yaml", "prompts/agents/supervisor.yaml", "prompts/agents/tool_selection.yaml", "prompts/classification/intent.yaml", "prompts/classification/language.yaml", "prompts/classification/priority.yaml", "prompts/classification/sentiment.yaml", "prompts/classification/topic.yaml", "prompts/classification/toxicity.yaml", "prompts/coding/bug_fixing.yaml", "prompts/coding/code_generation.yaml", "prompts/coding/code_review.yaml", "prompts/coding/documentation.yaml", "prompts/coding/sql_generation.yaml", "prompts/coding/test_generation.yaml", "prompts/extraction/contact_extraction.yaml", "prompts/extraction/data_parsing.yaml", "prompts/extraction/entity_extraction.yaml", "prompts/extraction/key_value_extraction.yaml", "prompts/extraction/relationship_extraction.yaml", "prompts/extraction/table_extraction.yaml", "prompts/summarization/abstractive.yaml", "prompts/summarization/bullet_points.yaml", "prompts/summarization/changelog.yaml", "prompts/summarization/executive_summary.yaml", "prompts/summarization/meeting_minutes.yaml", "prompts/summarization/technical_docs.yaml", "requirements.txt", "src/__init__.py", "src/ab_testing.py", "src/chains.py", "src/diff_tool.py", "src/registry.py", "src/template_engine.py", "tests/test_registry.py", "tests/test_template_engine.py" ], "file_count": 44, "download_size_bytes": 164391, "download_size_human": "160.5 KB", "product_url": "https://datanest-stores.com/ai-llm-toolkit/#product-llm-prompt-engineering-kit", "preview_url": "https://datanest-stores.com/ai-llm-toolkit/preview-llm-prompt-engineering-kit", "demo_url": "https://datanest-stores.com/ai-llm-toolkit/demo-llm-prompt-engineering-kit/" }, { "product_id": "ai-llm-toolkit:ai-agent-framework", "slug": "ai-agent-framework", "store": "ai-llm-toolkit", "store_name": "AI Builder Pro", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "ai agents", "fine-tuning", "guardrails", "llm", "prompt engineering", "rag", "vector database" ], "title": "AI Agent Framework", "short_description": "Multi-agent orchestration system with tool calling, memory management, planning loops, and human-in-the-loop patterns.", "detailed_description": "A modular, extensible framework for building LLM-powered AI agents in Python. Provides everything you need to go from a simple chatbot to a multi-agent orchestration system: tool calling, memory management, planning loops, coordination patterns, guardrails, and human-in-the-loop approval gates. Includes: Agent base class with lifecycle hooks, event streaming, and configurable behavior; Tool registry with automatic JSON Schema generation from Python type hints; Memory management; Planning loops; Multi-agent coordination; Guardrail hooks.", "price_usd": 59, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "pip install -r requirements.txt\npython examples/basic_agent.py", "from src.agent import Agent, AgentConfig\nfrom src.tool_registry import ToolRegistry, tool\nfrom src.llm_client import MockLLMClient\n\nregistry = ToolRegistry()\n\n@registry.register\n@tool(description=\"Add two numbers\")\ndef add(a: float, b: float) -> float:\n return a + b\n\nagent = Agent(\n config=AgentConfig(name=\"calc-bot\", system_prompt=\"You do math.\"),\n llm=MockLLMClient(),\n\u2026" ], "included_files": [ "README.md", "configs/agent_config.yaml", "configs/team_config.yaml", "examples/basic_agent.py", "examples/research_agent.py", "examples/team_example.py", "guide/01-building-an-agent.md", "guide/02-tool-design.md", "guides/building-an-agent.md", "guides/tool-design.md", "requirements.txt", "src/__init__.py", "src/agent.py", "src/approval.py", "src/coordination/__init__.py", "src/coordination/message_bus.py", "src/coordination/supervisor.py", "src/guardrails.py", "src/llm_client.py", "src/memory/__init__.py", "src/memory/buffer.py", "src/memory/long_term.py", "src/memory/summarizer.py", "src/planning/__init__.py", "src/planning/plan_execute.py", "src/planning/react.py", "src/tool_registry.py", "src/tools/__init__.py", "src/tools/calculator.py", "src/tools/file_io.py", "src/tools/web_search.py", "tests/__init__.py", "tests/test_memory.py", "tests/test_tools.py" ], "file_count": 34, "download_size_bytes": 145481, "download_size_human": "142.1 KB", "product_url": "https://datanest-stores.com/ai-llm-toolkit/#product-ai-agent-framework", "preview_url": "https://datanest-stores.com/ai-llm-toolkit/preview-ai-agent-framework", "demo_url": "https://datanest-stores.com/ai-llm-toolkit/demo-ai-agent-framework/" }, { "product_id": "ai-llm-toolkit:vector-database-toolkit", "slug": "vector-database-toolkit", "store": "ai-llm-toolkit", "store_name": "AI Builder Pro", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "ai agents", "fine-tuning", "guardrails", "llm", "prompt engineering", "rag", "vector database" ], "title": "Vector Database Toolkit", "short_description": "Setup guides for Pinecone, Weaviate, ChromaDB, and pgvector with indexing strategies, hybrid search, and benchmarking scripts.", "detailed_description": "Unified interface, per-backend adapters, benchmarking harness, and migration tools for Pinecone, Weaviate, ChromaDB, and pgvector. Stop rewriting vector database code when you switch backends. One interface, four adapters, a benchmark suite that runs without external services, and migration scripts to move data between any two backends. --- Includes: Common VectorStore interface \u2014 write backend-agnostic code that works with any vector database; Four production adapters \u2014 Pinecone (serverless + pods), Weaviate (native hybrid search), ChromaDB (zero-config), pgvector (PostgreSQL); In-memory reference adapter \u2014 full brute-force + BM25 implementation for testing and benchmarks, zero external deps; Benchmarking harness \u2014 measure insert throughput, search latency at multiple top-k values, and recall@10 against brute-force ground truth; Migration tool \u2014 move vectors between any two backends with progress tracking and error handling; Indexing strategy guide \u2014 HNSW vs IVFFlat parameter tuning with per-backend configuration examples.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "Backend client libraries", "pytest for testing" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "pip install -r requirements.txt\n# Or install only what you need:\npip install pinecone-client # for Pinecone\npip install chromadb # for ChromaDB\npip install weaviate-client # for Weaviate\npip install psycopg2-binary # for pgvector", "from src.adapters.memory_adapter import InMemoryAdapter\nfrom src.interface import VectorRecord\n\n# Works the same regardless of backend\nstore = InMemoryAdapter(collection_name=\"docs\", dimension=384)\nstore.create_collection()\n\nstore.upsert([\n VectorRecord(id=\"doc_1\", vector=[0.1] * 384, text=\"Machine learning basics\"),\n VectorRecord(id=\"doc_2\", vector=[0.2] * 384, text=\"Deep learning with transformers\"),\n])\n\nresults = store.search(query_vector=[0.15] * 384, top_k=5)\nfor r in results:\n\u2026" ], "included_files": [ "README.md", "benchmarks/harness.py", "guide/01-indexing-strategies.md", "guides/indexing-strategies.md", "requirements.txt", "src/__init__.py", "src/adapters/__init__.py", "src/adapters/chroma_adapter.py", "src/adapters/memory_adapter.py", "src/adapters/pgvector_adapter.py", "src/adapters/pinecone_adapter.py", "src/adapters/weaviate_adapter.py", "src/interface.py", "src/migration.py", "tests/conftest.py", "tests/test_adapters.py" ], "file_count": 16, "download_size_bytes": 63803, "download_size_human": "62.3 KB", "product_url": "https://datanest-stores.com/ai-llm-toolkit/#product-vector-database-toolkit", "preview_url": "https://datanest-stores.com/ai-llm-toolkit/preview-vector-database-toolkit", "demo_url": "https://datanest-stores.com/ai-llm-toolkit/demo-vector-database-toolkit/" }, { "product_id": "ai-llm-toolkit:llm-evaluation-framework", "slug": "llm-evaluation-framework", "store": "ai-llm-toolkit", "store_name": "AI Builder Pro", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "ai agents", "fine-tuning", "guardrails", "llm", "prompt engineering", "rag", "vector database" ], "title": "LLM Evaluation Framework", "short_description": "Automated evaluation harnesses, custom metrics, human feedback collection, regression testing, and quality monitoring dashboards.", "detailed_description": "A comprehensive toolkit for evaluating large language model outputs with automated metrics, regression testing, LLM-as-judge scoring, and human feedback collection. Whether you're comparing prompt templates, testing a model version upgrade, or building a continuous evaluation pipeline, this framework gives you the metrics library, harness infrastructure, and reporting tools to measure quality systematically instead of eyeballing responses. Includes: 13+ built-in metrics covering lexical (BLEU, ROUGE, F1), semantic (embedding; Evaluation harness that loads JSONL/JSON/CSV datasets, runs model inference; Regression tester that compares two evaluation runs, computes per-metric and; LLM-as-judge with pre-built rubrics (helpfulness, safety, code quality,; Human feedback collection with structured annotation schemas, JSONL storage,; Report generator producing self-contained HTML dashboards and Markdown.", "price_usd": 49, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "from src.metrics import compute_all_metrics\n\nprediction = \"The capital of France is Paris.\"\nreference = \"Paris is the capital of France.\"\n\nresults = compute_all_metrics(prediction, reference)\nfor metric, score in results.items():\n print(f\" {metric}: {score:.4f}\")", "from src.eval_harness import EvalHarness\n\ndef my_model(instruction: str, input_text: str = \"\") -> str:\n # Replace with your actual model call\n return \"model response\"\n\nharness = EvalHarness(\n model_fn=my_model,\n metrics=[\"exact_match\", \"token_f1\", \"bleu\", \"rouge_l\"],\n model_name=\"my-model-v1\",\n)\n\nresults = harness.run_dataset(\"datasets/qa_sample.jsonl\")\nharness.save_results(results, f\"results/{results.run_id}.json\")\n\u2026" ], "included_files": [ "README.md", "datasets/qa_sample.jsonl", "datasets/summarization_sample.jsonl", "guide/01-evaluation-guide.md", "guides/evaluation-guide.md", "requirements.txt", "src/__init__.py", "src/dataset_runner.py", "src/eval_harness.py", "src/human_feedback.py", "src/llm_judge.py", "src/metrics.py", "src/regression_tester.py", "src/report_generator.py", "tests/test_harness.py", "tests/test_metrics.py" ], "file_count": 16, "download_size_bytes": 122331, "download_size_human": "119.5 KB", "product_url": "https://datanest-stores.com/ai-llm-toolkit/#product-llm-evaluation-framework", "preview_url": "https://datanest-stores.com/ai-llm-toolkit/preview-llm-evaluation-framework", "demo_url": "https://datanest-stores.com/ai-llm-toolkit/demo-llm-evaluation-framework/" }, { "product_id": "ai-llm-toolkit:fine-tuning-pipeline", "slug": "fine-tuning-pipeline", "store": "ai-llm-toolkit", "store_name": "AI Builder Pro", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "ai agents", "fine-tuning", "guardrails", "llm", "prompt engineering", "rag", "vector database" ], "title": "Fine-Tuning Pipeline", "short_description": "LoRA/QLoRA fine-tuning scripts, dataset preparation tools, training monitoring, model merging, and deployment automation.", "detailed_description": "End-to-end LoRA/QLoRA fine-tuning toolkit for large language models. From raw data to deployment-ready model in a single pipeline: dataset preparation, instruction templating, training orchestration, monitoring, adapter merging, and export to GGUF/SafeTensors. --- Includes: Dataset preparation engine with auto-format detection (Alpaca, OpenAI, ShareGPT, Q&A), deduplication, quality scoring, and train/val splitting; 8 instruction templates (Alpaca, ChatML, Llama2, Llama3, Mistral, Phi3, Zephyr, Raw) with correct loss masking so the model learns to generate responses, not parrot instructions; YAML-driven training config with pre-built profiles (quick test, small dataset, large dataset, code generation) and CLI override support; LoRA/QLoRA trainer that wraps HuggingFace Transformers + PEFT with one-command training, automatic tokenizer setup, and gradient checkpointing; Training monitor with anomaly detection; Model merger supporting single-adapter merge plus multi-adapter strategies (Linear, TIES, DARE) for combining task-specific adapters.", "price_usd": 59, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "pip install -r requirements.txt", "python scripts/prepare_dataset.py data/sample_instructions.jsonl \\\n --output-dir data/prepared \\\n --val-ratio 0.1" ], "included_files": [ "README.md", "configs/lora_config.yaml", "configs/qlora_config.yaml", "configs/training_profiles.yaml", "data/sample_conversations.jsonl", "data/sample_instructions.jsonl", "guide/01-data-preparation.md", "guide/02-hyperparameter-tuning.md", "guide/03-lora-explained.md", "guides/data-preparation.md", "guides/hyperparameter-tuning.md", "guides/lora-explained.md", "requirements.txt", "scripts/merge_and_export.py", "scripts/prepare_dataset.py", "scripts/run_training.py", "src/__init__.py", "src/dataset_preparation.py", "src/evaluation_hooks.py", "src/export_deploy.py", "src/instruction_templates.py", "src/lora_trainer.py", "src/model_merger.py", "src/training_config.py", "src/training_monitor.py" ], "file_count": 25, "download_size_bytes": 140725, "download_size_human": "137.4 KB", "product_url": "https://datanest-stores.com/ai-llm-toolkit/#product-fine-tuning-pipeline", "preview_url": "https://datanest-stores.com/ai-llm-toolkit/preview-fine-tuning-pipeline", "demo_url": "https://datanest-stores.com/ai-llm-toolkit/demo-fine-tuning-pipeline/" }, { "product_id": "ai-llm-toolkit:ai-safety-guardrails", "slug": "ai-safety-guardrails", "store": "ai-llm-toolkit", "store_name": "AI Builder Pro", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "ai agents", "fine-tuning", "guardrails", "llm", "prompt engineering", "rag", "vector database" ], "title": "AI Safety & Guardrails Kit", "short_description": "Input/output filtering, toxicity detection, PII redaction, hallucination detection, and content policy enforcement scripts.", "detailed_description": "Input/output filtering, PII detection, prompt injection defense, toxicity screening, and configurable policy enforcement for LLM applications. Runs entirely on Python stdlib \u2014 no external dependencies required. Includes: PII Detection & Redaction \u2014 Regex-based detection of emails, phone numbers; Prompt Injection Defense \u2014 4-layer detection; Toxicity Screening \u2014 Keyword and heuristic-based detection of threats,; Groundedness Checking \u2014 Heuristic analysis of whether LLM output is; Policy Engine \u2014 Configurable rule-based system that combines all detectors; Input & Output Filters \u2014 Unified pre/post-processing wrappers for.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "from src.input_filter import InputFilter\nfrom src.output_filter import OutputFilter\n\ninput_guard = InputFilter()\noutput_guard = OutputFilter()\n\ndef safe_chat(user_message: str) -> str:\n # Check user input\n input_result = input_guard.check(user_message)\n if not input_result.allowed:\n return input_result.rejection_message\n\n # Call your LLM with filtered text (PII already redacted)\n response = your_llm(input_result.filtered_text)\n\u2026", "from src.policy_engine import PolicyEngine, POLICY_PRESETS\n\n# Choose: \"default\", \"strict\", or \"permissive\"\nengine = PolicyEngine(POLICY_PRESETS[\"strict\"])\n\ndecision = engine.evaluate(\"My SSN is 123-45-6789\", direction=\"input\")\nprint(f\"Action: {decision.action}\") # \"redact\"\nprint(f\"Text: {decision.processed_text}\") # SSN replaced" ], "included_files": [ "README.md", "guide/01-safety-guide.md", "guides/safety-guide.md", "policies/default_policy.yaml", "policies/strict_policy.yaml", "requirements.txt", "src/__init__.py", "src/groundedness_checker.py", "src/injection_detector.py", "src/input_filter.py", "src/output_filter.py", "src/pii_detector.py", "src/policy_engine.py", "src/toxicity_detector.py", "tests/test_injection_detector.py", "tests/test_pii_detector.py", "tests/test_policy_engine.py" ], "file_count": 17, "download_size_bytes": 114028, "download_size_human": "111.4 KB", "product_url": "https://datanest-stores.com/ai-llm-toolkit/#product-ai-safety-guardrails", "preview_url": "https://datanest-stores.com/ai-llm-toolkit/preview-ai-safety-guardrails", "demo_url": "https://datanest-stores.com/ai-llm-toolkit/demo-ai-safety-guardrails/" }, { "product_id": "ai-llm-toolkit:llm-cost-optimizer", "slug": "llm-cost-optimizer", "store": "ai-llm-toolkit", "store_name": "AI Builder Pro", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "ai agents", "fine-tuning", "guardrails", "llm", "prompt engineering", "rag", "vector database" ], "title": "LLM Cost Optimizer", "short_description": "Token usage tracking, model routing for cost/quality tradeoffs, caching strategies, batch processing, and budget alerting.", "detailed_description": "Track, analyse, and reduce LLM API spending with token accounting, intelligent model routing, response caching, budget alerts, and cost reporting. Most teams discover they're spending 3-5x more than necessary on LLM APIs. This toolkit gives you per-request cost tracking, a model router that picks the cheapest model for each quality tier, exact-match response caching (typically 20-40% savings), budget alerting, and a report generator that produces HTML dashboards and Markdown summaries \u2014 all with a stdlib-only token estimator so you can project costs before making any API calls. --- Includes: Token usage tracker with per-request logging, model breakdowns, and JSONL persistence; Pricing table for OpenAI and Anthropic models with YAML-driven updates (no code changes needed); Model router that classifies prompts by complexity and selects the cheapest qualifying model; Response cache with SHA-256 exact-match, TTL expiration, LRU eviction, and JSON persistence; Budget monitor with daily/weekly/monthly thresholds, percentage-based warnings, and custom alert callbacks; Token estimator (stdlib-only) with content-type detection for English prose, code, technical, and multilingual text.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "PyYAML", "pytest" ], "supported_versions": [], "supported_technologies": [ "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "pip install -r requirements.txt", "from src.pricing import PricingTable\nfrom src.token_tracker import TokenTracker\n\npricing = PricingTable(\"configs/pricing.yaml\")\ntracker = TokenTracker(log_path=\"usage.jsonl\", pricing=pricing)\n\n# After each API call, record the usage\ntracker.record(\n model=\"gpt-4o\",\n prompt_tokens=1200,\n completion_tokens=350,\n latency_ms=820,\n metadata={\"feature\": \"customer_support\", \"user_id\": \"u-42\"},\n)\n\u2026" ], "included_files": [ "README.md", "configs/pricing.yaml", "guide/01-getting-started.md", "guide/02-core-features.md", "guide/03-advanced-features.md", "requirements.txt", "src/__init__.py", "src/budget_alert.py", "src/cache.py", "src/model_router.py", "src/pricing.py", "src/report_generator.py", "src/token_tracker.py", "src/tokenizer_estimate.py", "tests/conftest.py", "tests/test_optimizer.py" ], "file_count": 16, "download_size_bytes": 61648, "download_size_human": "60.2 KB", "product_url": "https://datanest-stores.com/ai-llm-toolkit/#product-llm-cost-optimizer", "preview_url": "https://datanest-stores.com/ai-llm-toolkit/preview-llm-cost-optimizer", "demo_url": "https://datanest-stores.com/ai-llm-toolkit/demo-llm-cost-optimizer/" }, { "product_id": "ai-llm-toolkit:conversational-ai-templates", "slug": "conversational-ai-templates", "store": "ai-llm-toolkit", "store_name": "AI Builder Pro", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "ai agents", "fine-tuning", "guardrails", "llm", "prompt engineering", "rag", "vector database" ], "title": "Conversational AI Templates", "short_description": "Chatbot frameworks with context management, multi-turn conversation handling, intent classification, and escalation workflows.", "detailed_description": "A production-ready framework for building chatbots with multi-turn dialogue management, intent classification, slot filling, escalation workflows, and channel adapters. Includes three ready-to-deploy bot configurations: support bot, FAQ bot, and booking bot.", "price_usd": 49, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "FastAPI", "Flask", "OpenAI", "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "# See the dialogue manager in action\npython src/dialogue.py\n\n# Test intent classification\npython src/intent.py\n\n# Test slot extraction\npython src/slots.py\n\n# Test the web channel adapter\npython src/channels/web.py", "from src.dialogue import DialogueFlow, DialogueState, DialogueManager\n\nflow = DialogueFlow(\n name=\"My Bot\",\n initial_state=\"greeting\",\n global_intents={\"cancel\": \"greeting\", \"escalate\": \"agent\"},\n)\n\nflow.add_state(DialogueState(\n name=\"greeting\",\n entry_message=\"Hello! How can I help?\",\n transitions={\"order_status\": \"check_order\"},\n))\n\n\u2026" ], "included_files": [ "README.md", "guide/01-building-a-chatbot.md", "guide/02-channel-integration.md", "guides/building-a-chatbot.md", "guides/channel-integration.md", "requirements.txt", "src/__init__.py", "src/channels/__init__.py", "src/channels/base.py", "src/channels/slack.py", "src/channels/web.py", "src/context_manager.py", "src/dialogue.py", "src/escalation.py", "src/intent.py", "src/slots.py", "templates/booking_bot.yaml", "templates/faq_bot.yaml", "templates/support_bot.yaml", "tests/test_context.py", "tests/test_dialogue.py", "tests/test_intent.py" ], "file_count": 22, "download_size_bytes": 146687, "download_size_human": "143.2 KB", "product_url": "https://datanest-stores.com/ai-llm-toolkit/#product-conversational-ai-templates", "preview_url": "https://datanest-stores.com/ai-llm-toolkit/preview-conversational-ai-templates", "demo_url": "https://datanest-stores.com/ai-llm-toolkit/demo-conversational-ai-templates/" }, { "product_id": "ai-llm-toolkit:document-ai-toolkit", "slug": "document-ai-toolkit", "store": "ai-llm-toolkit", "store_name": "AI Builder Pro", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "ai agents", "fine-tuning", "guardrails", "llm", "prompt engineering", "rag", "vector database" ], "title": "Document AI Toolkit", "short_description": "PDF/document parsing pipelines, OCR integration, table extraction, summarization chains, and structured data extraction.", "detailed_description": "Multi-format document parsing, intelligent chunking, and structured data extraction for LLM pipelines. Parse any document. Chunk it for RAG. Extract structured data. Summarize it. All in one toolkit. --- Includes: Multi-format parser with unified Document model; 5 chunking strategies; Table extractor that pulls tabular data from HTML and Markdown into structured formats (dicts, CSV, JSON); Schema-guided extractor that pulls specific fields from unstructured text using regex patterns or LLM function calling; Summarization chains; OCR interface with Tesseract backend, cloud OCR adapter, and mock backend for testing.", "price_usd": 49, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "from src.document_parser import parse_file\nfrom src.text_chunker import TextChunker, ChunkStrategy\n\n# Parse any document\ndoc = parse_file(\"report.pdf\")\nprint(f\"Title: {doc.title}, Words: {doc.word_count}\")\n\n# Chunk for RAG\nchunker = TextChunker(strategy=ChunkStrategy.SENTENCE, chunk_size=500)\nchunks = chunker.chunk_document(doc)\nfor chunk in chunks:\n print(f\"Chunk {chunk.chunk_index}: {chunk.char_count} chars\")\n\n# Extract structured data\n\u2026", "pip install -r requirements.txt" ], "included_files": [ "README.md", "configs/extraction_config.yaml", "guide/01-document-processing.md", "guides/document-processing.md", "requirements.txt", "src/__init__.py", "src/document_parser.py", "src/ocr_interface.py", "src/schema_extractor.py", "src/summarizer.py", "src/table_extractor.py", "src/text_chunker.py", "tests/test_chunker.py", "tests/test_parser.py" ], "file_count": 14, "download_size_bytes": 75276, "download_size_human": "73.5 KB", "product_url": "https://datanest-stores.com/ai-llm-toolkit/#product-document-ai-toolkit", "preview_url": "https://datanest-stores.com/ai-llm-toolkit/preview-document-ai-toolkit", "demo_url": "https://datanest-stores.com/ai-llm-toolkit/demo-document-ai-toolkit/" }, { "product_id": "ai-llm-toolkit:ai-api-gateway", "slug": "ai-api-gateway", "store": "ai-llm-toolkit", "store_name": "AI Builder Pro", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "ai agents", "fine-tuning", "guardrails", "llm", "prompt engineering", "rag", "vector database" ], "title": "AI API Gateway", "short_description": "Unified API for multiple LLM providers, rate limiting, fallback routing, response caching, and usage analytics dashboard.", "detailed_description": "Unified client for multiple LLM providers with rate limiting, fallback routing, response caching, request logging, and usage analytics. Stop scattering OpenAI, Anthropic, and local model calls across your codebase. This gateway gives you a single complete() call that handles provider failover with circuit breakers, token-bucket rate limiting, SHA-256 response caching, structured JSONL logging, and usage analytics \u2014 with three provider adapters (OpenAI, Anthropic, OpenAI-compatible local servers) and a base class for adding more. --- Includes: Unified client \u2014 one complete() call for all providers, one chat() shortcut for single-turn conversations; Three provider adapters \u2014 OpenAI (SDK), Anthropic (SDK), and local/OpenAI-compatible servers (stdlib urllib, no dependencies); Retry router with exponential backoff, jitter, and automatic fallback to the next provider in the chain; Circuit breaker that temporarily disables consistently-failing providers and re-tests them after a cooldown; Token bucket rate limiter (stdlib, thread-safe) with per-provider capacity and refill rates; Response cache with SHA-256 keys, TTL expiration, LRU eviction, and JSON persistence.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "openai", "anthropic", "PyYAML", "pytest" ], "supported_versions": [], "supported_technologies": [ "OpenAI", "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "pip install -r requirements.txt", "print(gateway.cache_stats)\n# {'hits': 42, 'misses': 158, 'evictions': 0, 'skips': 0, 'size': 158, 'hit_rate': 0.21}" ], "included_files": [ "README.md", "configs/gateway_config.yaml", "guide/01-deployment-guide.md", "guides/deployment-guide.md", "requirements.txt", "src/__init__.py", "src/analytics.py", "src/cache.py", "src/client.py", "src/providers/__init__.py", "src/providers/anthropic_provider.py", "src/providers/local_provider.py", "src/providers/openai_provider.py", "src/rate_limiter.py", "src/request_logger.py", "src/retry.py", "tests/conftest.py", "tests/test_gateway.py" ], "file_count": 18, "download_size_bytes": 84377, "download_size_human": "82.4 KB", "product_url": "https://datanest-stores.com/ai-llm-toolkit/#product-ai-api-gateway", "preview_url": "https://datanest-stores.com/ai-llm-toolkit/preview-ai-api-gateway", "demo_url": "https://datanest-stores.com/ai-llm-toolkit/demo-ai-api-gateway/" }, { "product_id": "ai-llm-toolkit:mcp-server-starter", "slug": "mcp-server-starter", "store": "ai-llm-toolkit", "store_name": "AI Builder Pro", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "agents", "ai", "ai agents", "cloudflare-workers", "fine-tuning", "guardrails", "llm", "mcp", "model-context-protocol", "prompt engineering", "rag", "streamable-http", "typescript", "vector database" ], "title": "MCP Server Starter", "short_description": "Production-shaped remote MCP server starter \u2014 Streamable HTTP on Cloudflare Workers, stateless + stateful patterns, typed tools, discovery, rate limiting, and Vitest tests.", "detailed_description": "A production-shaped starter for building a remote Model Context Protocol (MCP) server that any MCP client (Claude Desktop, Cursor, Cline, OpenCode, or your own agent) can connect to over the network. It uses the Streamable HTTP transport and runs on Cloudflare Workers (or any Web-standard runtime: Deno, Bun, Node 18+). This is the exact shape used to ship a live, registry-listed commerce MCP server \u2014 generalized, anonymized, and reduced to a clean starting point you can extend. Includes: Streamable HTTP transport \u2014 Web-standard Request/Response, works on; Stateless mode by default \u2014 a fresh server per request, no Durable Objects; Typed tools with zod schemas \u2014 two worked example tools (echo, add) show; Friendly discovery \u2014 a plain GET /mcp returns a JSON descriptor instead of; Rate limiting \u2014 a token-bucket limiter (in-memory, with a KV upgrade note); Tests \u2014 a Vitest suite that drives the server through the transport end to.", "price_usd": 49, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": "2026-08-02", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [ "Node.js 18+", "Cloudflare account", "Npm" ], "supported_versions": [ "Node 18+" ], "supported_technologies": [ "TypeScript" ], "compatibility_notes": null, "preview_snippets": [ "npm install\nnpm test # runs the Vitest suite against the in-process server\nnpm run dev # wrangler dev \u2014 serves /mcp locally", "curl http://localhost:8787/mcp \\\n -H 'Content-Type: application/json' \\\n -H 'Accept: application/json, text/event-stream' \\\n -d '{\"jsonrpc\":\"2.0\",\"id\":1,\"method\":\"tools/list\"}'" ], "included_files": [ "README.md", "package.json", "src/descriptor.ts", "src/ratelimit.ts", "src/server.ts", "src/transport.ts", "src/worker.ts", "test/server.test.ts", "wrangler.toml" ], "file_count": 9, "download_size_bytes": 9472, "download_size_human": "9.2 KB", "product_url": "https://datanest-stores.com/ai-llm-toolkit/#product-mcp-server-starter", "preview_url": "https://datanest-stores.com/ai-llm-toolkit/preview-mcp-server-starter", "demo_url": null }, { "product_id": "ai-llm-toolkit:mcp-commerce-integration", "slug": "mcp-commerce-integration", "store": "ai-llm-toolkit", "store_name": "AI Builder Pro", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "agents", "ai", "ai agents", "checkout", "commerce", "fine-tuning", "guardrails", "llm", "mcp", "prompt engineering", "rag", "security", "stripe", "typescript", "vector database" ], "title": "Agent Commerce Integration Pack", "short_description": "Add safe commerce to an MCP server or agent API: trusted catalogue reads and server-side price resolution with price-tampering and unaudited-product guards. Buyer authorizes payment; no autonomous charging.", "detailed_description": "Add safe commerce to an MCP server or agent HTTP API: let agents search a catalogue, understand a product, and prepare a verified checkout link \u2014 without ever being able to tamper with the price or silently charge a customer. This is the battle-tested pattern behind a live, registry-listed commerce MCP server, generalized into drop-in TypeScript modules. Includes: CatalogRepository \u2014 a shared read layer over a trusted catalogue JSON; createCheckoutLink \u2014 resolves the trusted price id server-side, creates a; Sellability gate \u2014 products flagged unaudited/deprecated/not-sellable; Redirect allowlist \u2014 success/cancel URLs must be on an allowlisted host; Idempotency \u2014 an idempotency key prevents duplicate sessions; Guardrail tests \u2014 Vitest cases that prove price tampering is rejected and.", "price_usd": 59, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": "2026-08-02", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [ "Node.js 18+", "Stripe account" ], "supported_versions": [ "Node 18+" ], "supported_technologies": [ "TypeScript" ], "compatibility_notes": null, "preview_snippets": [ "npm install\nnpm test", "import { CatalogRepository } from './src/catalog.js';\nimport { createCheckoutLink } from './src/checkout.js';\n\nconst repo = new CatalogRepository(catalogJson);\nconst res = await createCheckoutLink(repo, { STRIPE_SECRET_KEY, CHECKOUT_ALLOWED_REDIRECT_HOSTS: 'yourstore.com' }, {\n productIdOrSlug: 'store:widget',\n // NOTE: no price/amount/currency accepted \u2014 resolved server-side.\n});\nconsole.log(res.checkout_url, res.expires_at);" ], "included_files": [ "README.md", "src/catalog.ts", "src/checkout.ts", "src/errors.ts", "test/checkout.test.ts" ], "file_count": 5, "download_size_bytes": 8073, "download_size_human": "7.9 KB", "product_url": "https://datanest-stores.com/ai-llm-toolkit/#product-mcp-commerce-integration", "preview_url": "https://datanest-stores.com/ai-llm-toolkit/preview-mcp-commerce-integration", "demo_url": null }, { "product_id": "ai-llm-toolkit:agent-tool-schema-patterns", "slug": "agent-tool-schema-patterns", "store": "ai-llm-toolkit", "store_name": "AI Builder Pro", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "agents", "ai", "ai agents", "fine-tuning", "function-calling", "guardrails", "json-schema", "llm", "mcp", "prompt engineering", "rag", "tool-use", "typescript", "vector database", "zod" ], "title": "Agent Tool Schema Design Patterns", "short_description": "Field guide and runnable examples for designing reliable tool/function schemas for LLM agents (MCP, OpenAI, Anthropic): descriptions, constraints, flat inputs, error contracts, annotations, and idempotency.", "detailed_description": "A field guide + runnable examples for designing reliable tool/function schemas for LLM agents (MCP tools, OpenAI function calling, Anthropic tool use). Good schemas are the single biggest lever on whether an agent calls your tool correctly.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": "2026-08-02", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [ "Node.js 18+", "TypeScript 5+" ], "supported_versions": [ "Node 18+" ], "supported_technologies": [ "TypeScript" ], "compatibility_notes": null, "preview_snippets": [ "npm install\nnpx tsx examples/described.ts # print a well-described schema as JSON Schema", "src/\n result.ts # ok()/fail() helpers \u2014 structured content + stable error codes\n validate.ts # parse-at-the-boundary + reject-smuggled-fields helper\nexamples/\n described.ts # good vs bad field descriptions (same tool)\n flat-vs-nested.ts # why flat inputs beat nested ones\n annotations.ts # readOnly / side-effect / idempotent annotations\n idempotent.ts # an idempotent side-effecting tool\nGUIDE.md # the full write-up with rationale + before/after metrics" ], "included_files": [ "GUIDE.md", "README.md", "examples/annotations.ts", "examples/described.ts", "examples/flat-vs-nested.ts", "examples/idempotent.ts", "package.json", "src/result.ts", "src/validate.ts" ], "file_count": 9, "download_size_bytes": 9592, "download_size_human": "9.4 KB", "product_url": "https://datanest-stores.com/ai-llm-toolkit/#product-agent-tool-schema-patterns", "preview_url": "https://datanest-stores.com/ai-llm-toolkit/preview-agent-tool-schema-patterns", "demo_url": null }, { "product_id": "ai-llm-toolkit:mcp-security-checklist", "slug": "mcp-security-checklist", "store": "ai-llm-toolkit", "store_name": "AI Builder Pro", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "agents", "ai", "ai agents", "checklist", "fine-tuning", "guardrails", "llm", "mcp", "payments", "prompt engineering", "rag", "security", "threat-model", "vector database" ], "title": "MCP / Agent Server Security Checklist", "short_description": "Audit-ready security checklists and a STRIDE threat model for remote MCP servers and agent-facing APIs: auth, input validation, rate limiting, secrets, payments, and data-exposure controls.", "detailed_description": "A practical, audit-ready security checklist and threat model for anyone running a remote MCP server or an agent-facing HTTP API. Agent traffic is a new attack surface: the \"user\" may be an autonomous model steered by an untrusted prompt. This pack is the checklist used to harden a live, payment-handling MCP server before it went public. Includes: checklists/pre-launch; checklists/payments; checklists/data-exposure; THREAT-MODEL.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": "2026-08-02", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [], "supported_versions": [], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [ "checklists/\n pre-launch.md # gate before exposing to agents\n payments.md # money-movement controls\n data-exposure.md # secret / PII / path leakage prevention\nTHREAT-MODEL.md # STRIDE threats + mitigations" ], "included_files": [ "README.md", "THREAT-MODEL.md", "checklists/data-exposure.md", "checklists/payments.md", "checklists/pre-launch.md" ], "file_count": 5, "download_size_bytes": 7929, "download_size_human": "7.7 KB", "product_url": "https://datanest-stores.com/ai-llm-toolkit/#product-mcp-security-checklist", "preview_url": "https://datanest-stores.com/ai-llm-toolkit/preview-mcp-security-checklist", "demo_url": null }, { "product_id": "ai-llm-toolkit:agent-eval-harness", "slug": "agent-eval-harness", "store": "ai-llm-toolkit", "store_name": "AI Builder Pro", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "agents", "ai", "ai agents", "ci", "evaluation", "fine-tuning", "guardrails", "llm", "mcp", "prompt engineering", "rag", "testing", "typescript", "vector database" ], "title": "Agent Tool Eval Harness", "short_description": "Dependency-light evaluation harness for agent tools and MCP servers: golden cases, matchers, a runner with latency metrics, and console + JSON reporters for CI regression gating.", "detailed_description": "A tiny, dependency-light evaluation harness for agent tools and MCP servers. Define golden cases (input \u2192 expected), run them against your tool, and get a pass/fail report with latency and optional cost \u2014 so you catch regressions before your users (and their agents) do. Includes: Golden cases \u2014 declare { name, input, expect } where expect is a value,; Runner \u2014 executes cases against any async (input) => output function; Metrics \u2014 pass rate, per-case latency (p50/p95), and an optional cost hook; Matchers \u2014 exact, partial (subset match), and predicate (custom fn); Reporters \u2014 a console table and a machine-readable JSON summary for CI.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": "2026-08-02", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [ "Node.js 18+" ], "supported_versions": [ "Node 18+" ], "supported_technologies": [ "TypeScript" ], "compatibility_notes": null, "preview_snippets": [ "npm install\nnpx tsx examples/run.ts # runs the sample suite against a demo tool", "import { runSuite } from './src/harness.js';\nimport { partial } from './src/matchers.js';\n\nconst report = await runSuite('search', mySearchTool, [\n { name: 'finds by keyword', input: { query: 'airflow' }, expect: partial({ total: (n: number) => n > 0 }) },\n { name: 'empty query is safe', input: { query: '' }, expect: (out) => Array.isArray(out.results) },\n]);\n\nprocess.exit(report.failed === 0 ? 0 : 1);" ], "included_files": [ "README.md", "examples/run.ts", "package.json", "src/harness.ts", "src/matchers.ts", "src/report.ts" ], "file_count": 6, "download_size_bytes": 6182, "download_size_human": "6.0 KB", "product_url": "https://datanest-stores.com/ai-llm-toolkit/#product-agent-eval-harness", "preview_url": "https://datanest-stores.com/ai-llm-toolkit/preview-agent-eval-harness", "demo_url": null }, { "product_id": "ai-llm-toolkit:agent-discoverability-kit", "slug": "agent-discoverability-kit", "store": "ai-llm-toolkit", "store_name": "AI Builder Pro", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "agents", "ai", "ai agents", "discoverability", "fine-tuning", "guardrails", "json-ld", "llm", "llms-txt", "mcp-registry", "prompt engineering", "rag", "seo", "typescript", "vector database" ], "title": "Agent Discoverability Kit", "short_description": "Generators that make a product or site discoverable to AI agents: llms.txt, .well-known/ai-plugin.json, an MCP-registry server.json, and valid schema.org Product JSON-LD.", "detailed_description": "Make your product, store, or API discoverable and usable by AI agents. This kit generates the machine-readable surfaces agents and LLM crawlers look for: llms.txt, a .well-known/ai-plugin.json manifest, an MCP-registry server.json, and valid Product JSON-LD \u2014 all from one small product/site config. This is the discoverability layer that got a real store listed in the official MCP registry and surfaced to LLM crawlers, packaged as reusable generators. Includes: llmsTxt(site) \u2014 build a clean llms; aiPluginManifest(site) \u2014 the well-known plugin manifest; serverJson(mcp) \u2014 a schema-valid MCP registry record for a remote server; productJsonLd(product) \u2014 valid schema.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": "2026-08-02", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [ "Node.js 18+" ], "supported_versions": [ "Node 18+" ], "supported_technologies": [ "TypeScript" ], "compatibility_notes": null, "preview_snippets": [ "npm install # only needed for the TypeScript toolchain / tests\nnpx tsx examples/generate.ts # prints all four artifacts for a sample store", "import { llmsTxt, productJsonLd } from './src/index.js';\n\nconst txt = llmsTxt({\n name: 'Acme Tools',\n baseUrl: 'https://acme.example',\n summary: 'Developer toolkits and templates.',\n mcpUrl: 'https://acme.example/mcp',\n products: [{ slug: 'widget', name: 'Widget Kit', price: 49, desc: 'A widget kit.' }],\n});" ], "included_files": [ "README.md", "examples/generate.ts", "package.json", "src/index.ts", "src/jsonld.ts", "src/llms.ts", "src/manifest.ts" ], "file_count": 7, "download_size_bytes": 6796, "download_size_human": "6.6 KB", "product_url": "https://datanest-stores.com/ai-llm-toolkit/#product-agent-discoverability-kit", "preview_url": "https://datanest-stores.com/ai-llm-toolkit/preview-agent-discoverability-kit", "demo_url": null }, { "product_id": "ai-llm-toolkit:ai-llm-complete-bundle", "slug": "ai-llm-complete-bundle", "store": "ai-llm-toolkit", "store_name": "AI Builder Pro", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "ai agents", "bundle", "fine-tuning", "guardrails", "llm", "prompt engineering", "rag", "vector database" ], "title": "AI Builder Bundle", "short_description": "All 11 AI/LLM tools \u2014 $509 bought separately, yours for $169. Save $340 (67%): RAG pipelines, agent frameworks, guardrails, and prompt tooling.", "detailed_description": "A modular retrieval-augmented generation framework with pluggable loaders, chunking strategies, embedding backends, vector stores, hybrid search, reranking, and a full evaluation suite. Build a RAG pipeline that actually works. Not a toy demo \u2014 a framework with four chunking strategies, BM25+dense hybrid search, reranking, and RAGAS-style evaluation metrics, all runnable locally with the included in-memory vector store. --- Includes: Document loaders for plain text, Markdown, HTML, and PDF with metadata extraction; Four chunking strategies \u2014 fixed-size, recursive text splitting, semantic (embedding-based topic detection), and sentence-window; Embedding abstraction with OpenAI adapter and a deterministic demo embedder for offline testing; Embedding cache that persists to disk so re-indexing unchanged documents costs nothing; In-memory vector store with full BM25 keyword search \u2014 runs the complete pipeline with zero external services; Hybrid search combining dense similarity and BM25 via Reciprocal Rank Fusion.", "price_usd": 169, "currency": "usd", "is_bundle": true, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "openai", "pypdf", "pytest" ], "supported_versions": [], "supported_technologies": [ "OpenAI", "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "pip install -r requirements.txt", "from src.loaders.text_loader import TextLoader, MarkdownLoader\nfrom src.chunking.strategies import RecursiveChunker\nfrom src.embeddings import DemoEmbedder\nfrom src.vector_store import InMemoryVectorStore\nfrom src.orchestrator import RAGOrchestrator\n\n# Load documents\nloader = MarkdownLoader()\ndocs = loader.load_directory(\"./my_docs/\", recursive=True)\n\n# Chunk with recursive strategy\nchunker = RecursiveChunker(chunk_size=1000, chunk_overlap=200)\nall_chunks = []\nfor doc in docs:\n\u2026" ], "included_files": [ "products/ai-agent-framework/README.md", "products/ai-agent-framework/source/README.md", "products/ai-agent-framework/source/configs/agent_config.yaml", "products/ai-agent-framework/source/configs/team_config.yaml", "products/ai-agent-framework/source/examples/basic_agent.py", "products/ai-agent-framework/source/examples/research_agent.py", "products/ai-agent-framework/source/examples/team_example.py", "products/ai-agent-framework/source/guide/01-building-an-agent.md", "products/ai-agent-framework/source/guide/02-tool-design.md", "products/ai-agent-framework/source/guides/building-an-agent.md", "products/ai-agent-framework/source/guides/tool-design.md", "products/ai-agent-framework/source/requirements.txt", "products/ai-agent-framework/source/src/__init__.py", "products/ai-agent-framework/source/src/agent.py", "products/ai-agent-framework/source/src/approval.py", "products/ai-agent-framework/source/src/coordination/__init__.py", "products/ai-agent-framework/source/src/coordination/message_bus.py", "products/ai-agent-framework/source/src/coordination/supervisor.py", "products/ai-agent-framework/source/src/guardrails.py", "products/ai-agent-framework/source/src/llm_client.py", "products/ai-agent-framework/source/src/memory/__init__.py", "products/ai-agent-framework/source/src/memory/buffer.py", "products/ai-agent-framework/source/src/memory/long_term.py", "products/ai-agent-framework/source/src/memory/summarizer.py", "products/ai-agent-framework/source/src/planning/__init__.py", "products/ai-agent-framework/source/src/planning/plan_execute.py", "products/ai-agent-framework/source/src/planning/react.py", "products/ai-agent-framework/source/src/tool_registry.py", "products/ai-agent-framework/source/src/tools/__init__.py", "products/ai-agent-framework/source/src/tools/calculator.py", "products/ai-agent-framework/source/src/tools/file_io.py", "products/ai-agent-framework/source/src/tools/web_search.py", "products/ai-agent-framework/source/tests/__init__.py", "products/ai-agent-framework/source/tests/test_memory.py", "products/ai-agent-framework/source/tests/test_tools.py", "products/ai-api-gateway/README.md", "products/ai-api-gateway/source/README.md", "products/ai-api-gateway/source/configs/gateway_config.yaml", "products/ai-api-gateway/source/guide/01-deployment-guide.md", "products/ai-api-gateway/source/guides/deployment-guide.md", "products/ai-api-gateway/source/requirements.txt", "products/ai-api-gateway/source/src/__init__.py", "products/ai-api-gateway/source/src/analytics.py", "products/ai-api-gateway/source/src/cache.py", "products/ai-api-gateway/source/src/client.py", "products/ai-api-gateway/source/src/providers/__init__.py", "products/ai-api-gateway/source/src/providers/anthropic_provider.py", "products/ai-api-gateway/source/src/providers/local_provider.py", "products/ai-api-gateway/source/src/providers/openai_provider.py", "products/ai-api-gateway/source/src/rate_limiter.py", "products/ai-api-gateway/source/src/request_logger.py", "products/ai-api-gateway/source/src/retry.py", 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"products/ai-safety-guardrails/source/src/toxicity_detector.py", "products/ai-safety-guardrails/source/tests/test_injection_detector.py", "products/ai-safety-guardrails/source/tests/test_pii_detector.py", "products/ai-safety-guardrails/source/tests/test_policy_engine.py", "products/conversational-ai-templates/README.md", "products/conversational-ai-templates/source/README.md", "products/conversational-ai-templates/source/guide/01-building-a-chatbot.md", "products/conversational-ai-templates/source/guide/02-channel-integration.md", "products/conversational-ai-templates/source/guides/building-a-chatbot.md", "products/conversational-ai-templates/source/guides/channel-integration.md", "products/conversational-ai-templates/source/requirements.txt", "products/conversational-ai-templates/source/src/__init__.py" ], "file_count": 80, "download_size_bytes": 1224252, "download_size_human": "1.2 MB", "product_url": "https://datanest-stores.com/ai-llm-toolkit/#product-ai-llm-complete-bundle", "preview_url": null, "demo_url": null }, { "product_id": "ai-ml-starter:mlflow-starter-kit", "slug": "mlflow-starter-kit", "store": "ai-ml-starter", "store_name": "ML Starter Kit", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "deployment", "experiment tracking", "experiment-tracking", "feature store", "machine learning starter", "ml beginner", "ml pipeline", "mlflow", "mlops", "model serving", "model-registry" ], "title": "MLflow Starter Kit", "short_description": "MLflow experiment tracking setup, model registry patterns, and deployment configs for going from notebooks to production.", "detailed_description": "A typed, production-minded layer over MLflow that takes you from ad-hoc notebook experiments to a structured, reproducible MLOps workflow: experiment tracking, a model registry promotion lifecycle, autologging for scikit-learn / XGBoost, deployment config generation for batch and real-time serving, and a one-command Docker stack (MLflow + Postgres + MinIO). The Python package (mlflowstarter) is a thin, well-documented wrapper -- you can always drop down to the raw mlflow. API. Everything ships with docstrings, type hints, runnable examples, and a real pytest suite. Includes: Typed configuration (MLflowConfig) -- load from env vars, a mapping, or a; Experiment tracking helpers -- one experimentrun context manager that; Model registry facade (ModelRegistry) -- registerfromrun, stage; Autologging wrappers -- project-sane AutologOptions defaults plus; Deployment generators -- build mlflow models serve / build-docker; Self-hosted stack -- docker/docker-compose.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": "2026-03-10", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.9+", "Docker", "Mlflow 2.x", "Docker + Docker Compose" ], "supported_versions": [ "Python 3.9+" ], "supported_technologies": [ "MLflow", "Docker", "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "docker compose -f docker/docker-compose.yml up -d\n# MLflow UI -> http://localhost:5000\n# MinIO console-> http://localhost:9001 (minioadmin / minioadmin)", "from mlflow_starter import MLflowConfig, configure, experiment_run, log_params, log_metrics\n\nconfigure(MLflowConfig.from_env()) # reads MLFLOW_TRACKING_URI, tags, ...\n\nwith experiment_run(\"baseline\", tags={\"owner\": \"ml-team\"}) as run:\n log_params({\"model\": {\"max_depth\": 6, \"n_estimators\": 300}}) # auto-flattened\n log_metrics({\"rmse\": 0.42, \"r2\": 0.91})\n print(\"run id:\", run.info.run_id)" ], "included_files": [ "README.md", "docker/docker-compose.yml", "examples/promote_model_example.py", "examples/train_sklearn_example.py", "guide/mlflow-setup-guide.md", "guide/model-registry-workflow.md", "guides/mlflow-setup-guide.md", "guides/model-registry-workflow.md", "src/mlflow_starter/__init__.py", "src/mlflow_starter/autolog.py", "src/mlflow_starter/config.py", "src/mlflow_starter/deployment.py", "src/mlflow_starter/registry.py", "src/mlflow_starter/tracking.py", "tests/conftest.py", "tests/test_registry.py", "tests/test_tracking.py" ], "file_count": 17, "download_size_bytes": 102466, "download_size_human": "100.1 KB", "product_url": "https://datanest-stores.com/ai-ml-starter/#product-mlflow-starter-kit", "preview_url": "https://datanest-stores.com/ai-ml-starter/preview-mlflow-starter-kit", "demo_url": "https://datanest-stores.com/ai-ml-starter/demo-mlflow-starter-kit/" }, { "product_id": "ai-ml-starter:model-serving-templates", "slug": "model-serving-templates", "store": "ai-ml-starter", "store_name": "ML Starter Kit", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ab-testing", "ai", "canary-deployment", "experiment tracking", "fastapi", "feature store", "flask", "inference", "machine learning starter", "ml beginner", "ml pipeline", "mlflow", "model serving", "model-serving" ], "title": "Model Serving Templates", "short_description": "FastAPI/Flask model serving endpoints, batched inference, A/B testing, and canary deployment configurations.", "detailed_description": "Production-ready templates for serving machine-learning models over HTTP with dynamic request micro-batching. Ships both a FastAPI (ASGI) and a Flask (WSGI) implementation behind one identical API, plus a pluggable model loader (pickle / joblib / ONNX), Prometheus metrics, runnable examples, tests, and a slim Docker setup with a stable + canary topology. The headline feature is the batcher: instances arriving in many concurrent requests are transparently merged into single predict() calls, then fanned back out to each caller \u2014 often a 10\u00d7+ throughput win with a bounded latency cost you control. Includes: FastAPI app (src/modelserving/app; Dynamic micro-batcher (batching; Pluggable loaders (loaders; Pydantic v2 schemas (schemas; Metrics + logging (middleware; Flask alternative (flaskapp.", "price_usd": 49, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": "2026-03-10", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.9+", "Fastapi 0.100+", "Docker" ], "supported_versions": [], "supported_technologies": [ "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "pip install \"fastapi[standard]\" uvicorn pydantic joblib scikit-learn httpx", "python examples/serve_sklearn.py\n# -> trains a LogisticRegression, saves it with joblib, serves on :8000" ], "included_files": [ "README.md", "docker/Dockerfile", "docker/docker-compose.yml", "examples/client_request.py", "examples/serve_sklearn.py", "guide/batching-and-scaling.md", "guide/serving-architecture.md", "guides/batching-and-scaling.md", "guides/serving-architecture.md", "src/model_serving/__init__.py", "src/model_serving/app.py", "src/model_serving/batching.py", "src/model_serving/flask_app.py", "src/model_serving/loaders.py", "src/model_serving/middleware.py", "src/model_serving/schemas.py", "tests/test_app.py", "tests/test_batching.py" ], "file_count": 18, "download_size_bytes": 98379, "download_size_human": "96.1 KB", "product_url": "https://datanest-stores.com/ai-ml-starter/#product-model-serving-templates", "preview_url": "https://datanest-stores.com/ai-ml-starter/preview-model-serving-templates", "demo_url": "https://datanest-stores.com/ai-ml-starter/demo-model-serving-templates/" }, { "product_id": "ai-ml-starter:feature-store-bootstrap", "slug": "feature-store-bootstrap", "store": "ai-ml-starter", "store_name": "ML Starter Kit", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "experiment tracking", "feast", "feature store", "feature-engineering", "feature-store", "machine learning starter", "ml beginner", "ml pipeline", "mlflow", "model serving", "offline-serving", "online-serving" ], "title": "Feature Store Bootstrap", "short_description": "Feature store patterns using Feast, feature engineering pipelines, and offline/online serving configurations.", "detailed_description": "A production-shaped Feast feature store you can read, run, and adapt in an afternoon. It ships a complete, tested feature repository for an anonymised retail-risk domain (\"AcmeCorp\") -- entities, batch and streaming sources, on-demand transformations, and two feature services (a real-time fraud model and a batch churn model) -- plus a small, unit-tested helper library that wraps the Feast read/write APIs the way a real team would. This is not a hello-world. The feature logic is separated from the Feast wrappers so it has real test coverage, the helpers are dependency-injected so they test without a running store, and the guides explain the why behind point-in-time correctness, train/serve skew, TTLs, and push-vs-batch ingestion. Includes: Complete Feast repository -- 2 entities, 3 batch feature views, a request; A reusable helper library (src/featurestore/) for ingestion, retrieval,; Point-in-time correct training retrieval driven by a FeatureService so; Streaming + batch ingestion via materialize and a PushSource; On-demand (request-time) features with the maths factored into pure,; Two runnable examples that generate their own data and run end to end.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": "2026-03-10", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.9+", "Feast 0.30+", "Redis", "pandas and pyarrow", "Redis for the online store" ], "supported_versions": [ "Python 3.9+" ], "supported_technologies": [ "Redis", "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "python -m venv .venv && source .venv/bin/activate\npip install \"feast[redis]\" pandas pyarrow pytest", "# 1. Apply the feature definitions to a fresh registry.\ncd feature_repo\nfeast apply\n\n# 2. Run the full write + serve example (generates sample data, materializes,\n# pushes a streaming update, and serves both feature services).\ncd ..\npython examples/materialize_and_serve.py\n\n# 3. Build a point-in-time correct training set for the churn model.\npython examples/training_dataset.py" ], "included_files": [ "README.md", "examples/materialize_and_serve.py", "examples/training_dataset.py", "feature_repo/example_repo.py", "feature_repo/feature_store.yaml", "guide/feast-setup.md", "guide/feature-engineering-patterns.md", "guides/feast-setup.md", "guides/feature-engineering-patterns.md", "src/feature_store/__init__.py", "src/feature_store/definitions.py", "src/feature_store/ingestion.py", "src/feature_store/registry.py", "src/feature_store/retrieval.py", "src/feature_store/transformations.py", "tests/test_definitions.py", "tests/test_retrieval.py" ], "file_count": 17, "download_size_bytes": 92797, "download_size_human": "90.6 KB", "product_url": "https://datanest-stores.com/ai-ml-starter/#product-feature-store-bootstrap", "preview_url": "https://datanest-stores.com/ai-ml-starter/preview-feature-store-bootstrap", "demo_url": "https://datanest-stores.com/ai-ml-starter/demo-feature-store-bootstrap/" }, { "product_id": "ai-ml-starter:experiment-tracking-pack", "slug": "experiment-tracking-pack", "store": "ai-ml-starter", "store_name": "ML Starter Kit", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "dashboards", "experiment tracking", "experiment-tracking", "feature store", "machine learning starter", "metrics", "ml beginner", "ml pipeline", "mlflow", "model serving", "wandb", "weights-and-biases" ], "title": "Experiment Tracking Pack", "short_description": "Experiment tracking with W&B and MLflow, custom dashboards, metrics comparison, and reproducibility tooling.", "detailed_description": "One experiment-tracking interface, three interchangeable backends. Instrument your ML training loop once and send the results to Weights & Biases, MLflow, or a zero-dependency local SQLite store \u2014 switching between them with a single line of configuration. The W&B and MLflow SDKs are imported lazily, so import exptracking (and the entire test suite) works with nothing but the Python standard library installed. You only need wandb or mlflow when you actually use that backend. Includes: Unified ExperimentTracker interface \u2014 startrun, logparams,; Three backends, identical API; CustomTracker \u2014 local, SQLite-backed, pure stdlib (great for tests/CI/offline); WandbTracker \u2014 Weights & Biases, with artifact and sweep support; MlflowTracker \u2014 MLflow Tracking, local mlruns/ or a self-hosted server; run() context manager that always finalizes a run \u2014 FINISHED on success,.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": "2026-03-10", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.9+", "Wandb account", "Mlflow 2.x" ], "supported_versions": [], "supported_technologies": [ "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "pip install wandb # for the W&B backend (then: wandb login)\npip install mlflow # for the MLflow backend\npip install pyyaml # only if you load a .yaml config (JSON needs nothing)\n# the local SQLite backend, sweeps, and comparison need no third-party packages", "tracker = get_tracker(\"wandb\", project=\"image-classification\", entity=\"your-org\")\ntracker = get_tracker(\"mlflow\", experiment_name=\"image-classification\")" ], "included_files": [ "README.md", "config.example.yaml", "examples/compare_experiments.py", "examples/track_training_run.py", "guide/choosing-a-tracker.md", "guide/experiment-tracking-guide.md", "guides/choosing-a-tracker.md", "guides/experiment-tracking-guide.md", "src/exp_tracking/__init__.py", "src/exp_tracking/base.py", "src/exp_tracking/compare.py", "src/exp_tracking/custom_tracker.py", "src/exp_tracking/factory.py", "src/exp_tracking/mlflow_tracker.py", "src/exp_tracking/sweep.py", "src/exp_tracking/wandb_tracker.py", "tests/test_base.py", "tests/test_custom_tracker.py" ], "file_count": 18, "download_size_bytes": 104580, "download_size_human": "102.1 KB", "product_url": "https://datanest-stores.com/ai-ml-starter/#product-experiment-tracking-pack", "preview_url": "https://datanest-stores.com/ai-ml-starter/preview-experiment-tracking-pack", "demo_url": "https://datanest-stores.com/ai-ml-starter/demo-experiment-tracking-pack/" }, { "product_id": "ai-ml-starter:ml-pipeline-templates", "slug": "ml-pipeline-templates", "store": "ai-ml-starter", "store_name": "ML Starter Kit", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "airflow", "deployment", "experiment tracking", "feature store", "kubeflow", "machine learning starter", "ml beginner", "ml pipeline", "ml-pipelines", "mlflow", "mlops", "model serving", "prefect", "training" ], "title": "ML Pipeline Templates", "short_description": "End-to-end ML pipelines: data ingestion, preprocessing, training, evaluation, and deployment orchestration.", "detailed_description": "End-to-end, dependency-light templates for building tabular machine-learning pipelines: data ingestion - preprocessing - training - evaluation, chained together by a small DAG orchestrator you can actually read. The orchestration core is pure standard library, so it is fast to import and trivial to test. The ML stages use pandas/numpy and prefer scikit-learn when it is installed -- but every stage has a numpy fallback, so the whole pipeline (and the test suite) runs end-to-end even with nothing but pandas + numpy. Includes: DAG orchestrator (Pipeline / Step) -- declare dependencies, get; Ingestion into one Dataset interface from CSV, Parquet, or SQL (or an; Preprocessing built on a scikit-learn ColumnTransformer (impute + scale +; Training with backend-agnostic K-fold cross-validation that works with any; Evaluation with pure-numpy regression and classification metrics plus a; Config-driven -- build the standard four-stage pipeline from.", "price_usd": 49, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": "2026-03-10", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.9+", "Orchestrator", "Docker", "Cloud storage", "pandas, numpy, PyYAML", "scikit-learn" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "pip install -r requirements.txt", "# 1) Run the full pipeline on a generated toy dataset\npython examples/run_pipeline.py\n\n# 2) Run it from the YAML config instead of an inline config\npython examples/run_pipeline.py --config configs/pipeline.yaml\n\n# 3) See the scikit-learn ColumnTransformer + RandomForest path\npython examples/sklearn_pipeline.py # prints an install hint if sklearn is absent\n\n# 4) Run the test suite\npip install pytest\npython -m pytest tests/ -v" ], "included_files": [ "README.md", "configs/pipeline.yaml", "examples/run_pipeline.py", "examples/sklearn_pipeline.py", "guide/orchestration-options.md", "guide/pipeline-architecture.md", "guides/orchestration-options.md", "guides/pipeline-architecture.md", "requirements.txt", "src/ml_pipeline/__init__.py", "src/ml_pipeline/evaluation.py", "src/ml_pipeline/ingestion.py", "src/ml_pipeline/pipeline.py", "src/ml_pipeline/preprocessing.py", "src/ml_pipeline/steps.py", "src/ml_pipeline/training.py", "tests/test_pipeline.py", "tests/test_preprocessing.py" ], "file_count": 18, "download_size_bytes": 116657, "download_size_human": "113.9 KB", "product_url": "https://datanest-stores.com/ai-ml-starter/#product-ml-pipeline-templates", "preview_url": "https://datanest-stores.com/ai-ml-starter/preview-ml-pipeline-templates", "demo_url": "https://datanest-stores.com/ai-ml-starter/demo-ml-pipeline-templates/" }, { "product_id": "ai-ml-starter:model-validation-framework", "slug": "model-validation-framework", "store": "ai-ml-starter", "store_name": "ML Starter Kit", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "drift-detection", "experiment tracking", "feature store", "machine learning starter", "ml beginner", "ml pipeline", "mlflow", "model serving", "model-validation", "monitoring", "quality-gates", "testing" ], "title": "Model Validation Framework", "short_description": "Model testing, data drift detection, performance monitoring, and validation gates for CI/CD.", "detailed_description": "A dependency-light framework for validating machine-learning models before you promote them to production and while they run there. It bundles data & concept drift detection, baseline performance monitoring, behavioral testing, and a configurable pass/fail validation gate you can wire straight into CI/CD. Everything is implemented in pure NumPy \u2014 the SciPy-grade statistics (Kolmogorov-Smirnov p-values, chi-square survival function, Population Stability Index, rank-based ROC AUC) are written from scratch, so the framework runs anywhere NumPy is installed and has zero heavy dependencies. If you already use scikit-learn or SciPy, the framework plugs into them cleanly (feed your own metric dicts or model callables into the same checks). Includes: Data drift detection \u2014 per-feature PSI + Kolmogorov-Smirnov (numeric) and; Performance monitoring \u2014 compare a current metric snapshot to a trusted; Behavioral tests \u2014 model-agnostic invariance, directional-expectation, and; Validation gate \u2014 a ValidationSuite aggregates any mix of checks into a; Markdown & JSON reports \u2014 drop a readable report into a PR comment or a CI; Typed, versionable thresholds \u2014 all policy lives in dataclasses.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": "2026-03-10", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.9+", "Scikit learn", "Scipy", "NumPy" ], "supported_versions": [ "Python 3.9+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "pip install numpy # required\npip install pandas # optional, for DataFrame inputs", "# 1. Standalone drift report on synthetic reference vs production data\npython examples/drift_check.py\n\n# 2. Full validation suite on a trained (NumPy) classifier\npython examples/validate_model.py" ], "included_files": [ "README.md", "examples/drift_check.py", "examples/validate_model.py", "guide/drift-detection.md", "guide/model-validation-guide.md", "guides/drift-detection.md", "guides/model-validation-guide.md", "src/model_validation/__init__.py", "src/model_validation/behavioral.py", "src/model_validation/drift.py", "src/model_validation/performance.py", "src/model_validation/report.py", "src/model_validation/statistical_tests.py", "src/model_validation/thresholds.py", "src/model_validation/validation.py", "tests/test_drift.py", "tests/test_performance.py", "tests/test_validation.py" ], "file_count": 18, "download_size_bytes": 109897, "download_size_human": "107.3 KB", "product_url": "https://datanest-stores.com/ai-ml-starter/#product-model-validation-framework", "preview_url": "https://datanest-stores.com/ai-ml-starter/preview-model-validation-framework", "demo_url": "https://datanest-stores.com/ai-ml-starter/demo-model-validation-framework/" }, { "product_id": "ai-ml-starter:hyperparameter-tuning-kit", "slug": "hyperparameter-tuning-kit", "store": "ai-ml-starter", "store_name": "ML Starter Kit", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "distributed", "experiment tracking", "feature store", "hyperparameter-tuning", "machine learning starter", "ml beginner", "ml pipeline", "mlflow", "model serving", "optimization", "optuna", "ray-tune" ], "title": "Hyperparameter Tuning Kit", "short_description": "Optuna/Ray Tune configs, search space definitions, pruning strategies, and distributed tuning setups.", "detailed_description": "A practical hyperparameter optimization toolkit built around a single idea: define your search space once and run it on both Optuna and Ray Tune without rewriting anything. Ships with cross-validated objectives, median/percentile pruning, ASHA/PBT scheduling, multi-objective support, and backend-agnostic result analysis. The core (search spaces, pruning, objectives, analysis) depends only on the Python standard library \u2014 it imports and its full test suite runs with no third-party packages at all. Optuna and Ray Tune are imported lazily, so you install only the backend you actually use. Includes: One declarative search space, two backends; Typed parameters \u2014 FloatParameter, IntParameter, CategoricalParameter; Cross-validated objectives \u2014 Objective wraps stdlib K-fold CV with a; Pruning in plain Python \u2014 MedianPruner, PercentilePruner,; Optuna runner \u2014 samplers (TPE, CMA-ES, QMC, random, grid), pruning, SQLite; Ray Tune runner \u2014 ASHA, Population Based Training, and median stopping via a.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": "2026-03-10", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.9+", "Optuna 3.x", "Ray tune 2.x" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "python examples/random_search_baseline.py", "from hpo import SearchSpace, FloatParameter, IntParameter, CategoricalParameter\n\nspace = SearchSpace([\n FloatParameter(\"learning_rate\", 1e-4, 1e-1, log=True), # log scale for rates\n IntParameter(\"n_estimators\", 50, 500, log=True),\n IntParameter(\"max_depth\", 2, 12),\n CategoricalParameter(\"max_features\", [\"sqrt\", \"log2\", None]),\n])" ], "included_files": [ "README.md", "examples/optuna_sklearn.py", "examples/random_search_baseline.py", "examples/ray_tune_torch.py", "guide/hpo-guide.md", "guide/search-space-design.md", "guides/hpo-guide.md", "guides/search-space-design.md", "src/hpo/__init__.py", "src/hpo/analysis.py", "src/hpo/objective.py", "src/hpo/optuna_search.py", "src/hpo/pruning.py", "src/hpo/ray_tune_search.py", "src/hpo/search_spaces.py", "tests/test_objective.py", "tests/test_pruning.py", "tests/test_search_spaces.py" ], "file_count": 18, "download_size_bytes": 118464, "download_size_human": "115.7 KB", "product_url": "https://datanest-stores.com/ai-ml-starter/#product-hyperparameter-tuning-kit", "preview_url": "https://datanest-stores.com/ai-ml-starter/preview-hyperparameter-tuning-kit", "demo_url": "https://datanest-stores.com/ai-ml-starter/demo-hyperparameter-tuning-kit/" }, { "product_id": "ai-ml-starter:ml-data-versioning", "slug": "ml-data-versioning", "store": "ai-ml-starter", "store_name": "ML Starter Kit", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "artifact-management", "data-versioning", "dvc", "experiment tracking", "feature store", "machine learning starter", "ml beginner", "ml pipeline", "mlflow", "mlops", "model serving", "reproducibility" ], "title": "ML Data Versioning", "short_description": "DVC setup, data pipeline versioning, experiment reproducibility, and artifact management workflows.", "detailed_description": "A standard-library Python toolkit for reproducible dataset versioning, lineage, and DVC pipelines. Hash datasets deterministically, snapshot and diff them without any infrastructure, generate a valid dvc.yaml, build a provenance graph, and capture the run environment that makes an experiment reproducible. Every module is pure Python (no third-party runtime dependencies), so it runs anywhere Python 3.9+ does and is fully unit-tested. Includes: Deterministic content hashing (hashing; Dataset snapshots + diffs (snapshots; Lineage graph (lineage; DVC pipeline builder (pipeline; Reproducibility capture (reproducibility.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": "2026-03-10", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.9+", "Git", "Dvc 3.x", "Remote storage" ], "supported_versions": [ "Python 3.9+" ], "supported_technologies": [ "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "# Run the test suite (standard library only; no pip installs required)\npython -m unittest discover -s tests -v\n\n# See dataset hashing, snapshots, and diffs in action\npython examples/version_dataset.py\n\n# See a generated dvc.yaml, a lineage graph, and an environment-drift report\npython examples/reproduce_experiment.py", "python -m unittest discover -s tests -v # standard library only\n# or, if you prefer pytest:\npytest tests/ -q" ], "included_files": [ "README.md", "dvc/dvc.yaml", "dvc/params.yaml", "examples/reproduce_experiment.py", "examples/version_dataset.py", "guide/dvc-setup.md", "guide/reproducibility-guide.md", "guides/dvc-setup.md", "guides/reproducibility-guide.md", "src/data_versioning/__init__.py", "src/data_versioning/hashing.py", "src/data_versioning/lineage.py", "src/data_versioning/pipeline.py", "src/data_versioning/reproducibility.py", "src/data_versioning/snapshots.py", "tests/test_hashing.py", "tests/test_lineage.py" ], "file_count": 17, "download_size_bytes": 113388, "download_size_human": "110.7 KB", "product_url": "https://datanest-stores.com/ai-ml-starter/#product-ml-data-versioning", "preview_url": "https://datanest-stores.com/ai-ml-starter/preview-ml-data-versioning", "demo_url": "https://datanest-stores.com/ai-ml-starter/demo-ml-data-versioning/" }, { "product_id": "ai-ml-starter:gpu-training-toolkit", "slug": "gpu-training-toolkit", "store": "ai-ml-starter", "store_name": "ML Starter Kit", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "cuda", "distributed-training", "experiment tracking", "feature store", "gpu-training", "machine learning starter", "mixed-precision", "ml beginner", "ml pipeline", "mlflow", "model serving", "multi-gpu", "pytorch" ], "title": "GPU Training Toolkit", "short_description": "Multi-GPU training configs, mixed precision training, distributed training, and cloud GPU setup guides.", "detailed_description": "Battle-tested PyTorch patterns for multi-GPU, mixed-precision, and distributed training \u2014 packaged as a small, importable library you drop into your project. It encodes the parts people get subtly wrong (effective batch math, fp16 loss scaling, rank-0 checkpointing, OOM-safe batch sizing) so your scale-out is correct the first time. Every PyTorch import is lazy, so the configuration/profiling math and the full test suite run on the Python standard library alone \u2014 no GPU and no torch required to explore, plan, or test. Includes: Distributed Data Parallel (DDP) \u2014 process-group init/teardown, per-rank; Mixed precision done right \u2014 autocast + GradScaler with the correct; torchrun launch generation \u2014 turn a typed config into the exact; Profiling & OOM-safe batch finder \u2014 GPU memory snapshots, a throughput; Distributed-safe checkpoints \u2014 rank-0-only, atomic writes with automatic; Typed TrainingConfig \u2014 one object that computes effective batch size,.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": "2026-03-10", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.9+", "Pytorch 2.x", "Cuda 11.8+", "Nvidia gpu", "PyTorch 2.x and CUDA 11.8+ to run training" ], "supported_versions": [ "Python 3.10+", "Node 4" ], "supported_technologies": [ "PyTorch", "Python" ], "compatibility_notes": null, "preview_snippets": [ "python3 -m gpu_training.config # effective batch + step-count demo\npython3 -m gpu_training.profiling # OOM-safe batch finder demo\npython3 -m gpu_training.launcher # generate torchrun commands", "import sys; sys.path.insert(0, \"src\")\nfrom gpu_training.config import TrainingConfig, gradient_accumulation_for_target\n\n# Hit a target effective batch of 512 with 8/GPU across 4 GPUs:\naccum = gradient_accumulation_for_target(512, per_device_batch_size=8, world_size=4)\ncfg = TrainingConfig(per_device_batch_size=8, gradient_accumulation_steps=accum, world_size=4)\nprint(cfg.effective_batch_size) # 512\nprint(cfg.describe(num_samples=1_000_000))" ], "included_files": [ "README.md", "examples/train_amp.py", "examples/train_ddp.py", "guide/distributed-training-guide.md", "guide/mixed-precision.md", "guides/distributed-training-guide.md", "guides/mixed-precision.md", "src/gpu_training/__init__.py", "src/gpu_training/amp.py", "src/gpu_training/checkpoint.py", "src/gpu_training/config.py", "src/gpu_training/ddp.py", "src/gpu_training/launcher.py", "src/gpu_training/profiling.py", "tests/test_config.py", "tests/test_profiling.py" ], "file_count": 16, "download_size_bytes": 113598, "download_size_human": "110.9 KB", "product_url": "https://datanest-stores.com/ai-ml-starter/#product-gpu-training-toolkit", "preview_url": "https://datanest-stores.com/ai-ml-starter/preview-gpu-training-toolkit", "demo_url": "https://datanest-stores.com/ai-ml-starter/demo-gpu-training-toolkit/" }, { "product_id": "ai-ml-starter:ml-monitoring-suite", "slug": "ml-monitoring-suite", "store": "ai-ml-starter", "store_name": "ML Starter Kit", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "alerting", "data-quality", "experiment tracking", "feature store", "grafana", "machine learning starter", "ml beginner", "ml pipeline", "mlflow", "model serving", "model-performance", "monitoring", "prometheus" ], "title": "ML Monitoring Suite", "short_description": "Model monitoring dashboards, alert configurations, data quality checks, and performance tracking.", "detailed_description": "Production model monitoring with zero third-party dependencies. A small, batteries-included Python package that keeps a served model healthy: prediction / latency / throughput metrics, data-quality expectations, online drift detection (PSI / KS / Jensen-Shannon), a stateful alert engine, Prometheus + JSON exporters, and a ready-to-import Grafana dashboard. Everything is pure Python standard library (3.9+), so it imports and runs in any environment your model already runs in \u2014 no pip install required to use it. Includes: Prediction metrics (metrics; Rolling storage (store; Data-quality expectations (dataquality; Drift detection (driftmonitor; Alerting (alerting; Exporters (exporters.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": "2026-03-10", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Docker", "Prometheus 2.x", "Grafana 9.x", "Python 3.9+" ], "supported_versions": [ "Python 3.9+" ], "supported_technologies": [ "Prometheus", "Grafana", "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Option A \u2014 vendor the package straight into your service\ncp -r src/ml_monitoring /path/to/your/service/\n\n# Option B \u2014 put src/ on PYTHONPATH\nexport PYTHONPATH=\"$PWD/src:$PYTHONPATH\"\npython3 -c \"import ml_monitoring; print(ml_monitoring.__version__)\"", "from ml_monitoring import JSONExporter, PrometheusExporter\n\nprint(PrometheusExporter(registry).render()) # Prometheus text exposition\nprint(JSONExporter(registry).render()) # structured JSON\nprint(monitor.snapshot()) # a dict of every current signal" ], "included_files": [ "README.md", "dashboards/grafana-ml-monitoring.json", "examples/data_quality_check.py", "examples/monitor_predictions.py", "guide/alerting-setup.md", "guide/monitoring-guide.md", "guides/alerting-setup.md", "guides/monitoring-guide.md", "src/ml_monitoring/__init__.py", "src/ml_monitoring/alerting.py", "src/ml_monitoring/data_quality.py", "src/ml_monitoring/drift_monitor.py", "src/ml_monitoring/exporters.py", "src/ml_monitoring/metrics.py", "src/ml_monitoring/store.py", "tests/test_data_quality.py", "tests/test_metrics.py" ], "file_count": 17, "download_size_bytes": 127905, "download_size_human": "124.9 KB", "product_url": "https://datanest-stores.com/ai-ml-starter/#product-ml-monitoring-suite", "preview_url": "https://datanest-stores.com/ai-ml-starter/preview-ml-monitoring-suite", "demo_url": "https://datanest-stores.com/ai-ml-starter/demo-ml-monitoring-suite/" }, { "product_id": "ai-ml-starter:ml-starter-complete-bundle", "slug": "ml-starter-complete-bundle", "store": "ai-ml-starter", "store_name": "ML Starter Kit", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "bundle", "complete-kit", "experiment tracking", "feature store", "machine learning starter", "machine-learning", "ml beginner", "ml pipeline", "mlflow", "mlops", "model serving", "production-ml" ], "title": "ML Starter Bundle", "short_description": "All 10 ML Starter tools \u2014 $380 bought separately, yours for $149. Save $231 (61%): notebook-to-production training, serving, and monitoring.", "detailed_description": "The complete ML Starter Kit \u2014 all 10 products in one package at a significant discount. Everything an ML engineer needs to go from notebook prototypes to production-grade machine learning systems.", "price_usd": 149, "currency": "usd", "is_bundle": true, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": "2026-03-10", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [], "supported_versions": [], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md" ], "file_count": 1, "download_size_bytes": 1117504, "download_size_human": "1.1 MB", "product_url": "https://datanest-stores.com/ai-ml-starter/#product-ml-starter-complete-bundle", "preview_url": "https://datanest-stores.com/ai-ml-starter/preview-ml-starter-complete-bundle", "demo_url": null }, { "product_id": "ai-toolkit:ai-agent-framework", "slug": "ai-agent-framework", "store": "ai-toolkit", "store_name": "AI Toolkit", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "ai agent", "ai prompts", "chatbot", "embeddings", "llm", "rag", "vector search" ], "title": "AI Agent Framework", "short_description": "Python framework for building AI agents with tool use, planning loops, memory, and multi-step reasoning.", "detailed_description": "Python framework for building AI agents with tool use, planning loops, memory management, multi-step reasoning, and structured output parsing. Zero dependencies. Part of the AI Toolkit collection by CodeVault. Includes: ReAct loop \u2014 Thought \u2192 Action \u2192 Observation reasoning loop (same pattern as LangChain/AutoGPT); Tool system \u2014 Register custom tools with parameter schemas and callable functions; Memory manager \u2014 Sliding-window context management with intelligent trimming; Output parser \u2014 Parses structured Thought/Action/Final Answer format from LLM responses; Built-in tools \u2014 Search, calculator, and JSON formatter included as examples; LLM-agnostic \u2014 Abstract LLMInterface \u2014 wire to OpenAI, Anthropic, or any API.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Run the demo with a simulated agent\npython src/ai_agent_framework.py --demo\n\n# List all registered tools\npython src/ai_agent_framework.py --list-tools\n\n# Export agent config\npython src/ai_agent_framework.py --export-config my_agent.json\n\n# Run agent on a task (uses simulated LLM)\npython src/ai_agent_framework.py --task \"What is the population of Helsinki?\"", "ai-agent-framework/\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 src/\n\u2502 \u2514\u2500\u2500 ai_agent_framework.py # Core engine (~420 lines)\n\u2514\u2500\u2500 examples/\n \u251c\u2500\u2500 basic_usage.py # Build a custom agent\n \u2514\u2500\u2500 custom_tools.json # Tool definition examples" ], "included_files": [ "README.md", "examples/basic_usage.py", "examples/custom_tools.json", "guide/01_features.md", "guide/02_project-structure.md", "guide/03_usage-examples.md", "guide/04_license.md", "src/ai_agent_framework.py" ], "file_count": 8, "download_size_bytes": 50211, "download_size_human": "49.0 KB", "product_url": "https://datanest-stores.com/ai-toolkit/#product-ai-agent-framework", "preview_url": "https://datanest-stores.com/ai-toolkit/preview-ai-agent-framework", "demo_url": "https://datanest-stores.com/ai-toolkit/demo-ai-agent-framework/" }, { "product_id": "ai-toolkit:ai-content-detector", "slug": "ai-content-detector", "store": "ai-toolkit", "store_name": "AI Toolkit", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "ai agent", "ai prompts", "chatbot", "embeddings", "llm", "rag", "vector search" ], "title": "AI Content Detector", "short_description": "Python AI content detector using perplexity analysis, burstiness scoring, and statistical text analysis.", "detailed_description": "Python AI content detector: perplexity analysis, burstiness scoring, vocabulary richness, statistical text analysis, and confidence-scored reports. All math from scratch. Zero dependencies. Part of the AI Toolkit collection by CodeVault. Includes: Perplexity estimation \u2014 Bigram language model measures text predictability; Burstiness scoring \u2014 Sentence length variation analysis (AI text is suspiciously uniform); Vocabulary richness \u2014 Type-token ratio detects AI's characteristic word diversity; Transition word density \u2014 AI overuses words like \"Furthermore\", \"Additionally\", \"Moreover\"; Repetition detection \u2014 N-gram repetition patterns common in AI output; Readability scoring \u2014 Flesch reading ease and syllable analysis.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Run demo with AI and human text samples\npython src/ai_content_detector.py --demo\n\n# Analyze inline text\npython src/ai_content_detector.py --text \"Your text to analyze goes here...\"\n\n# Analyze a file\npython src/ai_content_detector.py --file document.txt\n\n# Analyze with JSON export\npython src/ai_content_detector.py --file document.txt --export report.json\n\n# Batch analyze a folder\npython src/ai_content_detector.py --batch essays/ --export results.json\n\u2026", "ai-content-detector/\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 src/\n\u2502 \u2514\u2500\u2500 ai_content_detector.py # Core engine (~470 lines)\n\u2514\u2500\u2500 examples/\n \u251c\u2500\u2500 basic_usage.py # Programmatic usage example\n \u2514\u2500\u2500 sample_texts/ # AI and human text samples\n \u251c\u2500\u2500 ai_generated.txt\n \u2514\u2500\u2500 human_written.txt" ], "included_files": [ "README.md", "examples/basic_usage.py", "examples/sample_texts/ai_generated.txt", "examples/sample_texts/human_written.txt", "guide/01_features.md", "guide/02_cli-reference.md", "guide/03_important-disclaimer.md", "src/ai_content_detector.py" ], "file_count": 8, "download_size_bytes": 52950, "download_size_human": "51.7 KB", "product_url": "https://datanest-stores.com/ai-toolkit/#product-ai-content-detector", "preview_url": "https://datanest-stores.com/ai-toolkit/preview-ai-content-detector", "demo_url": "https://datanest-stores.com/ai-toolkit/demo-ai-content-detector/" }, { "product_id": "ai-toolkit:ai-toolkit-pro-bundle", "slug": "ai-toolkit-pro-bundle", "store": "ai-toolkit", "store_name": "AI Toolkit", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "ai agent", "ai prompts", "bundle", "chatbot", "embeddings", "llm", "rag", "vector search" ], "title": "AI Toolkit Pro Bundle", "short_description": "All 10 AI tools \u2014 $250 bought separately, yours for $59. Save $191 (76%): chatbots, RAG, agents, and embeddings.", "detailed_description": "All 10 AI tools \u2014 $250 bought separately, yours for $59. Save $191 (76%): chatbots, RAG, agents, and embeddings.", "price_usd": 59, "currency": "usd", "is_bundle": true, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [ "Python 3.10+", "Works on macOS, Linux, and Windows" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [ "# 1. Unzip the bundle\nunzip ai-toolkit-pro-bundle.zip\n\n# 2. Explore the tools\nls -la\n\n# 3. Try the RAG pipeline\ncd rag-pipeline-starter/\npython src/rag_pipeline_starter.py --demo\n\n# 4. Build a vector search index\ncd ../vector-search-setup/\npython src/vector_search_setup.py --build-index examples/sample_documents.jsonl --output my_index.json\npython src/vector_search_setup.py --query \"database indexing\" --index my_index.json --top-k 3\n\u2026", "ai-toolkit-pro-bundle/\n\u251c\u2500\u2500 chatbot-builder/\n\u2502 \u251c\u2500\u2500 README.md\n\u2502 \u251c\u2500\u2500 LICENSE\n\u2502 \u251c\u2500\u2500 src/chatbot_builder.py\n\u2502 \u2514\u2500\u2500 examples/\n\u251c\u2500\u2500 embedding-generator/\n\u2502 \u251c\u2500\u2500 README.md\n\u2502 \u251c\u2500\u2500 LICENSE\n\u2502 \u251c\u2500\u2500 src/embedding_generator.py\n\u2502 \u2514\u2500\u2500 examples/\n\u251c\u2500\u2500 rag-pipeline-starter/\n\u2502 \u251c\u2500\u2500 README.md\n\u2502 \u251c\u2500\u2500 LICENSE\n\u2026" ], "included_files": [ "README.md" ], "file_count": 1, "download_size_bytes": 306273, "download_size_human": "299.1 KB", "product_url": "https://datanest-stores.com/ai-toolkit/#product-ai-toolkit-pro-bundle", "preview_url": "https://datanest-stores.com/ai-toolkit/preview-ai-toolkit-pro-bundle", "demo_url": null }, { "product_id": "ai-toolkit:chatbot-builder", "slug": "chatbot-builder", "store": "ai-toolkit", "store_name": "AI Toolkit", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "ai agent", "ai prompts", "chatbot", "embeddings", "llm", "rag", "vector search" ], "title": "Chatbot Builder", "short_description": "Python chatbot framework with intent matching, conversation state, response templates, and context memory.", "detailed_description": "Python chatbot framework with intent matching, conversation state, response templates, and context memory. Zero dependencies. Part of the AI Toolkit collection by CodeVault. Includes: Intent matching \u2014 Pattern-based and keyword intent detection with confidence scoring; Conversation state machine \u2014 Finite-state conversation flows with transitions and guards; Response templates \u2014 Variable-injected response templates with random variation; Context memory \u2014 Sliding-window context that persists across conversation turns; Fallback handling \u2014 Configurable fallback responses when no intent matches; Conversation history \u2014 Full turn-by-turn log with timestamps.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Run the interactive chatbot demo\npython src/chatbot_builder.py\n\n# Run with a custom config\npython src/chatbot_builder.py --config examples/support_bot.json\n\n# Run the example script\npython examples/basic_usage.py", "chatbot-builder/\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 src/\n\u2502 \u2514\u2500\u2500 chatbot_builder.py # Core chatbot engine (single file, ~300 lines)\n\u2514\u2500\u2500 examples/\n \u251c\u2500\u2500 basic_usage.py # Programmatic usage example\n \u2514\u2500\u2500 support_bot.json # Sample chatbot configuration" ], "included_files": [ "README.md", "examples/basic_usage.py", "examples/support_bot.json", "guide/01_features.md", "guide/02_project-structure.md", "guide/03_usage-examples.md", "guide/04_license.md", "src/chatbot_builder.py" ], "file_count": 8, "download_size_bytes": 40096, "download_size_human": "39.2 KB", "product_url": "https://datanest-stores.com/ai-toolkit/#product-chatbot-builder", "preview_url": "https://datanest-stores.com/ai-toolkit/preview-chatbot-builder", "demo_url": "https://datanest-stores.com/ai-toolkit/demo-chatbot-builder/" }, { "product_id": "ai-toolkit:embedding-generator", "slug": "embedding-generator", "store": "ai-toolkit", "store_name": "AI Toolkit", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "ai agent", "ai prompts", "chatbot", "embeddings", "llm", "rag", "vector search" ], "title": "Embedding Generator", "short_description": "Python text embedding pipeline with tokenization, vector generation, similarity search, and caching.", "detailed_description": "Python text embedding pipeline with tokenization, vector generation, similarity search, and caching. Zero dependencies. Part of the AI Toolkit collection by CodeVault. Includes: Multiple embedding methods \u2014 Hash-trick, Bag-of-Words, and TF-IDF; Tokenizer \u2014 Configurable tokenization with stop-word removal; Similarity search \u2014 Cosine similarity ranking over a corpus; LRU caching \u2014 Automatic embedding cache with configurable size; Corpus builder \u2014 Build vocabulary and IDF from your documents; CLI interface \u2014 Embed text, load corpora, and search from terminal.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Embed a single text\npython src/embedding_generator.py --text \"machine learning is great\"\n\n# Load a corpus and search\npython src/embedding_generator.py --file examples/sample_corpus.txt --query \"neural networks\"\n\n# Interactive mode\npython src/embedding_generator.py\n\n# Use TF-IDF method\npython src/embedding_generator.py --method tfidf --file examples/sample_corpus.txt --query \"data science\"" ], "included_files": [ "README.md", "examples/basic_usage.py", "examples/sample_corpus.txt", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_configuration.md", "guide/04_license.md", "src/embedding_generator.py" ], "file_count": 8, "download_size_bytes": 34293, "download_size_human": "33.5 KB", "product_url": "https://datanest-stores.com/ai-toolkit/#product-embedding-generator", "preview_url": "https://datanest-stores.com/ai-toolkit/preview-embedding-generator", "demo_url": "https://datanest-stores.com/ai-toolkit/demo-embedding-generator/" }, { "product_id": "ai-toolkit:fine-tuning-pipeline", "slug": "fine-tuning-pipeline", "store": "ai-toolkit", "store_name": "AI Toolkit", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "ai agent", "ai prompts", "chatbot", "embeddings", "llm", "rag", "vector search" ], "title": "Fine-Tuning Pipeline", "short_description": "Python data pipeline for LLM fine-tuning with data cleaning, formatting, and quality checks.", "detailed_description": "Python data pipeline for LLM fine-tuning: data cleaning, JSONL formatting, validation, train/test splitting, quality checks, and dataset statistics. Zero dependencies. Part of the AI Toolkit collection by CodeVault. Includes: Format detection \u2014 Auto-detects chat, completion, and instruction (Alpaca) formats; Format conversion \u2014 Convert between OpenAI chat, legacy completion, and Alpaca formats; Text cleaning \u2014 Normalize whitespace, smart quotes, control characters, and unicode; Validation \u2014 Check for missing fields, empty content, token limits, and format errors; Token counting \u2014 Approximate token counts for budget estimation; Train/test split \u2014 Reproducible random splitting with configurable ratio.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Run demo with sample data\npython src/fine_tuning_pipeline.py --demo\n\n# Run the full pipeline: clean \u2192 convert \u2192 validate \u2192 split\npython src/fine_tuning_pipeline.py --input raw_data.jsonl --output prepared/\n\n# Validate a dataset\npython src/fine_tuning_pipeline.py --validate dataset.jsonl\n\n# Show dataset statistics\npython src/fine_tuning_pipeline.py --stats dataset.jsonl\n\n# Split with custom ratio\npython src/fine_tuning_pipeline.py --split dataset.jsonl --ratio 0.9 --output prepared/", "fine-tuning-pipeline/\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 src/\n\u2502 \u2514\u2500\u2500 fine_tuning_pipeline.py # Core engine (~430 lines)\n\u2514\u2500\u2500 examples/\n \u251c\u2500\u2500 basic_usage.py # Programmatic usage example\n \u2514\u2500\u2500 sample_training_data.jsonl # Sample data in mixed formats" ], "included_files": [ "README.md", "examples/basic_usage.py", "examples/sample_training_data.jsonl", "guide/01_features.md", "guide/02_project-structure.md", "guide/03_supported-formats.md", "guide/04_faq.md", "src/fine_tuning_pipeline.py" ], "file_count": 8, "download_size_bytes": 49494, "download_size_human": "48.3 KB", "product_url": "https://datanest-stores.com/ai-toolkit/#product-fine-tuning-pipeline", "preview_url": "https://datanest-stores.com/ai-toolkit/preview-fine-tuning-pipeline", "demo_url": "https://datanest-stores.com/ai-toolkit/demo-fine-tuning-pipeline/" }, { "product_id": "ai-toolkit:llm-cost-tracker", "slug": "llm-cost-tracker", "store": "ai-toolkit", "store_name": "AI Toolkit", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "ai agent", "ai prompts", "chatbot", "embeddings", "llm", "rag", "vector search" ], "title": "LLM Cost Tracker", "short_description": "Python LLM cost tracker with token counting, per-request cost calculation, and budget alerts.", "detailed_description": "Python LLM cost tracker: token counting, per-request cost calculation, budget alerts, usage reports by model, and cost forecasting. Zero dependencies. Part of the AI Toolkit collection by CodeVault. Includes: Cost calculation \u2014 Automatic per-request cost from token counts and model pricing; Pricing table \u2014 Built-in pricing for 14+ models (GPT-4o, Claude, Llama, Mixtral, etc; Budget alerts \u2014 Real-time budget monitoring with ok/warning/critical/exceeded levels; Usage reports \u2014 Per-model breakdown, daily cost charts, and top expensive requests; Cost forecasting \u2014 Predict future costs based on recent usage patterns; JSONL database \u2014 Append-only log for durability and easy integration.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Run demo with simulated API calls\npython src/llm_cost_tracker.py --demo\n\n# Show pricing table\npython src/llm_cost_tracker.py --pricing\n\n# Log a request\npython src/llm_cost_tracker.py --log '{\"model\":\"gpt-4o\",\"input_tokens\":500,\"output_tokens\":200}'\n\n# Check budget\npython src/llm_cost_tracker.py --budget 50.00 --db costs.jsonl\n\n# Generate usage report\npython src/llm_cost_tracker.py --report --db costs.jsonl\n\u2026", "llm-cost-tracker/\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 src/\n\u2502 \u2514\u2500\u2500 llm_cost_tracker.py # Core engine (~420 lines)\n\u2514\u2500\u2500 examples/\n \u251c\u2500\u2500 basic_usage.py # Programmatic usage example\n \u2514\u2500\u2500 sample_costs.jsonl # Sample cost data" ], "included_files": [ "README.md", "examples/basic_usage.py", "examples/sample_costs.jsonl", "guide/01_features.md", "guide/02_project-structure.md", "guide/03_usage-examples.md", "guide/04_license.md", "src/llm_cost_tracker.py" ], "file_count": 8, "download_size_bytes": 45121, "download_size_human": "44.1 KB", "product_url": "https://datanest-stores.com/ai-toolkit/#product-llm-cost-tracker", "preview_url": "https://datanest-stores.com/ai-toolkit/preview-llm-cost-tracker", "demo_url": "https://datanest-stores.com/ai-toolkit/demo-llm-cost-tracker/" }, { "product_id": "ai-toolkit:model-evaluation-tool", "slug": "model-evaluation-tool", "store": "ai-toolkit", "store_name": "AI Toolkit", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "ai agent", "ai prompts", "chatbot", "embeddings", "llm", "rag", "vector search" ], "title": "Model Evaluation Tool", "short_description": "Python model evaluation suite with accuracy, precision, recall, F1, and per-class reports.", "detailed_description": "Python model evaluation suite: accuracy, precision, recall, F1, confusion matrix, per-class reports, and benchmark runner. All metrics computed from scratch. Zero dependencies. Part of the AI Toolkit collection by CodeVault. Includes: Core metrics \u2014 Accuracy, precision, recall, F1 score (macro and weighted); Confusion matrix \u2014 Multi-class confusion matrix with ASCII table rendering; Per-class report \u2014 Precision, recall, F1, and support for every class; Benchmark runner \u2014 Evaluate across multiple test splits with mean/std statistics; File loaders \u2014 Load labels from JSONL, plain text, or structured JSON; JSON export \u2014 Export full reports for dashboards and CI pipelines.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "model-evaluation-tool/\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 src/\n\u2502 \u2514\u2500\u2500 model_evaluation_tool.py # Core engine (~350 lines)\n\u2514\u2500\u2500 examples/\n \u251c\u2500\u2500 basic_usage.py # Programmatic usage example\n \u2514\u2500\u2500 sample_predictions.jsonl # Sample prediction data", "from model_evaluation_tool import evaluate, benchmark\n\ny_true = [\"positive\", \"negative\", \"positive\", \"neutral\"]\ny_pred = [\"positive\", \"negative\", \"neutral\", \"neutral\"]\n\n# Full evaluation\nreport = evaluate(y_true, y_pred)\nprint(report.to_string())\nprint(f\"Accuracy: {report.accuracy:.4f}\")\nprint(f\"Macro F1: {report.macro_f1:.4f}\")\n\n# Access per-class metrics\nfor cls in report.per_class:\n print(f\"{cls.label}: P={cls.precision:.3f} R={cls.recall:.3f} F1={cls.f1:.3f}\")" ], "included_files": [ "README.md", "examples/basic_usage.py", "examples/sample_predictions.jsonl", "guide/01_features.md", "guide/02_project-structure.md", "guide/03_usage-examples.md", "guide/04_license.md", "src/model_evaluation_tool.py" ], "file_count": 8, "download_size_bytes": 39806, "download_size_human": "38.9 KB", "product_url": "https://datanest-stores.com/ai-toolkit/#product-model-evaluation-tool", "preview_url": "https://datanest-stores.com/ai-toolkit/preview-model-evaluation-tool", "demo_url": "https://datanest-stores.com/ai-toolkit/demo-model-evaluation-tool/" }, { "product_id": "ai-toolkit:prompt-engineering-kit", "slug": "prompt-engineering-kit", "store": "ai-toolkit", "store_name": "AI Toolkit", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "ai agent", "ai prompts", "chatbot", "embeddings", "llm", "rag", "vector search" ], "title": "Prompt Engineering Kit", "short_description": "Python prompt template library with chain-of-thought scaffolding, few-shot management, and versioning.", "detailed_description": "Python prompt template library with variable injection, chain-of-thought scaffolding, few-shot example management, and prompt quality scoring. Zero dependencies. Part of the AI Toolkit collection by CodeVault. Includes: Template library \u2014 8 battle-tested prompt templates for common tasks (summarize, code review, classify, extract, etc; Variable injection \u2014 {placeholder} syntax with automatic detection and validation; Chain-of-thought \u2014 Multi-step prompt chains that scaffold complex reasoning; Few-shot examples \u2014 Attach input/output examples to any template; Prompt scoring \u2014 Quality analyzer that rates prompts on length, specificity, structure, and clarity; Strategy tagging \u2014 Templates tagged by strategy (zero-shot, few-shot, CoT, role-play, structured output).", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "prompt-engineering-kit/\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 src/\n\u2502 \u2514\u2500\u2500 prompt_engineering_kit.py # Core engine (~400 lines)\n\u2514\u2500\u2500 examples/\n \u251c\u2500\u2500 basic_usage.py # Programmatic usage example\n \u2514\u2500\u2500 custom_templates.json # Sample custom template library", "from prompt_engineering_kit import PromptTemplate, score_prompt, PromptStrategy\n\n# Create a custom template\ntemplate = PromptTemplate(\n name=\"my_template\",\n description=\"Custom analysis prompt\",\n template=\"Analyze {topic} and provide {count} key insights.\",\n strategy=PromptStrategy.ZERO_SHOT,\n)\n\n# Render with variables\nprompt = template.render({\"topic\": \"serverless architecture\", \"count\": \"5\"})\nprint(prompt)\n\n\u2026" ], "included_files": [ "README.md", "examples/basic_usage.py", "examples/custom_templates.json", "guide/01_features.md", "guide/02_project-structure.md", "guide/03_built-in-templates.md", "guide/04_faq.md", "src/prompt_engineering_kit.py" ], "file_count": 8, "download_size_bytes": 54960, "download_size_human": "53.7 KB", "product_url": "https://datanest-stores.com/ai-toolkit/#product-prompt-engineering-kit", "preview_url": "https://datanest-stores.com/ai-toolkit/preview-prompt-engineering-kit", "demo_url": "https://datanest-stores.com/ai-toolkit/demo-prompt-engineering-kit/" }, { "product_id": "ai-toolkit:rag-pipeline-starter", "slug": "rag-pipeline-starter", "store": "ai-toolkit", "store_name": "AI Toolkit", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "ai agent", "ai prompts", "chatbot", "embeddings", "llm", "rag", "vector search" ], "title": "RAG Pipeline Starter", "short_description": "Python RAG pipeline with document ingestion, text chunking, vector store, retrieval engine, and prompt assembly.", "detailed_description": "Python RAG pipeline with document ingestion, text chunking, vector store, retrieval engine, and prompt assembly. Zero dependencies. Part of the AI Toolkit collection by CodeVault. Includes: Document loader \u2014 Ingest; Text chunker \u2014 Configurable chunk size and overlap; Vector store \u2014 In-memory store with cosine similarity search; Retrieval engine \u2014 Top-K retrieval with relevance scoring; Prompt assembler \u2014 Template-based prompt construction with context injection; Pipeline orchestrator \u2014 Single RAGPipeline class ties everything together.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Run the built-in demo\npython src/rag_pipeline.py --demo\n\n# Ingest a directory and query\npython src/rag_pipeline.py --ingest ./my-docs/ --query \"How do I deploy?\"\n\n# Interactive mode\npython src/rag_pipeline.py" ], "included_files": [ "README.md", "examples/basic_usage.py", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_license.md", "src/rag_pipeline.py" ], "file_count": 6, "download_size_bytes": 33540, "download_size_human": "32.8 KB", "product_url": "https://datanest-stores.com/ai-toolkit/#product-rag-pipeline-starter", "preview_url": "https://datanest-stores.com/ai-toolkit/preview-rag-pipeline-starter", "demo_url": "https://datanest-stores.com/ai-toolkit/demo-rag-pipeline-starter/" }, { "product_id": "ai-toolkit:vector-search-setup", "slug": "vector-search-setup", "store": "ai-toolkit", "store_name": "AI Toolkit", "domain": "ai", "category": "AI & Machine Learning", "tags": [ "ai", "ai agent", "ai prompts", "chatbot", "embeddings", "llm", "rag", "vector search" ], "title": "Vector Search Setup", "short_description": "Python vector search with index building, cosine similarity, and approximate nearest neighbor search.", "detailed_description": "Python vector search engine with index building, cosine similarity, LSH-powered approximate nearest neighbors, and a query API. All math from scratch. Zero dependencies. Part of the AI Toolkit collection by CodeVault. Includes: Cosine similarity \u2014 Manual implementation of dot product, magnitude, and cosine similarity; LSH indexing \u2014 Locality-sensitive hashing for sub-linear approximate nearest neighbor search; Exact search \u2014 Brute-force search for small indexes where perfect recall matters; Text-to-vector \u2014 Hashed bag-of-words encoder converts text to fixed-dimension vectors; Vocabulary builder \u2014 Automatic vocabulary extraction from your document corpus; Index persistence \u2014 Save and load indexes to/from JSON files.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Run the interactive demo with built-in sample data\npython src/vector_search_setup.py --demo\n\n# Build an index from a JSONL file\npython src/vector_search_setup.py --build-index data.jsonl --output my_index.json\n\n# Query an existing index\npython src/vector_search_setup.py --query \"machine learning algorithms\" --index my_index.json --top-k 5\n\n# Run performance benchmark\npython src/vector_search_setup.py --benchmark --dim 128 --num-vectors 5000", "vector-search-setup/\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 src/\n\u2502 \u2514\u2500\u2500 vector_search_setup.py # Core engine (~400 lines)\n\u2514\u2500\u2500 examples/\n \u251c\u2500\u2500 basic_usage.py # Programmatic usage example\n \u2514\u2500\u2500 sample_documents.jsonl # Sample data for index building" ], "included_files": [ "README.md", "examples/basic_usage.py", "examples/sample_documents.jsonl", "guide/01_features.md", "guide/02_project-structure.md", "guide/03_data-format.md", "guide/04_faq.md", "src/vector_search_setup.py" ], "file_count": 8, "download_size_bytes": 49181, "download_size_human": "48.0 KB", "product_url": "https://datanest-stores.com/ai-toolkit/#product-vector-search-setup", "preview_url": "https://datanest-stores.com/ai-toolkit/preview-vector-search-setup", "demo_url": "https://datanest-stores.com/ai-toolkit/demo-vector-search-setup/" }, { "product_id": "analytics-hub:ab-test-framework", "slug": "ab-test-framework", "store": "analytics-hub", "store_name": "Analytics Hub", "domain": "data", "category": "Data & Analytics", "tags": [ "analytics", "business intelligence", "dashboards", "data", "data visualization", "kpis", "metrics" ], "title": "A/B Test Framework", "short_description": "Statistical A/B testing framework with experiment setup, significance testing, and result analysis.", "detailed_description": "Statistical A/B testing framework: experiment setup, significance testing, sample size calculation, and result analysis. Know when a winner is real, not noise. Includes: Z-test for proportions \u2014 Industry-standard two-proportion z-test for conversion rate comparisons; Chi-squared test \u2014 Alternative significance test (2\u00d72 contingency table); Sample size calculator \u2014 Know how many visitors you need before starting; Confidence levels \u2014 90%, 95%, 99% with correct z-score thresholds; Simulation mode \u2014 Generate synthetic experiments to test your setup; Relative & absolute lift \u2014 Both metrics reported for full context.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "{\n \"control\": {\n \"name\": \"Original Checkout\",\n \"visitors\": 5000,\n \"conversions\": 160\n },\n \"treatment\": {\n \"name\": \"New Checkout\",\n \"visitors\": 5000,\n \"conversions\": 205\n }\n}", "variant,visitors,conversions\nControl,5000,160\nTreatment,5000,205" ], "included_files": [ "README.md", "examples/sample_experiment.json", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_output.md", "guide/04_faq.md", "src/ab_test_framework.py" ], "file_count": 7, "download_size_bytes": 39154, "download_size_human": "38.2 KB", "product_url": "https://datanest-stores.com/analytics-hub/#product-ab-test-framework", "preview_url": "https://datanest-stores.com/analytics-hub/preview-ab-test-framework", "demo_url": "https://datanest-stores.com/analytics-hub/demo-ab-test-framework/" }, { "product_id": "analytics-hub:analytics-hub-pro-bundle", "slug": "analytics-hub-pro-bundle", "store": "analytics-hub", "store_name": "Analytics Hub", "domain": "data", "category": "Data & Analytics", "tags": [ "analytics", "bundle", "business intelligence", "dashboards", "data", "data visualization", "kpis", "metrics" ], "title": "Analytics Hub Pro Bundle", "short_description": "All 10 Analytics Hub tools \u2014 $240 bought separately, yours for $59. Save $181 (75%): dashboards, funnels, cohorts, and SaaS metrics.", "detailed_description": "All 10 Analytics Hub tools \u2014 $240 bought separately, yours for $59. Save $181 (75%): dashboards, funnels, cohorts, and SaaS metrics.", "price_usd": 59, "currency": "usd", "is_bundle": true, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [ "analytics-hub-pro-bundle/\n\u251c\u2500\u2500 dashboard-generator/\n\u2502 \u251c\u2500\u2500 README.md\n\u2502 \u251c\u2500\u2500 src/dashboard_generator.py\n\u2502 \u251c\u2500\u2500 examples/\n\u2502 \u2514\u2500\u2500 LICENSE\n\u251c\u2500\u2500 event-tracker/\n\u2502 \u251c\u2500\u2500 README.md\n\u2502 \u251c\u2500\u2500 src/event_tracker.py\n\u2502 \u251c\u2500\u2500 examples/\n\u2502 \u2514\u2500\u2500 LICENSE\n\u251c\u2500\u2500 funnel-analyzer/\n\u2502 \u251c\u2500\u2500 README.md\n\u2502 \u251c\u2500\u2500 src/funnel_analyzer.py\n\u2026" ], "included_files": [ "README.md" ], "file_count": 1, "download_size_bytes": 252226, "download_size_human": "246.3 KB", "product_url": "https://datanest-stores.com/analytics-hub/#product-analytics-hub-pro-bundle", "preview_url": "https://datanest-stores.com/analytics-hub/preview-analytics-hub-pro-bundle", "demo_url": null }, { "product_id": "analytics-hub:cohort-tool", "slug": "cohort-tool", "store": "analytics-hub", "store_name": "Analytics Hub", "domain": "data", "category": "Data & Analytics", "tags": [ "analytics", "business intelligence", "dashboards", "data", "data visualization", "kpis", "metrics" ], "title": "Cohort Tool", "short_description": "Cohort analysis with user retention heatmaps, behavior segmentation, and time-series grouping.", "detailed_description": "Cohort analysis: user retention heatmaps, behavior segmentation, and time-series grouping. Understand how user behavior changes over time. Includes: Retention analysis \u2014 Track what percentage of users remain active over time; Flexible cohorts \u2014 Group by month, week, or day; Heatmap output \u2014 HTML retention heatmap with color-coded cells; Average retention \u2014 See the overall retention curve across all cohorts; JSON export \u2014 Machine-readable results for dashboards and further analysis; CLI and library \u2014 Use from the command line or import as a module.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Monthly cohort retention\npython src/cohort_tool.py --events examples/sample_cohort_data.json --cohort-by month\n\n# Weekly cohorts with HTML heatmap\npython src/cohort_tool.py --events data.json --cohort-by week --html heatmap.html\n\n# Export results\npython src/cohort_tool.py --events data.json --output retention.json --periods 12" ], "included_files": [ "README.md", "examples/sample_cohort_events.json", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_license.md", "src/cohort_tool.py" ], "file_count": 6, "download_size_bytes": 32320, "download_size_human": "31.6 KB", "product_url": "https://datanest-stores.com/analytics-hub/#product-cohort-tool", "preview_url": "https://datanest-stores.com/analytics-hub/preview-cohort-tool", "demo_url": "https://datanest-stores.com/analytics-hub/demo-cohort-tool/" }, { "product_id": "analytics-hub:dashboard-generator", "slug": "dashboard-generator", "store": "analytics-hub", "store_name": "Analytics Hub", "domain": "data", "category": "Data & Analytics", "tags": [ "analytics", "business intelligence", "dashboards", "data", "data visualization", "kpis", "metrics" ], "title": "Dashboard Generator", "short_description": "Generate interactive HTML dashboards from JSON/CSV data with charts, tables, and KPI cards.", "detailed_description": "Generate interactive HTML dashboards from JSON/CSV data with charts, tables, and KPI cards. Auto-detects chart types from your data, or use a config file for full control. Includes: Auto-detection \u2014 Feed it any JSON/CSV and it builds a sensible dashboard automatically; SVG charts \u2014 Bar, line, and pie charts rendered as inline SVG (no JS dependencies); KPI cards \u2014 Highlight key metrics with trend indicators (up/down/flat); Data tables \u2014 Sortable, responsive tables for raw data inspection; Multi-series line charts \u2014 Compare multiple metrics on one timeline; Config file support \u2014 Full control over layout via JSON config.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Auto-generate dashboard from CSV data\npython src/dashboard_generator.py --data examples/sample_sales.csv --title \"Sales Dashboard\"\n\n# Auto-generate from JSON\npython src/dashboard_generator.py --data examples/sample_metrics.json\n\n# Use a config file for full control\npython src/dashboard_generator.py --config examples/dashboard_config.json\n\n# Custom output path and branding\npython src/dashboard_generator.py --data data.csv --output report.html --color \"#FF3366\"", "{\n \"title\": \"Monthly Report\",\n \"subtitle\": \"Q1 2026 Performance\",\n \"brand_color\": \"#00E5FF\",\n \"kpis\": [\n {\"label\": \"Revenue\", \"value\": \"$42.5K\", \"change\": 12.3},\n {\"label\": \"Users\", \"value\": \"1,847\", \"change\": -3.1},\n {\"label\": \"Churn\", \"value\": \"2.1%\", \"change\": -0.5}\n ],\n \"charts\": [\n {\n \"chart_type\": \"bar\",\n \"title\": \"Revenue by Month\",\n \"labels\": [\"Jan\", \"Feb\", \"Mar\"],\n\u2026" ], "included_files": [ "README.md", "examples/dashboard_config.json", "examples/sample_sales.csv", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_configuration-reference.md", "guide/04_license.md", "src/dashboard_generator.py" ], "file_count": 8, "download_size_bytes": 50233, "download_size_human": "49.1 KB", "product_url": "https://datanest-stores.com/analytics-hub/#product-dashboard-generator", "preview_url": "https://datanest-stores.com/analytics-hub/preview-dashboard-generator", "demo_url": "https://datanest-stores.com/analytics-hub/demo-dashboard-generator/" }, { "product_id": "analytics-hub:data-visualizer", "slug": "data-visualizer", "store": "analytics-hub", "store_name": "Analytics Hub", "domain": "data", "category": "Data & Analytics", "tags": [ "analytics", "business intelligence", "dashboards", "data", "data visualization", "kpis", "metrics" ], "title": "Data Visualizer", "short_description": "Pure Python SVG chart generator for line, bar, pie, and scatter charts from JSON/CSV data.", "detailed_description": "Pure Python SVG chart generator: create publication-ready line, bar, pie, and scatter charts from JSON/CSV data. Zero external dependencies. Includes: Four chart types \u2014 Bar, line, pie, and scatter plots; Pure SVG output \u2014 Scalable vector graphics, works in any browser or image viewer; Auto-detection \u2014 Picks sensible column defaults from your data; Nice axis ticks \u2014 Automatically rounds tick values for clean labels (100, 200, 300 not 97, 194, 291); Dark theme \u2014 Designed for dark backgrounds with high-contrast colors; Multi-series line charts \u2014 Compare multiple data series on one chart.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "month,revenue,users\nJan,42500,185\nFeb,45800,201\nMar,48200,218", "from data_visualizer import bar_chart, ChartConfig\n\nconfig = ChartConfig(title=\"Monthly Revenue\", color=\"#00E5FF\")\nsvg = bar_chart(\n labels=[\"Jan\", \"Feb\", \"Mar\", \"Apr\"],\n values=[42500, 45800, 48200, 51100],\n config=config,\n)\n\nPath(\"chart.svg\").write_text(svg)" ], "included_files": [ "README.md", "examples/sample_chart_data.csv", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_input-format.md", "guide/04_faq.md", "src/data_visualizer.py" ], "file_count": 7, "download_size_bytes": 44479, "download_size_human": "43.4 KB", "product_url": "https://datanest-stores.com/analytics-hub/#product-data-visualizer", "preview_url": "https://datanest-stores.com/analytics-hub/preview-data-visualizer", "demo_url": "https://datanest-stores.com/analytics-hub/demo-data-visualizer/" }, { "product_id": "analytics-hub:event-tracker", "slug": "event-tracker", "store": "analytics-hub", "store_name": "Analytics Hub", "domain": "data", "category": "Data & Analytics", "tags": [ "analytics", "business intelligence", "dashboards", "data", "data visualization", "kpis", "metrics" ], "title": "Event Tracker", "short_description": "Lightweight event tracking system for custom events, user actions, and page views with SQLite or JSON storage.", "detailed_description": "Lightweight event tracking system for custom events, user actions, and page views. Stores to SQLite or JSON with built-in querying and aggregation. Includes: Custom events \u2014 Track any event with arbitrary properties; User tracking \u2014 Associate events with user IDs and sessions; Page views \u2014 Built-in shortcut for page view tracking; SQLite storage \u2014 Indexed database with efficient querying; JSON storage \u2014 Append-only JSONL files for streaming pipelines; Aggregation \u2014 Group-by stats, counts, unique users, averages.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Track an event\npython src/event_tracker.py track \"signup\" --user \"user_123\" --props '{\"plan\": \"pro\"}'\n\n# Track a page view\npython src/event_tracker.py track \"page_view\" --user \"user_123\" --props '{\"page\": \"/pricing\"}'\n\n# Query recent events\npython src/event_tracker.py query --last 7d\n\n# Query by event name\npython src/event_tracker.py query --event \"signup\" --limit 50\n\n# View statistics\npython src/event_tracker.py stats\n\u2026", "from event_tracker import SQLiteStore, track_event, track_page_view\n\nstore = SQLiteStore(\"my_events.db\")\n\n# Track custom events\ntrack_event(\"purchase\", user_id=\"user_456\", properties={\"amount\": 49.99}, store=store)\ntrack_page_view(\"/dashboard\", user_id=\"user_456\", store=store)\n\n# Query events\nevents = store.query(event_name=\"purchase\", limit=10)\nstats = store.stats(group_by=\"event_name\")" ], "included_files": [ "README.md", "examples/sample_events.json", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_event-schema.md", "guide/04_faq.md", "src/event_tracker.py" ], "file_count": 7, "download_size_bytes": 36068, "download_size_human": "35.2 KB", "product_url": "https://datanest-stores.com/analytics-hub/#product-event-tracker", "preview_url": "https://datanest-stores.com/analytics-hub/preview-event-tracker", "demo_url": "https://datanest-stores.com/analytics-hub/demo-event-tracker/" }, { "product_id": "analytics-hub:funnel-analyzer", "slug": "funnel-analyzer", "store": "analytics-hub", "store_name": "Analytics Hub", "domain": "data", "category": "Data & Analytics", "tags": [ "analytics", "business intelligence", "dashboards", "data", "data visualization", "kpis", "metrics" ], "title": "Funnel Analyzer", "short_description": "Analyze conversion funnels with step-by-step rates, drop-off points, cohort comparison, and time-to-convert metrics.", "detailed_description": "Analyze conversion funnels: step-by-step conversion rates, drop-off points, cohort comparison, and time-to-convert metrics. Find where users bail and fix your flow. Includes: Step-by-step analysis \u2014 Conversion rate between each funnel step; Drop-off detection \u2014 Exact numbers and percentages of users lost at each step; Overall conversion \u2014 End-to-end funnel conversion rate; Time-to-convert \u2014 Average time from funnel entry to each step; Cohort comparison \u2014 Compare funnel performance across user segments; Visual bar report \u2014 ASCII bar chart showing funnel shape.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Analyze a funnel\npython src/funnel_analyzer.py --events examples/sample_funnel.json \\\n --steps \"page_view,signup,activate,purchase\"\n\n# Compare cohorts\npython src/funnel_analyzer.py --events examples/sample_funnel.json \\\n --steps \"page_view,signup,purchase\" --cohort source\n\n# Export results\npython src/funnel_analyzer.py --events data.json \\\n --steps \"visit,signup,activate\" --output results.json", "[\n {\"event_name\": \"page_view\", \"user_id\": \"u1\", \"timestamp\": \"2026-01-01T10:00:00Z\"},\n {\"event_name\": \"signup\", \"user_id\": \"u1\", \"timestamp\": \"2026-01-01T10:05:00Z\"},\n {\"event_name\": \"activate\", \"user_id\": \"u1\", \"timestamp\": \"2026-01-01T10:30:00Z\"}\n]" ], "included_files": [ "README.md", "examples/sample_funnel.json", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_output-example.md", "guide/04_license.md", "src/funnel_analyzer.py" ], "file_count": 7, "download_size_bytes": 35075, "download_size_human": "34.3 KB", "product_url": "https://datanest-stores.com/analytics-hub/#product-funnel-analyzer", "preview_url": "https://datanest-stores.com/analytics-hub/preview-funnel-analyzer", "demo_url": "https://datanest-stores.com/analytics-hub/demo-funnel-analyzer/" }, { "product_id": "analytics-hub:kpi-dashboard", "slug": "kpi-dashboard", "store": "analytics-hub", "store_name": "Analytics Hub", "domain": "data", "category": "Data & Analytics", "tags": [ "analytics", "business intelligence", "dashboards", "data", "data visualization", "kpis", "metrics" ], "title": "KPI Dashboard", "short_description": "Self-contained HTML dashboard with metric cards, threshold alerts, trend indicators, and time period comparison.", "detailed_description": "KPI dashboard builder: generate a self-contained HTML dashboard with metric cards, threshold alerts, trend indicators, traffic-light status, and target progress bars. Know your numbers at a glance. Includes: Traffic-light status \u2014 Green/yellow/red indicators based on configurable thresholds; Trend indicators \u2014 Period-over-period change with directional arrows; Target progress bars \u2014 Visual progress toward goals; Alert summary \u2014 Critical and warning KPIs highlighted at the top; Category grouping \u2014 Organize metrics by category (revenue, engagement, growth); Auto-refresh \u2014 Set a refresh interval for live monitoring.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Generate KPI dashboard from JSON\npython src/kpi_dashboard.py --data examples/sample_kpis.json --output dashboard.html\n\n# Custom title and auto-refresh every 30 seconds\npython src/kpi_dashboard.py --data kpis.json --title \"Acme Corp KPIs\" --refresh 30\n\n# Custom brand color\npython src/kpi_dashboard.py --data kpis.csv --color \"#FF3366\" --output report.html", "[\n {\n \"name\": \"MRR\",\n \"value\": 68700,\n \"unit\": \"$\",\n \"green_above\": 60000,\n \"yellow_above\": 45000,\n \"trend\": 5.2,\n \"target\": 75000,\n \"description\": \"Monthly Recurring Revenue\",\n \"category\": \"revenue\"\n }\n]" ], "included_files": [ "README.md", "examples/sample_kpis.json", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_scheduling.md", "guide/04_license.md", "src/kpi_dashboard.py" ], "file_count": 7, "download_size_bytes": 37579, "download_size_human": "36.7 KB", "product_url": "https://datanest-stores.com/analytics-hub/#product-kpi-dashboard", "preview_url": "https://datanest-stores.com/analytics-hub/preview-kpi-dashboard", "demo_url": "https://datanest-stores.com/analytics-hub/demo-kpi-dashboard/" }, { "product_id": "analytics-hub:log-analyzer", "slug": "log-analyzer", "store": "analytics-hub", "store_name": "Analytics Hub", "domain": "data", "category": "Data & Analytics", "tags": [ "analytics", "business intelligence", "dashboards", "data", "data visualization", "kpis", "metrics" ], "title": "Log Analyzer", "short_description": "Parse access logs, detect error patterns, and analyze traffic by hour, path, status code, and IP frequency.", "detailed_description": "Log analysis tool: parse access logs (Apache/Nginx Combined format), detect error patterns, analyze traffic by hour/path/status, and spot anomalies. Turn raw logs into insights. Includes: Access log parsing \u2014 Handles Apache/Nginx Combined Log Format out of the box; Traffic analysis \u2014 Requests by hour, top paths, top IPs, status code breakdown; Error analysis \u2014 Error rates, top error paths, error trends over time; Anomaly detection \u2014 Statistical spike detection for traffic, IPs, and error rates; Pretty terminal output \u2014 Hourly traffic histograms, formatted tables; JSON export \u2014 Machine-readable reports for automation.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "192.168.1.100 - - [14/Mar/2026:10:00:00 +0000] \"GET /index.html HTTP/1.1\" 200 2326 \"https://docs.example.com/\" \"Mozilla/5.0\"" ], "included_files": [ "README.md", "examples/sample_access.log", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_reports.md", "guide/04_faq.md", "src/log_analyzer.py" ], "file_count": 7, "download_size_bytes": 37154, "download_size_human": "36.3 KB", "product_url": "https://datanest-stores.com/analytics-hub/#product-log-analyzer", "preview_url": "https://datanest-stores.com/analytics-hub/preview-log-analyzer", "demo_url": "https://datanest-stores.com/analytics-hub/demo-log-analyzer/" }, { "product_id": "analytics-hub:metric-calculator", "slug": "metric-calculator", "store": "analytics-hub", "store_name": "Analytics Hub", "domain": "data", "category": "Data & Analytics", "tags": [ "analytics", "business intelligence", "dashboards", "data", "data visualization", "kpis", "metrics" ], "title": "Metric Calculator", "short_description": "SaaS metrics library: MRR, ARR, churn rate, LTV, CAC, NPS, net revenue retention, and quick ratio.", "detailed_description": "SaaS metrics library: MRR, ARR, churn rate, LTV, CAC, NPS, net revenue retention, quick ratio, and more. Feed it subscription data and get actionable business metrics with trend detection. Includes: 12+ SaaS metrics \u2014 MRR, ARR, ARPU, churn, revenue churn, NRR, LTV, CAC, LTV; Trend detection \u2014 Period-over-period change tracking with direction indicators; Healthy range indicators \u2014 Know if your metrics are in the good, warning, or danger zone; Multi-period analysis \u2014 Track metrics across months to spot trends; Formula documentation \u2014 Every metric function explains WHY it matters, not just how to calculate it; Pretty terminal output \u2014 Formatted metrics table with arrows and ranges.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Calculate all metrics for the latest period\npython src/metric_calculator.py --data examples/sample_subscriptions.json\n\n# Show last 6 periods with trends\npython src/metric_calculator.py --data examples/sample_subscriptions.json --periods 6\n\n# Include CAC (provide total acquisition spend)\npython src/metric_calculator.py --data examples/sample_subscriptions.json --acquisition-cost 15000\n\n# Export to JSON\npython src/metric_calculator.py --data data.json --output metrics_report.json", "[\n {\n \"period\": \"2026-01\",\n \"mrr\": 42500,\n \"new_mrr\": 5200,\n \"expansion_mrr\": 1800,\n \"contraction_mrr\": 400,\n \"churned_mrr\": 1200,\n \"total_customers\": 185,\n \"new_customers\": 32,\n \"churned_customers\": 4,\n \"revenue\": 42500\n }\n]" ], "included_files": [ "README.md", "examples/sample_subscriptions.json", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_metrics-reference.md", "guide/04_license.md", "src/metric_calculator.py" ], "file_count": 7, "download_size_bytes": 38162, "download_size_human": "37.3 KB", "product_url": "https://datanest-stores.com/analytics-hub/#product-metric-calculator", "preview_url": "https://datanest-stores.com/analytics-hub/preview-metric-calculator", "demo_url": "https://datanest-stores.com/analytics-hub/demo-metric-calculator/" }, { "product_id": "analytics-hub:report-generator", "slug": "report-generator", "store": "analytics-hub", "store_name": "Analytics Hub", "domain": "data", "category": "Data & Analytics", "tags": [ "analytics", "business intelligence", "dashboards", "data", "data visualization", "kpis", "metrics" ], "title": "Report Generator", "short_description": "Automated reporting tool generating polished HTML reports with KPI cards from JSON/CSV data.", "detailed_description": "Automated reporting tool: load data from JSON/CSV, apply templates, and generate polished HTML reports with KPI cards, summary statistics, and data tables. Schedule with cron for hands-free reporting. Includes: Three built-in templates \u2014 Summary, Detailed, and Table-Only for different needs; Auto-detection \u2014 Identifies numeric columns and calculates summary stats automatically; KPI extraction \u2014 Surfaces revenue totals, rate averages, and row counts as headline cards; Dark theme \u2014 Professional dark UI matching the Analytics Hub brand; Responsive tables \u2014 Data tables that work on desktop and mobile; Column statistics \u2014 Count, total, mean, median, min, max, std dev for every numeric column.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "month,revenue,customers,churn_rate\n2026-01,42500,185,2.1\n2026-02,45800,201,1.8\n2026-03,48200,218,1.5", "# Generate daily report at 6 AM\n0 6 * * * python /path/to/report_generator.py --data /path/to/data.csv --template summary --output /var/www/reports/daily.html" ], "included_files": [ "README.md", "examples/sample_revenue.csv", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_input-format.md", "guide/04_faq.md", "src/report_generator.py" ], "file_count": 7, "download_size_bytes": 40954, "download_size_human": "40.0 KB", "product_url": "https://datanest-stores.com/analytics-hub/#product-report-generator", "preview_url": "https://datanest-stores.com/analytics-hub/preview-report-generator", "demo_url": "https://datanest-stores.com/analytics-hub/demo-report-generator/" }, { "product_id": "api-developer:rest-api-design-guide", "slug": "rest-api-design-guide", "store": "api-developer", "store_name": "API Developer Pro", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "api gateway", "api testing", "dev", "documentation", "graphql", "grpc", "rest api", "webhooks" ], "title": "REST API Design Guide", "short_description": "RESTful API design patterns, versioning strategies, pagination, filtering, error handling, and OpenAPI specs.", "detailed_description": "A comprehensive, battle-tested reference for designing robust REST APIs. Covers design principles, versioning strategies, pagination, filtering, error handling, idempotency, caching, and includes a complete OpenAPI 3.1 specification for a realistic Orders API -- plus runnable Python reference implementations you can drop into any project.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [ "Python 3.10" ], "supported_technologies": [ "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "guides/rest-design-principles.md # Foundations\nguides/versioning-strategies.md # How to evolve your API\nguides/pagination-and-filtering.md # Handling large datasets\nguides/error-handling.md # Consistent error responses\nguides/idempotency-and-caching.md # Performance & reliability", "# Pagination helpers -- see offset + cursor pagination in action\npython3 src/pagination.py\n\n# RFC 7807 error envelopes -- generate Problem Details JSON\npython3 src/error_envelope.py\n\n# ETag utilities -- generate and validate entity tags\npython3 src/etag.py" ], "included_files": [ "README.md", "examples/error-responses.json", "examples/openapi.yaml", "examples/sample-requests.http", "guide/error-handling.md", "guide/idempotency-and-caching.md", "guide/pagination-and-filtering.md", "guide/rest-design-principles.md", "guide/versioning-strategies.md", "guides/error-handling.md", "guides/idempotency-and-caching.md", "guides/pagination-and-filtering.md", "guides/rest-design-principles.md", "guides/versioning-strategies.md", "src/error_envelope.py", "src/etag.py", "src/pagination.py", "tests/test_error_envelope.py", "tests/test_pagination.py" ], "file_count": 19, "download_size_bytes": 178309, "download_size_human": "174.1 KB", "product_url": "https://datanest-stores.com/api-developer/#product-rest-api-design-guide", "preview_url": "https://datanest-stores.com/api-developer/preview-rest-api-design-guide", "demo_url": "https://datanest-stores.com/api-developer/demo-rest-api-design-guide/" }, { "product_id": "api-developer:graphql-starter-kit", "slug": "graphql-starter-kit", "store": "api-developer", "store_name": "API Developer Pro", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "api gateway", "api testing", "dev", "documentation", "graphql", "grpc", "rest api", "webhooks" ], "title": "GraphQL Starter Kit", "short_description": "GraphQL server setup with schema design, resolvers, authentication, dataloader patterns, and subscription support.", "detailed_description": "A complete, runnable GraphQL server built with Python's standard library (zero pip dependencies). Includes a realistic blog + commerce schema, resolvers with authentication, the DataLoader pattern for N+1 elimination, and subscription support via Server-Sent Events.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python", "Shell" ], "compatibility_notes": null, "preview_snippets": [ "cd graphql-starter-kit\npython -m src", "GraphQL server running at http://127.0.0.1:4000/graphql\nGraphiQL IDE at http://127.0.0.1:4000/\nPress Ctrl+C to stop." ], "included_files": [ "README.md", "examples/curl-examples.sh", "examples/queries.graphql", "examples/variables.json", "guide/auth-and-context.md", "guide/n+1-and-dataloader.md", "guide/resolver-patterns.md", "guide/schema-design.md", "guides/auth-and-context.md", "guides/n+1-and-dataloader.md", "guides/resolver-patterns.md", "guides/schema-design.md", "schema/schema.graphql", "src/__init__.py", "src/__main__.py", "src/auth.py", "src/dataloader.py", "src/executor.py", "src/resolvers.py", "src/schema_parser.py", "src/server.py", "src/subscriptions.py", "tests/test_dataloader.py", "tests/test_resolvers.py" ], "file_count": 24, "download_size_bytes": 138212, "download_size_human": "135.0 KB", "product_url": "https://datanest-stores.com/api-developer/#product-graphql-starter-kit", "preview_url": "https://datanest-stores.com/api-developer/preview-graphql-starter-kit", "demo_url": "https://datanest-stores.com/api-developer/demo-graphql-starter-kit/" }, { "product_id": "api-developer:api-gateway-patterns", "slug": "api-gateway-patterns", "store": "api-developer", "store_name": "API Developer Pro", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "api gateway", "api testing", "dev", "documentation", "graphql", "grpc", "rest api", "webhooks" ], "title": "API Gateway Patterns", "short_description": "Kong, AWS API Gateway, and custom gateway configs with rate limiting, auth, transformation, and monitoring.", "detailed_description": "Production-ready API gateway configurations for Kong, AWS API Gateway, and a custom Python gateway implementation. Includes rate limiting, authentication, request transformation, and monitoring -- with complete guides explaining when and why to use each approach.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "# Start with demo configuration (proxies to public test API)\ncd api-gateway-patterns\npython -m src\n\n# Or with a config file\npython -m src --config configs/gateway-dev.json\n\n# Test it\ncurl http://127.0.0.1:8080/api/posts\ncurl http://127.0.0.1:8080/api/posts/1", "python src/rate_limiter.py\n# Shows token bucket behavior: burst, depletion, refill" ], "included_files": [ "README.md", "aws/api-gateway-openapi.yaml", "aws/lambda-authorizer.py", "aws/usage-plans.json", "configs/gateway-dev.json", "configs/gateway-prod.json", "guide/auth-patterns.md", "guide/gateway-comparison.md", "guide/rate-limiting-strategies.md", "guides/auth-patterns.md", "guides/gateway-comparison.md", "guides/rate-limiting-strategies.md", "kong/jwt-auth.yaml", "kong/kong.yaml", "kong/rate-limiting.yaml", "kong/request-transformer.yaml", "src/__init__.py", "src/__main__.py", "src/auth_middleware.py", "src/gateway.py", "src/rate_limiter.py", "src/transform_middleware.py", "tests/test_rate_limiter.py" ], "file_count": 23, "download_size_bytes": 137957, "download_size_human": "134.7 KB", "product_url": "https://datanest-stores.com/api-developer/#product-api-gateway-patterns", "preview_url": "https://datanest-stores.com/api-developer/preview-api-gateway-patterns", "demo_url": "https://datanest-stores.com/api-developer/demo-api-gateway-patterns/" }, { "product_id": "api-developer:webhook-framework", "slug": "webhook-framework", "store": "api-developer", "store_name": "API Developer Pro", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "api gateway", "api testing", "dev", "documentation", "graphql", "grpc", "rest api", "webhooks" ], "title": "Webhook Framework", "short_description": "Webhook delivery system with retry logic, signature verification, event sourcing, and monitoring dashboard.", "detailed_description": "A complete webhook delivery system with HMAC signature verification, exponential backoff retry, SQLite-backed event sourcing, and an HTML monitoring dashboard. Zero external dependencies -- Python 3.10+ standard library only. Includes: Webhook delivery engine with configurable retry logic (exponential backoff + full jitter); HMAC-SHA256 signatures with timestamp binding to prevent replay attacks; Append-only event store on SQLite for full delivery audit trail; Webhook receiver with signature verification, idempotency checking, and event routing; Monitoring dashboard that renders delivery stats as an HTML page; Idempotency keys to prevent duplicate processing on retries.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "python examples/send_webhook.py", "python -m src.dashboard --port 8080 --db webhook_events.db\n# Open http://127.0.0.1:8080" ], "included_files": [ "README.md", "examples/sample-events.json", "examples/send_webhook.py", "examples/verify_signature.py", "guide/event-sourcing.md", "guide/retry-and-idempotency.md", "guide/signature-verification.md", "guides/event-sourcing.md", "guides/retry-and-idempotency.md", "guides/signature-verification.md", "src/__init__.py", "src/dashboard.py", "src/delivery.py", "src/event_store.py", "src/receiver.py", "src/signatures.py", "templates/dashboard.html", "tests/test_delivery_retry.py", "tests/test_signatures.py" ], "file_count": 19, "download_size_bytes": 78036, "download_size_human": "76.2 KB", "product_url": "https://datanest-stores.com/api-developer/#product-webhook-framework", "preview_url": "https://datanest-stores.com/api-developer/preview-webhook-framework", "demo_url": "https://datanest-stores.com/api-developer/demo-webhook-framework/" }, { "product_id": "api-developer:api-testing-automation", "slug": "api-testing-automation", "store": "api-developer", "store_name": "API Developer Pro", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "api gateway", "api testing", "dev", "documentation", "graphql", "grpc", "rest api", "webhooks" ], "title": "API Testing Automation", "short_description": "Postman collections, Newman CI integration, contract testing with Pact, and load testing with k6 scripts.", "detailed_description": "A complete API testing toolkit with Postman collections, Newman CI integration, Pact contract tests, k6 load tests, and Python-based smoke testing. Everything you need for a professional API testing pipeline.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python", "Node.js" ], "supported_versions": [], "supported_technologies": [ "Python", "JavaScript", "Shell", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "npm install -g newman\nnewman run postman/collection.json -e postman/environment.json", "python src/smoke_test.py --base-url https://api.docs.example.com/v1 --verbose" ], "included_files": [ "README.md", "guide/ci-integration.md", "guide/load-testing-guide.md", "guide/testing-strategy.md", "guides/ci-integration.md", "guides/load-testing-guide.md", "guides/testing-strategy.md", "k6/load-test.js", "k6/spike-test.js", "k6/stress-test.js", "newman/ci-run.sh", "newman/github-actions.yml", "pact/consumer-test.js", "pact/pacts/sample-pact.json", "pact/provider-verify.js", "postman/collection.json", "postman/environment.json", "src/contract_check.py", "src/smoke_test.py" ], "file_count": 19, "download_size_bytes": 68269, "download_size_human": "66.7 KB", "product_url": "https://datanest-stores.com/api-developer/#product-api-testing-automation", "preview_url": "https://datanest-stores.com/api-developer/preview-api-testing-automation", "demo_url": "https://datanest-stores.com/api-developer/demo-api-testing-automation/" }, { "product_id": "api-developer:api-documentation-toolkit", "slug": "api-documentation-toolkit", "store": "api-developer", "store_name": "API Developer Pro", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "api gateway", "api testing", "dev", "documentation", "graphql", "grpc", "rest api", "webhooks" ], "title": "API Documentation Toolkit", "short_description": "Swagger/OpenAPI generators, Redoc themes, API changelog automation, and developer portal templates.", "detailed_description": "A collection of Python tools for generating, maintaining, and publishing API documentation. Includes an OpenAPI spec generator that extracts route metadata from annotated Python handlers, a changelog generator that diffs two OpenAPI specs and produces markdown, and a static developer portal builder -- all using the Python standard library only.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [ "Python 3.10" ], "supported_technologies": [ "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "# Run the generator on a sample handler file\npython3 src/openapi_from_code.py\n\n# Or import and use in your own script\npython3 -c \"\nfrom src.openapi_from_code import OpenAPIGenerator\ngen = OpenAPIGenerator(title='My API', version='1.0.0')\ngen.add_route('GET', '/users', summary='List users', tags=['Users'])\nprint(gen.to_yaml())\n\"", "# Diff the included v1 and v2 sample specs\npython3 src/changelog_gen.py examples/two-versioned-specs/v1.yaml examples/two-versioned-specs/v2.yaml\n\n# Output is a structured markdown changelog" ], "included_files": [ "README.md", "docs/QUICKSTART.md", "docs/how-the-generators-work.md", "examples/sample-openapi.yaml", "examples/two-versioned-specs/v1.yaml", "examples/two-versioned-specs/v2.yaml", "guide/01_what-s-inside.md", "guide/02_quick-start.md", "guide/03_configuration.md", "guide/04_faq.md", "src/changelog_gen.py", "src/openapi_from_code.py", "src/portal_build.py", "templates/changelog-template.md", "templates/developer-portal.html", "templates/redoc-theme.css", "tests/test_changelog_gen.py" ], "file_count": 17, "download_size_bytes": 90162, "download_size_human": "88.0 KB", "product_url": "https://datanest-stores.com/api-developer/#product-api-documentation-toolkit", "preview_url": "https://datanest-stores.com/api-developer/preview-api-documentation-toolkit", "demo_url": "https://datanest-stores.com/api-developer/demo-api-documentation-toolkit/" }, { "product_id": "api-developer:grpc-service-templates", "slug": "grpc-service-templates", "store": "api-developer", "store_name": "API Developer Pro", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "api gateway", "api testing", "dev", "documentation", "graphql", "grpc", "rest api", "webhooks" ], "title": "gRPC Service Templates", "short_description": "Protocol buffer schemas, service implementations in Python/Go/Node, streaming patterns, and load balancing configs.", "detailed_description": "Production-ready gRPC service templates in Python, Go, and Node.js \u2014 with shared protobuf definitions, streaming patterns, load balancing configs, and deep-dive guides.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python", "Node.js" ], "supported_versions": [], "supported_technologies": [ "Python", "JavaScript", "Go", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "# 1. Install dependencies\npip install grpcio grpcio-tools grpcio-health-checking grpcio-reflection\n\n# 2. Generate stubs from proto files\nmkdir -p python/gen\npython -m grpc_tools.protoc \\\n -I. \\\n --python_out=python/gen \\\n --grpc_python_out=python/gen \\\n proto/common.proto proto/service.proto\n\n# 3. Start the server\npython python/server.py --port 50051\n\n\u2026", "# 1. Install protoc plugins\ngo install google.golang.org/protobuf/cmd/protoc-gen-go@latest\ngo install google.golang.org/grpc/cmd/protoc-gen-go-grpc@latest\n\n# 2. Initialize module and install dependencies\ncd go/\ngo mod init github.com/example/grpc-templates\ngo get google.golang.org/grpc\ngo get google.golang.org/protobuf\ncd ..\n\n# 3. Generate stubs\nmkdir -p go/gen/catalogpb\nprotoc -I. \\\n\u2026" ], "included_files": [ "README.md", "configs/envoy-load-balancer.yaml", "configs/grpc-health-check.yaml", "go/client.go", "go/server.go", "guide/codegen-instructions.md", "guide/load-balancing.md", "guide/streaming-patterns.md", "guides/codegen-instructions.md", "guides/load-balancing.md", "guides/streaming-patterns.md", "node/client.js", "node/package.json", "node/server.js", "proto/common.proto", "proto/service.proto", "python/client.py", "python/server.py" ], "file_count": 18, "download_size_bytes": 136910, "download_size_human": "133.7 KB", "product_url": "https://datanest-stores.com/api-developer/#product-grpc-service-templates", "preview_url": "https://datanest-stores.com/api-developer/preview-grpc-service-templates", "demo_url": "https://datanest-stores.com/api-developer/demo-grpc-service-templates/" }, { "product_id": "api-developer:api-complete-bundle", "slug": "api-complete-bundle", "store": "api-developer", "store_name": "API Developer Pro", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "api gateway", "api testing", "bundle", "dev", "documentation", "graphql", "grpc", "rest api", "webhooks" ], "title": "API Developer Bundle", "short_description": "All 7 API tools \u2014 $223 bought separately, yours for $79. Save $144 (65%): REST, GraphQL, and gRPC design, docs, and testing.", "detailed_description": "A comprehensive, battle-tested reference for designing robust REST APIs. Covers design principles, versioning strategies, pagination, filtering, error handling, idempotency, caching, and includes a complete OpenAPI 3.1 specification for a realistic Orders API -- plus runnable Python reference implementations you can drop into any project.", "price_usd": 79, "currency": "usd", "is_bundle": true, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [ "Python", "Node.js" ], "supported_versions": [ "Python 3.10" ], "supported_technologies": [ "Python", "JavaScript", "Go", "Shell", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "guides/rest-design-principles.md # Foundations\nguides/versioning-strategies.md # How to evolve your API\nguides/pagination-and-filtering.md # Handling large datasets\nguides/error-handling.md # Consistent error responses\nguides/idempotency-and-caching.md # Performance & reliability", "# Pagination helpers -- see offset + cursor pagination in action\npython3 src/pagination.py\n\n# RFC 7807 error envelopes -- generate Problem Details JSON\npython3 src/error_envelope.py\n\n# ETag utilities -- generate and validate entity tags\npython3 src/etag.py" ], "included_files": [ "products/api-documentation-toolkit/README.md", "products/api-documentation-toolkit/source/README.md", "products/api-documentation-toolkit/source/docs/QUICKSTART.md", "products/api-documentation-toolkit/source/docs/how-the-generators-work.md", "products/api-documentation-toolkit/source/examples/sample-openapi.yaml", "products/api-documentation-toolkit/source/examples/two-versioned-specs/v1.yaml", "products/api-documentation-toolkit/source/examples/two-versioned-specs/v2.yaml", "products/api-documentation-toolkit/source/guide/01_what-s-inside.md", "products/api-documentation-toolkit/source/guide/02_quick-start.md", "products/api-documentation-toolkit/source/guide/03_configuration.md", "products/api-documentation-toolkit/source/guide/04_faq.md", "products/api-documentation-toolkit/source/src/changelog_gen.py", "products/api-documentation-toolkit/source/src/openapi_from_code.py", "products/api-documentation-toolkit/source/src/portal_build.py", "products/api-documentation-toolkit/source/templates/changelog-template.md", "products/api-documentation-toolkit/source/templates/developer-portal.html", "products/api-documentation-toolkit/source/templates/redoc-theme.css", "products/api-documentation-toolkit/source/tests/test_changelog_gen.py", "products/api-gateway-patterns/README.md", "products/api-gateway-patterns/source/README.md", "products/api-gateway-patterns/source/aws/api-gateway-openapi.yaml", "products/api-gateway-patterns/source/aws/lambda-authorizer.py", "products/api-gateway-patterns/source/aws/usage-plans.json", "products/api-gateway-patterns/source/configs/gateway-dev.json", "products/api-gateway-patterns/source/configs/gateway-prod.json", "products/api-gateway-patterns/source/guide/auth-patterns.md", "products/api-gateway-patterns/source/guide/gateway-comparison.md", "products/api-gateway-patterns/source/guide/rate-limiting-strategies.md", "products/api-gateway-patterns/source/guides/auth-patterns.md", "products/api-gateway-patterns/source/guides/gateway-comparison.md", "products/api-gateway-patterns/source/guides/rate-limiting-strategies.md", "products/api-gateway-patterns/source/kong/jwt-auth.yaml", "products/api-gateway-patterns/source/kong/kong.yaml", "products/api-gateway-patterns/source/kong/rate-limiting.yaml", "products/api-gateway-patterns/source/kong/request-transformer.yaml", "products/api-gateway-patterns/source/src/__init__.py", "products/api-gateway-patterns/source/src/__main__.py", "products/api-gateway-patterns/source/src/auth_middleware.py", "products/api-gateway-patterns/source/src/gateway.py", "products/api-gateway-patterns/source/src/rate_limiter.py", "products/api-gateway-patterns/source/src/transform_middleware.py", "products/api-gateway-patterns/source/tests/test_rate_limiter.py", "products/api-testing-automation/README.md", "products/api-testing-automation/source/README.md", "products/api-testing-automation/source/guide/ci-integration.md", "products/api-testing-automation/source/guide/load-testing-guide.md", "products/api-testing-automation/source/guide/testing-strategy.md", "products/api-testing-automation/source/guides/ci-integration.md", "products/api-testing-automation/source/guides/load-testing-guide.md", "products/api-testing-automation/source/guides/testing-strategy.md", "products/api-testing-automation/source/k6/load-test.js", "products/api-testing-automation/source/k6/spike-test.js", "products/api-testing-automation/source/k6/stress-test.js", "products/api-testing-automation/source/newman/ci-run.sh", "products/api-testing-automation/source/newman/github-actions.yml", "products/api-testing-automation/source/pact/consumer-test.js", "products/api-testing-automation/source/pact/pacts/sample-pact.json", "products/api-testing-automation/source/pact/provider-verify.js", "products/api-testing-automation/source/postman/collection.json", "products/api-testing-automation/source/postman/environment.json", "products/api-testing-automation/source/src/contract_check.py", "products/api-testing-automation/source/src/smoke_test.py", "products/graphql-starter-kit/README.md", "products/graphql-starter-kit/source/README.md", "products/graphql-starter-kit/source/examples/curl-examples.sh", "products/graphql-starter-kit/source/examples/queries.graphql", "products/graphql-starter-kit/source/examples/variables.json", "products/graphql-starter-kit/source/guide/auth-and-context.md", "products/graphql-starter-kit/source/guide/n+1-and-dataloader.md", "products/graphql-starter-kit/source/guide/resolver-patterns.md", "products/graphql-starter-kit/source/guide/schema-design.md", "products/graphql-starter-kit/source/guides/auth-and-context.md", "products/graphql-starter-kit/source/guides/n+1-and-dataloader.md", "products/graphql-starter-kit/source/guides/resolver-patterns.md", "products/graphql-starter-kit/source/guides/schema-design.md", "products/graphql-starter-kit/source/schema/schema.graphql", "products/graphql-starter-kit/source/src/__init__.py", "products/graphql-starter-kit/source/src/__main__.py", "products/graphql-starter-kit/source/src/auth.py", "products/graphql-starter-kit/source/src/dataloader.py" ], "file_count": 80, "download_size_bytes": 841317, "download_size_human": "821.6 KB", "product_url": "https://datanest-stores.com/api-developer/#product-api-complete-bundle", "preview_url": null, "demo_url": null }, { "product_id": "api-launchpad:api-docs-generator", "slug": "api-docs-generator", "store": "api-launchpad", "store_name": "API Launchpad", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "api", "api design", "api testing", "dev", "graphql", "openapi", "rest", "webhook" ], "title": "API Docs Generator", "short_description": "Generate beautiful static HTML documentation from OpenAPI specs with dark theme and search.", "detailed_description": "Generate beautiful static HTML documentation from OpenAPI specs. Single-file output with dark theme, search, and responsive design. Zero dependencies. Part of the API Launchpad toolkit by CodeVault. Includes: Single-file HTML output \u2014 host anywhere (S3, GitHub Pages, Netlify, Vercel); Dark theme with syntax-highlighted code blocks and amber accents; Sidebar navigation with tag-based endpoint grouping; Live search across endpoint names, paths, and descriptions; Request/response examples auto-generated from schema definitions; Parameter tables with type, location, required status, and description.", "price_usd": 10, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Generate docs from an OpenAPI spec\npython3 src/main.py --spec openapi.json --output docs.html\n\n# Generate demo docs\npython3 src/main.py --demo --output docs.html\n\n# Print HTML to stdout\npython3 src/main.py --demo", "from main import generate_docs\nimport json\n\n# Load your OpenAPI spec\nwith open(\"openapi.json\") as f:\n spec = json.load(f)\n\n# Generate HTML documentation\nhtml = generate_docs(spec, custom_css=\"\"\"\n /* Override brand color */\n .logo { color: #00FF88; }\n\"\"\")\n\n# Save to file\n\u2026" ], "included_files": [ "README.md", "examples/sample_spec.json", "guide/01_api-docs-generator.md", "guide/02_features.md", "guide/03_quick-start.md", "src/main.py" ], "file_count": 6, "download_size_bytes": 40449, "download_size_human": "39.5 KB", "product_url": "https://datanest-stores.com/api-launchpad/#product-api-docs-generator", "preview_url": "https://datanest-stores.com/api-launchpad/preview-api-docs-generator", "demo_url": "https://datanest-stores.com/api-launchpad/demo-api-docs-generator/" }, { "product_id": "api-launchpad:api-key-manager", "slug": "api-key-manager", "store": "api-launchpad", "store_name": "API Launchpad", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "api", "api design", "api testing", "dev", "graphql", "openapi", "rest", "webhook" ], "title": "API Key Manager", "short_description": "Generate, validate, and rotate API keys with scoping, expiration, and grace periods.", "detailed_description": "Generate, validate, and rotate API keys with scoping, expiration, and grace periods. Zero dependencies. Part of the API Launchpad toolkit by CodeVault. Includes: Cryptographically secure key generation with secrets; Custom prefixes; HMAC-SHA256 validation with timing-safe comparison (never stores plaintext); Scope-based permissions; Key expiration with configurable TTL (default; Key rotation with grace periods \u2014 old key still works during transition.", "price_usd": 9, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "key.has_scope(\"read\") # True if scopes include \"read\" or \"admin\"\nkey.has_scope(\"read:users\") # True if scopes include \"read\", \"read:*\", \"read:users\", or \"admin\"", "api-key-manager/\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 src/\n\u2502 \u2514\u2500\u2500 main.py # Key manager + CLI + demo server (~280 lines)\n\u2514\u2500\u2500 examples/\n \u2514\u2500\u2500 config.example.json" ], "included_files": [ "README.md", "examples/config.example.json", "guide/01_api-key-manager.md", "guide/02_features.md", "guide/03_quick-start.md", "src/main.py" ], "file_count": 6, "download_size_bytes": 38872, "download_size_human": "38.0 KB", "product_url": "https://datanest-stores.com/api-launchpad/#product-api-key-manager", "preview_url": "https://datanest-stores.com/api-launchpad/preview-api-key-manager", "demo_url": "https://datanest-stores.com/api-launchpad/demo-api-key-manager/" }, { "product_id": "api-launchpad:api-launchpad-pro-bundle", "slug": "api-launchpad-pro-bundle", "store": "api-launchpad", "store_name": "API Launchpad", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "api", "api design", "api testing", "bundle", "dev", "graphql", "openapi", "rest", "webhook" ], "title": "API Launchpad Pro Bundle", "short_description": "All 10 API tools \u2014 $95 bought separately, yours for $40. Save $55 (58%): REST, GraphQL, webhooks, rate limiting, and testing.", "detailed_description": "The complete API engineering toolkit. All 10 products, one price. Save 43%. Part of the API Launchpad store by CodeVault \u2014 Ship faster. Build smarter.", "price_usd": 40, "currency": "usd", "is_bundle": true, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [ "Python 3.10+", "No pip packages required" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [ "Build Protect Document Test & Ship\n\u2500\u2500\u2500\u2500\u2500 \u2500\u2500\u2500\u2500\u2500\u2500\u2500 \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nREST Starter Rate Limiter OpenAPI Gen Test Framework\nGraphQL BP API Key Mgr Docs Generator Mock Server\nWebhook Hdlr Versioning TK " ], "included_files": [ "README.md" ], "file_count": 1, "download_size_bytes": 245898, "download_size_human": "240.1 KB", "product_url": "https://datanest-stores.com/api-launchpad/#product-api-launchpad-pro-bundle", "preview_url": "https://datanest-stores.com/api-launchpad/preview-api-launchpad-pro-bundle", "demo_url": null }, { "product_id": "api-launchpad:api-launchpad-starter-bundle", "slug": "api-launchpad-starter-bundle", "store": "api-launchpad", "store_name": "API Launchpad", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "api", "api design", "api testing", "bundle", "dev", "graphql", "openapi", "rest", "webhook" ], "title": "API Launchpad Starter Bundle", "short_description": "Everything you need to launch and protect your first API. 4 essential tools at 20% off.", "detailed_description": "Everything you need to launch and protect your first API. Four essential tools at 20% off. Part of the API Launchpad store by CodeVault \u2014 Ship faster. Build smarter.", "price_usd": 20, "currency": "usd", "is_bundle": true, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [ "Python 3.10+", "No pip packages required" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [ "api-launchpad-starter-bundle/\n\u251c\u2500\u2500 rest-api-starter/\n\u2502 \u251c\u2500\u2500 README.md\n\u2502 \u251c\u2500\u2500 LICENSE\n\u2502 \u251c\u2500\u2500 src/main.py # Start here \u2014 run: python3 src/main.py\n\u2502 \u2514\u2500\u2500 examples/\n\u251c\u2500\u2500 webhook-handler/\n\u2502 \u251c\u2500\u2500 README.md\n\u2502 \u251c\u2500\u2500 LICENSE\n\u2502 \u251c\u2500\u2500 src/main.py\n\u2502 \u2514\u2500\u2500 examples/\n\u251c\u2500\u2500 rate-limiter/\n\u2502 \u251c\u2500\u2500 README.md\n\u2502 \u251c\u2500\u2500 LICENSE\n\u2026", "cd rest-api-starter\npython3 src/main.py\n# Server running at http://localhost:8000" ], "included_files": [ "README.md" ], "file_count": 1, "download_size_bytes": 245898, "download_size_human": "240.1 KB", "product_url": "https://datanest-stores.com/api-launchpad/#product-api-launchpad-starter-bundle", "preview_url": "https://datanest-stores.com/api-launchpad/preview-api-launchpad-starter-bundle", "demo_url": null }, { "product_id": "api-launchpad:api-test-framework", "slug": "api-test-framework", "store": "api-launchpad", "store_name": "API Launchpad", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "api", "api design", "api testing", "dev", "graphql", "openapi", "rest", "webhook" ], "title": "API Test Framework", "short_description": "Fluent API testing with chainable assertions, performance benchmarks, and HTML reports.", "detailed_description": "Fluent API testing with chainable assertions, performance benchmarks, and HTML reports. Zero dependencies. Part of the API Launchpad toolkit by CodeVault. Includes: Fluent request builder; Chainable assertions; Test suite runner with setup/teardown hooks; Performance benchmarking; HTML report generation with pass/fail summary and timing data; JSON path extraction.", "price_usd": 10, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Run demo tests against a local server\npython3 src/main.py --demo --base-url http://localhost:8000\n\n# Benchmark an endpoint\npython3 src/main.py --benchmark http://localhost:8000/users --iterations 50\n\n# Generate an HTML report\npython3 src/main.py --demo --base-url http://localhost:8000 --report report.html", "from main import RequestBuilder, TestSuite\n\nsuite = TestSuite(\"My API Tests\", base_url=\"http://localhost:8000\")\n\n@suite.test\ndef test_list_users():\n return (RequestBuilder()\n .get(\"/users\")\n .expect_status(200)\n .expect_header_exists(\"content-type\")\n .expect_json_path(\"$.count\", lambda v: v is not None)\n .expect_max_time(500)\n .named(\"List Users\"))\n\n\u2026" ], "included_files": [ "README.md", "examples/test_suite.example.py", "guide/01_api-test-framework.md", "guide/02_features.md", "guide/03_quick-start.md", "src/main.py" ], "file_count": 6, "download_size_bytes": 39704, "download_size_human": "38.8 KB", "product_url": "https://datanest-stores.com/api-launchpad/#product-api-test-framework", "preview_url": "https://datanest-stores.com/api-launchpad/preview-api-test-framework", "demo_url": "https://datanest-stores.com/api-launchpad/demo-api-test-framework/" }, { "product_id": "api-launchpad:api-versioning-toolkit", "slug": "api-versioning-toolkit", "store": "api-launchpad", "store_name": "API Launchpad", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "api", "api design", "api testing", "dev", "graphql", "openapi", "rest", "webhook" ], "title": "API Versioning Toolkit", "short_description": "Three versioning strategies with deprecation headers, version registry, and migration helpers.", "detailed_description": "Three versioning strategies (URL path, header, query param) with deprecation headers and version registry. Zero dependencies. Part of the API Launchpad toolkit by CodeVault. Includes: Three strategies; Version registry; Deprecation headers; Version middleware; Versioned router; Demo server with 3 API versions showing real migration patterns.", "price_usd": 9, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Start with URL path versioning\npython3 src/main.py --strategy url --port 8000\n\n# Test different versions\ncurl http://localhost:8000/v1/users # Version 1 (deprecated)\ncurl http://localhost:8000/v2/users # Version 2\ncurl http://localhost:8000/v3/users # Version 3 (latest)\n\n# Start with header versioning\npython3 src/main.py --strategy header\ncurl -H \"X-API-Version: 2\" http://localhost:8000/users\n\n# Start with query param versioning\npython3 src/main.py --strategy query\n\u2026", "from main import VersionRegistry, URLPathStrategy, VersionMiddleware, VersionedRouter\n\n# 1. Set up version registry\nregistry = VersionRegistry(default_version=2)\nregistry.register(1, status=\"deprecated\",\n deprecation_date=\"2025-06-01\",\n sunset_date=\"2026-06-01\")\nregistry.register(2, status=\"active\")\nregistry.register(3, status=\"active\")\n\n# 2. Choose a strategy\nstrategy = URLPathStrategy(prefix=\"v\")\n\n# 3. Create middleware\n\u2026" ], "included_files": [ "README.md", "examples/config.example.json", "guide/01_api-versioning-toolkit.md", "guide/02_features.md", "guide/03_quick-start.md", "src/main.py" ], "file_count": 6, "download_size_bytes": 38004, "download_size_human": "37.1 KB", "product_url": "https://datanest-stores.com/api-launchpad/#product-api-versioning-toolkit", "preview_url": "https://datanest-stores.com/api-launchpad/preview-api-versioning-toolkit", "demo_url": "https://datanest-stores.com/api-launchpad/demo-api-versioning-toolkit/" }, { "product_id": "api-launchpad:graphql-boilerplate", "slug": "graphql-boilerplate", "store": "api-launchpad", "store_name": "API Launchpad", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "api", "api design", "api testing", "dev", "graphql", "openapi", "rest", "webhook" ], "title": "GraphQL Boilerplate", "short_description": "Lightweight GraphQL server built on Python's standard library, zero dependencies.", "detailed_description": "Lightweight GraphQL server built on Python's standard library. Zero dependencies. Part of the API Launchpad toolkit by CodeVault. Includes: Schema definition with Python dataclasses \u2014 no SDL files needed; Field resolvers with argument passing and context support; Query parsing for queries, mutations, and nested field selections; Introspection support \u2014 schema and type queries for tooling compatibility; Built-in GraphQL Explorer \u2014 HTML-based query editor served at /; CORS enabled \u2014 ready for frontend integration out of the box.", "price_usd": 10, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Start the server\npython3 src/main.py\n\n# Open the GraphQL Explorer in your browser\nopen http://localhost:4000/\n\n# Or use curl\ncurl -X POST http://localhost:4000/graphql \\\n -H 'Content-Type: application/json' \\\n -d '{\"query\": \"{ users { id name email } }\"}'", "from main import schema, FieldDef\n\nschema.register_type(\"Product\", [\n FieldDef(name=\"id\", type_name=\"ID\"),\n FieldDef(name=\"name\", type_name=\"String\"),\n FieldDef(name=\"price\", type_name=\"Float\"),\n FieldDef(name=\"in_stock\", type_name=\"Boolean\"),\n], description=\"A product in the catalog\")" ], "included_files": [ "README.md", "examples/queries.example.json", "guide/01_graphql-boilerplate.md", "guide/02_features.md", "guide/03_quick-start.md", "src/main.py" ], "file_count": 6, "download_size_bytes": 38826, "download_size_human": "37.9 KB", "product_url": "https://datanest-stores.com/api-launchpad/#product-graphql-boilerplate", "preview_url": "https://datanest-stores.com/api-launchpad/preview-graphql-boilerplate", "demo_url": "https://datanest-stores.com/api-launchpad/demo-graphql-boilerplate/" }, { "product_id": "api-launchpad:mock-server-builder", "slug": "mock-server-builder", "store": "api-launchpad", "store_name": "API Launchpad", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "api", "api design", "api testing", "dev", "graphql", "openapi", "rest", "webhook" ], "title": "Mock Server Builder", "short_description": "Create mock API servers from OpenAPI specs for testing and development.", "detailed_description": "Create mock API servers from OpenAPI specs for testing and development. Zero dependencies. Part of the API Launchpad toolkit by CodeVault. Includes: OpenAPI 3; Fake data generation; Path parameter matching; Hot-reload; Endpoint index; Spec serving.", "price_usd": 9, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Start with built-in demo spec\npython3 src/main.py --demo\n\n# Start from your own OpenAPI spec\npython3 src/main.py --spec openapi.json\n\n# Custom port\npython3 src/main.py --spec openapi.json --port 3000", "# List all mock endpoints\ncurl http://localhost:8080/\n\n# Hit a mock endpoint\ncurl http://localhost:8080/users\ncurl http://localhost:8080/users/abc123" ], "included_files": [ "README.md", "examples/sample_spec.json", "guide/01_mock-server-builder.md", "guide/02_features.md", "guide/03_quick-start.md", "src/main.py" ], "file_count": 6, "download_size_bytes": 37139, "download_size_human": "36.3 KB", "product_url": "https://datanest-stores.com/api-launchpad/#product-mock-server-builder", "preview_url": "https://datanest-stores.com/api-launchpad/preview-mock-server-builder", "demo_url": "https://datanest-stores.com/api-launchpad/demo-mock-server-builder/" }, { "product_id": "api-launchpad:openapi-spec-generator", "slug": "openapi-spec-generator", "store": "api-launchpad", "store_name": "API Launchpad", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "api", "api design", "api testing", "dev", "graphql", "openapi", "rest", "webhook" ], "title": "OpenAPI Spec Generator", "short_description": "Generate OpenAPI 3.0 specifications from Python function signatures and docstrings.", "detailed_description": "Generate OpenAPI 3.0 specifications from Python function signatures and docstrings. Zero dependencies. Part of the API Launchpad toolkit by CodeVault. Includes: Function introspection; Docstring parsing; Type mapping; Module scanning; Dual output; Request body inference.", "price_usd": 10, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Generate a demo spec\npython3 src/main.py --demo\n\n# Generate spec from a Python module\npython3 src/main.py --module my_api.py --title \"My API\" --output spec.json\n\n# Output as YAML\npython3 src/main.py --module my_api.py --format yaml --output spec.yaml", "from main import OpenAPIGenerator\n\ngen = OpenAPIGenerator(\n title=\"Acme Corp API\",\n version=\"2.0.0\",\n description=\"The Acme Corp public API\",\n server_url=\"https://api.docs.example.com/v2\",\n)\n\n# Register endpoints from your functions\ndef list_users(limit: int = 10, offset: int = 0) -> list:\n \"\"\"List all users with pagination.\n\n Args:\n\u2026" ], "included_files": [ "README.md", "examples/generated_spec.json", "examples/sample_api.py", "guide/01_openapi-spec-generator.md", "guide/02_features.md", "guide/03_quick-start.md", "src/main.py" ], "file_count": 7, "download_size_bytes": 41739, "download_size_human": "40.8 KB", "product_url": "https://datanest-stores.com/api-launchpad/#product-openapi-spec-generator", "preview_url": "https://datanest-stores.com/api-launchpad/preview-openapi-spec-generator", "demo_url": "https://datanest-stores.com/api-launchpad/demo-openapi-spec-generator/" }, { "product_id": "api-launchpad:rate-limiter", "slug": "rate-limiter", "store": "api-launchpad", "store_name": "API Launchpad", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "api", "api design", "api testing", "dev", "graphql", "openapi", "rest", "webhook" ], "title": "Rate Limiter", "short_description": "Four production-ready rate limiting algorithms in a single Python module, zero dependencies.", "detailed_description": "Four production-ready rate limiting algorithms in a single Python module. Zero dependencies. Part of the API Launchpad toolkit by CodeVault. Includes: Four algorithms; Three key extractors; HTTP middleware with X-RateLimit- response headers (IETF draft standard); Thread-safe with stdlib threading locks; Automatic cleanup of expired rate limit entries; Demo server included \u2014 see rate limiting in action.", "price_usd": 9, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Start demo server with Token Bucket (10 req/min)\npython3 src/main.py --algo token-bucket --limit 10 --window 60\n\n# Test rate limiting\nfor i in $(seq 1 15); do\n curl -s http://localhost:8000/ | python3 -m json.tool\ndone", "from main import TokenBucketLimiter, RateLimitMiddleware, ip_key_extractor\n\n# Create a limiter: 100 requests per 60 seconds\nlimiter = TokenBucketLimiter(capacity=100, refill_rate=100/60)\n\n# Check a request\nresult = limiter.check(\"192.168.1.1\")\nif result.allowed:\n print(f\"Allowed. {result.remaining} remaining.\")\nelse:\n print(f\"Rate limited. Retry after {result.retry_after:.1f}s\")\n\n# Add response headers\nfor key, value in result.headers().items():\n\u2026" ], "included_files": [ "README.md", "examples/config.example.json", "guide/01_rate-limiter.md", "guide/02_features.md", "guide/03_quick-start.md", "src/main.py" ], "file_count": 6, "download_size_bytes": 36698, "download_size_human": "35.8 KB", "product_url": "https://datanest-stores.com/api-launchpad/#product-rate-limiter", "preview_url": "https://datanest-stores.com/api-launchpad/preview-rate-limiter", "demo_url": "https://datanest-stores.com/api-launchpad/demo-rate-limiter/" }, { "product_id": "api-launchpad:rest-api-starter", "slug": "rest-api-starter", "store": "api-launchpad", "store_name": "API Launchpad", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "api", "api design", "api testing", "dev", "graphql", "openapi", "rest", "webhook" ], "title": "REST API Starter", "short_description": "Production-ready REST API server built on Python's standard library, zero dependencies.", "detailed_description": "Production-ready REST API server built on Python's standard library. Zero dependencies. Part of the API Launchpad toolkit by CodeVault. Includes: Declarative routing with path parameters (/users/{id}) and type coercion; Middleware pipeline; RFC 7807 error responses \u2014 structured JSON errors that clients can parse; Auto-generated OpenAPI 3; Request validation with JSON body parsing and type checking; Zero dependencies \u2014 runs on Python stdlib only.", "price_usd": 10, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Start the server\npython3 src/main.py\n\n# Test it\ncurl http://localhost:8000/items\ncurl http://localhost:8000/items/1\ncurl -X POST http://localhost:8000/items -d '{\"name\":\"New Item\",\"price\":5.99}'\ncurl http://localhost:8000/openapi.json", "from main import router, Response, Request\n\n@router.get(\"/users/{user_id}\", summary=\"Get user\", tags=[\"users\"])\ndef get_user(request: Request) -> Response:\n user_id = request.path_params[\"user_id\"]\n return Response(body={\"id\": user_id, \"name\": \"Acme User\"})\n\n@router.post(\"/users\", summary=\"Create user\", tags=[\"users\"])\ndef create_user(request: Request) -> Response:\n data = request.json()\n return Response(status=201, body={\"id\": \"4\", **data})" ], "included_files": [ "README.md", "examples/config.example.json", "guide/01_rest-api-starter.md", "guide/02_features.md", "guide/03_quick-start.md", "src/main.py" ], "file_count": 6, "download_size_bytes": 38190, "download_size_human": "37.3 KB", "product_url": "https://datanest-stores.com/api-launchpad/#product-rest-api-starter", "preview_url": "https://datanest-stores.com/api-launchpad/preview-rest-api-starter", "demo_url": "https://datanest-stores.com/api-launchpad/demo-rest-api-starter/" }, { "product_id": "api-launchpad:webhook-handler", "slug": "webhook-handler", "store": "api-launchpad", "store_name": "API Launchpad", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "api", "api design", "api testing", "dev", "graphql", "openapi", "rest", "webhook" ], "title": "Webhook Handler", "short_description": "Production-ready webhook receiver with signature verification, retry logic, and dead letter queue.", "detailed_description": "Production-ready webhook receiver with signature verification, retry logic, and dead letter queue. Zero dependencies. Part of the API Launchpad toolkit by CodeVault. Includes: HMAC-SHA256 signature verification with timing-safe comparison; Provider presets; Event routing; Retry with exponential backoff and configurable jitter; Dead letter queue; Idempotency.", "price_usd": 9, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "from main import webhook\n\n@webhook.router.on(\"payment.completed\")\ndef handle_payment(event_type: str, payload: dict) -> bool:\n print(f\"Payment received: ${payload['amount'] / 100:.2f}\")\n # Your logic: update DB, send email, etc.\n return True # Return True on success, False to retry\n\n@webhook.router.on(\"user.created\")\ndef handle_new_user(event_type: str, payload: dict) -> bool:\n print(f\"New user: {payload.get('email', 'unknown')}\")\n return True\n\n@webhook.router.default\n\u2026", "{\n \"event_type\": \"payment.completed\",\n \"payload\": { \"id\": \"pay_123\", \"amount\": 2999 },\n \"error\": \"All retry attempts failed\",\n \"timestamp\": \"2026-01-15T10:30:00+00:00\",\n \"attempts\": \"max_retries_exceeded\"\n}" ], "included_files": [ "README.md", "examples/config.example.json", "guide/01_webhook-handler.md", "guide/02_features.md", "guide/03_quick-start.md", "src/main.py" ], "file_count": 6, "download_size_bytes": 36846, "download_size_human": "36.0 KB", "product_url": "https://datanest-stores.com/api-launchpad/#product-webhook-handler", "preview_url": "https://datanest-stores.com/api-launchpad/preview-webhook-handler", "demo_url": "https://datanest-stores.com/api-launchpad/demo-webhook-handler/" }, { "product_id": "auth-vault:api-auth-middleware", "slug": "api-auth-middleware", "store": "auth-vault", "store_name": "Auth Vault", "domain": "security", "category": "Security & Compliance", "tags": [ "authentication", "identity", "jwt", "mfa", "oauth", "rbac", "security" ], "title": "API Auth Middleware", "short_description": "Multi-strategy API authentication for bearer tokens, API keys, and HMAC request signatures.", "detailed_description": "Multi-strategy API authentication middleware for bearer tokens, API keys, and HMAC request signatures. Zero dependencies. Part of the Auth Vault toolkit by CodeVault. Includes: Three auth strategies; Middleware chaining; Per-route configuration; HMAC-SHA256 request signing with replay protection (timestamp + nonce); Timing-safe comparison on all credential checks; RFC 7807 Problem Details error responses.", "price_usd": 9, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Generate a new API key\npython3 src/main.py generate --name \"My App\" --scopes read write\n\n# List registered API keys\npython3 src/main.py list\n\n# Validate an API key\npython3 src/main.py validate --key \"av_abc123...\"\n\n# Sign a request with HMAC\npython3 src/main.py sign --client demo-client --secret s3cret \\\n --method GET --path /api/data\n\n# Start demo server with protected routes\n\u2026", "api-auth-middleware/\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 src/\n\u2502 \u2514\u2500\u2500 main.py # Auth middleware + demo server + CLI (~480 lines)\n\u2514\u2500\u2500 examples/\n \u2514\u2500\u2500 config.example.json" ], "included_files": [ "README.md", "examples/config.example.json", "guide/01_api-auth-middleware.md", "guide/02_features.md", "guide/03_quick-start.md", "src/main.py" ], "file_count": 6, "download_size_bytes": 42297, "download_size_human": "41.3 KB", "product_url": "https://datanest-stores.com/auth-vault/#product-api-auth-middleware", "preview_url": "https://datanest-stores.com/auth-vault/preview-api-auth-middleware", "demo_url": "https://datanest-stores.com/auth-vault/demo-api-auth-middleware/" }, { "product_id": "auth-vault:auth-audit-logger", "slug": "auth-audit-logger", "store": "auth-vault", "store_name": "Auth Vault", "domain": "security", "category": "Security & Compliance", "tags": [ "authentication", "identity", "jwt", "mfa", "oauth", "rbac", "security" ], "title": "Auth Audit Logger", "short_description": "Authentication event logging with anomaly detection, account lockout, and structured JSON audit trails.", "detailed_description": "Authentication event logging with anomaly detection, account lockout, and structured JSON audit trails. Zero dependencies. Part of the Auth Vault toolkit by CodeVault. Includes: Structured JSON event logging with ISO 8601 timestamps; 17 event types; Rate-based anomaly detection; Account lockout after configurable failure threshold; IP-based tracking with suspicious pattern detection; Log rotation with configurable max file size and retention.", "price_usd": 9, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Log a successful login\npython3 src/main.py log --event login_success --user user123 --ip 192.168.1.1\n\n# Log a failed login\npython3 src/main.py log --event login_failure --user user456 --ip 10.0.0.1\n\n# Query recent events\npython3 src/main.py query --limit 20\n\n# Query failures for a specific user\npython3 src/main.py query --user user456 --event login_failure\n\n# Show activity summary\npython3 src/main.py summary\n\u2026", "from main import AuditLogger\n\naudit = AuditLogger(lockout_threshold=5, lockout_duration=900)\n\n# Log events\naudit.log_login_success(\"user123\", ip=\"192.168.1.1\")\nalerts, locked = audit.log_login_failure(\"user456\", ip=\"10.0.0.1\",\n reason=\"bad password\")\n\nif locked:\n print(\"Account is now locked!\")\n\nfor alert in alerts:\n print(f\"ALERT: {alert.description}\")\n\u2026" ], "included_files": [ "README.md", "examples/config.example.json", "guide/01_auth-audit-logger.md", "guide/02_features.md", "guide/03_quick-start.md", "src/main.py" ], "file_count": 6, "download_size_bytes": 44180, "download_size_human": "43.1 KB", "product_url": "https://datanest-stores.com/auth-vault/#product-auth-audit-logger", "preview_url": "https://datanest-stores.com/auth-vault/preview-auth-audit-logger", "demo_url": "https://datanest-stores.com/auth-vault/demo-auth-audit-logger/" }, { "product_id": "auth-vault:auth-vault-pro-bundle", "slug": "auth-vault-pro-bundle", "store": "auth-vault", "store_name": "Auth Vault", "domain": "security", "category": "Security & Compliance", "tags": [ "authentication", "bundle", "identity", "jwt", "mfa", "oauth", "rbac", "security" ], "title": "Auth Vault Pro Bundle", "short_description": "All 10 auth tools \u2014 $94 bought separately, yours for $40. Save $54 (57%): OAuth2, JWT, RBAC, 2FA, and session management.", "detailed_description": "All 10 auth tools \u2014 $94 bought separately, yours for $40. Save $54 (57%): OAuth2, JWT, RBAC, 2FA, and session management.", "price_usd": 40, "currency": "usd", "is_bundle": true, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [ "Password Hasher \u2192 Session Manager", "JWT Library \u2192 API Auth Middleware \u2192 Auth Audit Logger" ], "included_files": [ "README.md" ], "file_count": 1, "download_size_bytes": 243295, "download_size_human": "237.6 KB", "product_url": "https://datanest-stores.com/auth-vault/#product-auth-vault-pro-bundle", "preview_url": "https://datanest-stores.com/auth-vault/preview-auth-vault-pro-bundle", "demo_url": null }, { "product_id": "auth-vault:auth-vault-starter-bundle", "slug": "auth-vault-starter-bundle", "store": "auth-vault", "store_name": "Auth Vault", "domain": "security", "category": "Security & Compliance", "tags": [ "authentication", "bundle", "identity", "jwt", "mfa", "oauth", "rbac", "security" ], "title": "Auth Vault Starter Bundle", "short_description": "The essential authentication toolkit \u2014 4 core tools at a discount.", "detailed_description": "The essential authentication toolkit \u2014 4 core tools at a discount.", "price_usd": 20, "currency": "usd", "is_bundle": true, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md" ], "file_count": 1, "download_size_bytes": 243295, "download_size_human": "237.6 KB", "product_url": "https://datanest-stores.com/auth-vault/#product-auth-vault-starter-bundle", "preview_url": "https://datanest-stores.com/auth-vault/preview-auth-vault-starter-bundle", "demo_url": null }, { "product_id": "auth-vault:jwt-library", "slug": "jwt-library", "store": "auth-vault", "store_name": "Auth Vault", "domain": "security", "category": "Security & Compliance", "tags": [ "authentication", "identity", "jwt", "mfa", "oauth", "rbac", "security" ], "title": "JWT Library", "short_description": "Create and validate JSON Web Tokens with HS256, claims validation, expiration, and refresh.", "detailed_description": "Create and validate JSON Web Tokens with HS256, claims validation, expiration checking, and token refresh. Zero dependencies. Part of the Auth Vault toolkit by CodeVault. Includes: HS256 (HMAC-SHA256) signing and verification; Standard claims validation; Automatic expiration checking with configurable clock skew tolerance; Token refresh with rotation detection; Base64url encoding/decoding per RFC 7515; Custom claims support for application-specific data.", "price_usd": 9, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Create a JWT\npython3 src/main.py create --sub user123 --scopes read write --ttl 3600\n\n# Decode a token (without validation)\npython3 src/main.py decode --token eyJhbGciOiJIUzI1NiJ9...\n\n# Validate a token\npython3 src/main.py validate --token eyJhbGciOiJIUzI1NiJ9...\n\n# Refresh a token\npython3 src/main.py refresh --token eyJhbGciOiJIUzI1NiJ9...", "jwt-library/\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 src/\n\u2502 \u2514\u2500\u2500 main.py # JWT library + CLI (~420 lines)\n\u2514\u2500\u2500 examples/\n \u2514\u2500\u2500 config.example.json" ], "included_files": [ "README.md", "examples/config.example.json", "guide/01_jwt-library.md", "guide/02_features.md", "guide/03_quick-start.md", "src/main.py" ], "file_count": 6, "download_size_bytes": 34327, "download_size_human": "33.5 KB", "product_url": "https://datanest-stores.com/auth-vault/#product-jwt-library", "preview_url": "https://datanest-stores.com/auth-vault/preview-jwt-library", "demo_url": "https://datanest-stores.com/auth-vault/demo-jwt-library/" }, { "product_id": "auth-vault:magic-link-auth", "slug": "magic-link-auth", "store": "auth-vault", "store_name": "Auth Vault", "domain": "security", "category": "Security & Compliance", "tags": [ "authentication", "identity", "jwt", "mfa", "oauth", "rbac", "security" ], "title": "Magic Link Auth", "short_description": "Passwordless authentication via magic links with token generation, email templates, and rate limiting.", "detailed_description": "Passwordless authentication via magic links with token generation, email templates, rate limiting, and HTTP server. Zero dependencies. Part of the Auth Vault toolkit by CodeVault. Includes: Cryptographically secure tokens (URL-safe, single-use); Configurable expiration (default; Rate limiting; HTML email templates with customizable branding; Plain text fallback for email clients that strip HTML; Token verification with automatic cleanup of expired tokens.", "price_usd": 9, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Generate a magic link for an email\npython3 src/main.py generate --email user@docs.example.com\n\n# Verify a magic link token\npython3 src/main.py verify --token TOKEN_STRING\n\n# Send a magic link email (requires SMTP config)\npython3 src/main.py send --email user@docs.example.com\n\n# Start the demo HTTP server\npython3 src/main.py serve --port 8000", "from main import MagicLinkAuth\n\nauth = MagicLinkAuth(\n base_url=\"https://auth.docs.example.com\",\n token_ttl_seconds=900,\n max_requests_per_hour=5,\n)\n\n# Generate a magic link\ntoken, link = auth.create_link(\"user@docs.example.com\")\nprint(f\"Magic link: {link}\")\n\n# Generate email HTML\nhtml = auth.render_email(\n\u2026" ], "included_files": [ "README.md", "examples/config.example.json", "guide/01_magic-link-auth.md", "guide/02_features.md", "guide/03_quick-start.md", "src/main.py" ], "file_count": 6, "download_size_bytes": 37920, "download_size_human": "37.0 KB", "product_url": "https://datanest-stores.com/auth-vault/#product-magic-link-auth", "preview_url": "https://datanest-stores.com/auth-vault/preview-magic-link-auth", "demo_url": "https://datanest-stores.com/auth-vault/demo-magic-link-auth/" }, { "product_id": "auth-vault:oauth2-implementation", "slug": "oauth2-implementation", "store": "auth-vault", "store_name": "Auth Vault", "domain": "security", "category": "Security & Compliance", "tags": [ "authentication", "identity", "jwt", "mfa", "oauth", "rbac", "security" ], "title": "OAuth2 Implementation", "short_description": "Complete OAuth2 server/client with authorization code, PKCE, client credentials, and refresh flows.", "detailed_description": "Complete OAuth2 server/client with authorization code, PKCE, client credentials, and token refresh flows. Zero dependencies. Part of the Auth Vault toolkit by CodeVault. Includes: Authorization Code Grant with PKCE support (RFC 7636); Client Credentials Grant for machine-to-machine authentication; Token refresh with automatic rotation and revocation; CSRF protection via cryptographic state parameter; Scope-based authorization with consent flow; Configurable token expiration and signing (HMAC-SHA256).", "price_usd": 10, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Start the OAuth2 authorization server\npython3 src/main.py serve --port 8080\n\n# Register a new client\npython3 src/main.py register --name \"My App\" \\\n --redirect-uri https://app.docs.example.com/callback \\\n --scopes read write\n\n# Generate a PKCE challenge\npython3 src/main.py pkce\n\n# Exchange an authorization code for tokens\npython3 src/main.py exchange --code AUTH_CODE --verifier PKCE_VERIFIER\n\n\u2026", "oauth2-implementation/\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 src/\n\u2502 \u2514\u2500\u2500 main.py # OAuth2 server + client (~683 lines)\n\u2514\u2500\u2500 examples/\n \u2514\u2500\u2500 config.example.json" ], "included_files": [ "README.md", "examples/config.example.json", "guide/01_oauth2-implementation.md", "guide/02_features.md", "guide/03_quick-start.md", "src/main.py" ], "file_count": 6, "download_size_bytes": 42517, "download_size_human": "41.5 KB", "product_url": "https://datanest-stores.com/auth-vault/#product-oauth2-implementation", "preview_url": "https://datanest-stores.com/auth-vault/preview-oauth2-implementation", "demo_url": "https://datanest-stores.com/auth-vault/demo-oauth2-implementation/" }, { "product_id": "auth-vault:password-hasher", "slug": "password-hasher", "store": "auth-vault", "store_name": "Auth Vault", "domain": "security", "category": "Security & Compliance", "tags": [ "authentication", "identity", "jwt", "mfa", "oauth", "rbac", "security" ], "title": "Password Hasher", "short_description": "Secure password hashing with PBKDF2-SHA256, strength analysis, generation, and timing-safe verification.", "detailed_description": "Secure password hashing with PBKDF2-SHA256, strength analysis, password generation, and timing-safe verification. Zero dependencies. Part of the Auth Vault toolkit by CodeVault. Includes: PBKDF2-HMAC-SHA256 hashing with 600,000 iterations (OWASP 2024); Password strength analyzer; Common password detection via built-in blocklist; Timing-safe comparison to prevent timing attacks; Hash format versioning for algorithm migration; Secure password generator with configurable complexity.", "price_usd": 9, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Hash a password\npython3 src/main.py hash --password \"MyS3cur3Pass!\"\n\n# Verify a password against a hash\npython3 src/main.py verify --password \"MyS3cur3Pass!\" \\\n --hash '$pbkdf2-sha256$600000$...'\n\n# Check password strength\npython3 src/main.py strength --password \"test123\"\n\n# Generate a secure password\npython3 src/main.py generate --length 20", "from main import PasswordHasher, StrengthAnalyzer\n\nhasher = PasswordHasher(iterations=600000)\n\n# Hash a password\nhashed = hasher.hash(\"MyS3cur3Pass!\")\nprint(f\"Hash: {hashed}\")\n\n# Verify a password\nis_valid = hasher.verify(\"MyS3cur3Pass!\", hashed)\nprint(f\"Valid: {is_valid}\")\n\n# Check strength\nanalyzer = StrengthAnalyzer()\n\u2026" ], "included_files": [ "README.md", "examples/config.example.json", "guide/01_password-hasher.md", "guide/02_features.md", "guide/03_quick-start.md", "src/main.py" ], "file_count": 6, "download_size_bytes": 36724, "download_size_human": "35.9 KB", "product_url": "https://datanest-stores.com/auth-vault/#product-password-hasher", "preview_url": "https://datanest-stores.com/auth-vault/preview-password-hasher", "demo_url": "https://datanest-stores.com/auth-vault/demo-password-hasher/" }, { "product_id": "auth-vault:rbac-system", "slug": "rbac-system", "store": "auth-vault", "store_name": "Auth Vault", "domain": "security", "category": "Security & Compliance", "tags": [ "authentication", "identity", "jwt", "mfa", "oauth", "rbac", "security" ], "title": "RBAC System", "short_description": "Role-based access control with hierarchical roles, granular permissions, and deny-override policies.", "detailed_description": "Role-based access control with hierarchical roles, granular permissions, deny-override policies, and enforcement engine. Zero dependencies. Part of the Auth Vault toolkit by CodeVault. Includes: Hierarchical roles with inheritance (admin > editor > viewer); Granular permissions; Wildcard permissions; Deny-override; Resource-level policies with condition evaluation; JSON-based policy storage with thread-safe read/write.", "price_usd": 10, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Create roles and permissions\npython3 src/main.py create-role --name editor --inherits viewer \\\n --permissions \"posts:write\" \"posts:delete\" \"media:upload\"\n\n# Assign a role to a user\npython3 src/main.py assign --user user123 --role editor\n\n# Check a permission\npython3 src/main.py check --user user123 --permission posts:write\n\n# List all roles\npython3 src/main.py list-roles\n\n# List permissions for a role (including inherited)\n\u2026", "from main import RBACManager\n\nrbac = RBACManager()\n\n# Define roles\nrbac.create_role(\"viewer\", permissions=[\"posts:read\", \"comments:read\"])\nrbac.create_role(\"editor\", inherits=[\"viewer\"],\n permissions=[\"posts:write\", \"posts:delete\"])\nrbac.create_role(\"admin\", inherits=[\"editor\"], permissions=[\"*\"])\n\n# Assign roles\nrbac.assign_role(\"user123\", \"editor\")\n\n# Check permissions\n\u2026" ], "included_files": [ "README.md", "examples/config.example.json", "guide/01_rbac-system.md", "guide/02_features.md", "guide/03_quick-start.md", "src/main.py" ], "file_count": 6, "download_size_bytes": 36207, "download_size_human": "35.4 KB", "product_url": "https://datanest-stores.com/auth-vault/#product-rbac-system", "preview_url": "https://datanest-stores.com/auth-vault/preview-rbac-system", "demo_url": "https://datanest-stores.com/auth-vault/demo-rbac-system/" }, { "product_id": "auth-vault:session-manager", "slug": "session-manager", "store": "auth-vault", "store_name": "Auth Vault", "domain": "security", "category": "Security & Compliance", "tags": [ "authentication", "identity", "jwt", "mfa", "oauth", "rbac", "security" ], "title": "Session Manager", "short_description": "Secure session management with signed cookies, fingerprinting, sliding expiration, and CSRF protection.", "detailed_description": "Secure session management with signed cookies, fingerprinting, sliding expiration, concurrent limits, and CSRF protection. Zero dependencies. Part of the Auth Vault toolkit by CodeVault. Includes: HMAC-signed session cookies \u2014 tamper-proof, no client-side manipulation; In-memory store with Redis-compatible interface; Automatic session expiration with background cleanup; Sliding expiration; Session fingerprinting; Concurrent session limits.", "price_usd": 9, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Create a new session\npython3 src/main.py create --user user123 --ip 192.168.1.1\n\n# Validate a session\npython3 src/main.py validate --session-id abc123...\n\n# List active sessions for a user\npython3 src/main.py list --user user123\n\n# Destroy a session\npython3 src/main.py destroy --session-id abc123...\n\n# Start demo HTTP server with session middleware\npython3 src/main.py serve --port 8000", "session-manager/\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 src/\n\u2502 \u2514\u2500\u2500 main.py # Session manager + CLI (~478 lines)\n\u2514\u2500\u2500 examples/\n \u2514\u2500\u2500 config.example.json" ], "included_files": [ "README.md", "examples/config.example.json", "guide/01_session-manager.md", "guide/02_features.md", "guide/03_quick-start.md", "src/main.py" ], "file_count": 6, "download_size_bytes": 35737, "download_size_human": "34.9 KB", "product_url": "https://datanest-stores.com/auth-vault/#product-session-manager", "preview_url": "https://datanest-stores.com/auth-vault/preview-session-manager", "demo_url": "https://datanest-stores.com/auth-vault/demo-session-manager/" }, { "product_id": "auth-vault:social-login-integrator", "slug": "social-login-integrator", "store": "auth-vault", "store_name": "Auth Vault", "domain": "security", "category": "Security & Compliance", "tags": [ "authentication", "identity", "jwt", "mfa", "oauth", "rbac", "security" ], "title": "Social Login Integrator", "short_description": "Google, GitHub, and Discord OAuth2 flows with unified user profile normalization.", "detailed_description": "Google, GitHub, and Discord OAuth2 flows with unified user profile normalization. Zero dependencies. Part of the Auth Vault toolkit by CodeVault. Includes: Three providers; OAuth2 authorization code flow with PKCE option; CSRF protection via cryptographic state parameter; Unified user profile; Token exchange with error handling and retry; Provider-agnostic callback handler.", "price_usd": 10, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Generate an authorization URL for Google\npython3 src/main.py authorize --provider google \\\n --client-id YOUR_GOOGLE_CLIENT_ID \\\n --redirect-uri https://app.docs.example.com/auth/google/callback\n\n# Exchange a callback code for a user profile\npython3 src/main.py callback --provider google \\\n --code AUTH_CODE \\\n --client-id YOUR_GOOGLE_CLIENT_ID \\\n --client-secret YOUR_GOOGLE_SECRET\n\n# Start the demo callback server\npython3 src/main.py serve --port 8000", "social-login-integrator/\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 src/\n\u2502 \u2514\u2500\u2500 main.py # Social login handler + CLI (~586 lines)\n\u2514\u2500\u2500 examples/\n \u2514\u2500\u2500 config.example.json" ], "included_files": [ "README.md", "examples/config.example.json", "guide/01_social-login-integrator.md", "guide/02_features.md", "guide/03_quick-start.md", "src/main.py" ], "file_count": 6, "download_size_bytes": 37724, "download_size_human": "36.8 KB", "product_url": "https://datanest-stores.com/auth-vault/#product-social-login-integrator", "preview_url": "https://datanest-stores.com/auth-vault/preview-social-login-integrator", "demo_url": "https://datanest-stores.com/auth-vault/demo-social-login-integrator/" }, { "product_id": "auth-vault:two-factor-auth", "slug": "two-factor-auth", "store": "auth-vault", "store_name": "Auth Vault", "domain": "security", "category": "Security & Compliance", "tags": [ "authentication", "identity", "jwt", "mfa", "oauth", "rbac", "security" ], "title": "Two-Factor Auth", "short_description": "TOTP (RFC 6238), backup codes, QR generation for authenticator apps, and recovery flows.", "detailed_description": "TOTP (RFC 6238), backup codes, QR code generation for authenticator apps, and recovery flows. Zero dependencies. Part of the Auth Vault toolkit by CodeVault. Includes: TOTP generation per RFC 6238 (30-second intervals, 6-digit codes); QR code generation as SVG \u2014 no image library dependencies; Backup codes; Compatible with Google Authenticator, Authy, and 1Password; Configurable time window tolerance for clock drift; Recovery flow with backup code verification.", "price_usd": 10, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "two-factor-auth/\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 src/\n\u2502 \u2514\u2500\u2500 main.py # 2FA engine + QR + CLI (~515 lines)\n\u2514\u2500\u2500 examples/\n \u2514\u2500\u2500 config.example.json" ], "included_files": [ "README.md", "examples/config.example.json", "guide/01_two-factor-auth.md", "guide/02_features.md", "guide/03_quick-start.md", "src/main.py" ], "file_count": 6, "download_size_bytes": 36636, "download_size_human": "35.8 KB", "product_url": "https://datanest-stores.com/auth-vault/#product-two-factor-auth", "preview_url": "https://datanest-stores.com/auth-vault/preview-two-factor-auth", "demo_url": "https://datanest-stores.com/auth-vault/demo-two-factor-auth/" }, { "product_id": "automation-hub:api-poller", "slug": "api-poller", "store": "automation-hub", "store_name": "Automation Hub", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "automation", "cloud", "cron jobs", "etl", "pipelines", "scripting", "workflows" ], "title": "API Poller", "short_description": "Scheduled API polling tool that checks endpoints, detects changes, and sends notifications.", "detailed_description": "A scheduled API polling tool that checks HTTP endpoints at configurable intervals, detects changes in responses, sends alerts when differences are found, and archives response data to SQLite for historical comparison. Includes: Scheduled polling \u2014 Check endpoints at configurable intervals (seconds to hours); Change detection \u2014 Content hashing detects when API responses change; Alert webhooks \u2014 Send notifications when changes or errors are detected; Response archiving \u2014 Store every response in SQLite for historical analysis; Multi-endpoint support \u2014 Poll many endpoints with independent schedules; One-shot mode \u2014 Poll once and exit (great for cron integration).", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "{\n \"endpoints\": [\n {\n \"name\": \"status-page\",\n \"url\": \"https://api.docs.example.com/v1/status\",\n \"interval_seconds\": 60,\n \"alert_webhook\": \"https://hooks.docs.example.com/alert\",\n \"headers\": {\"Authorization\": \"Bearer YOUR_API_TOKEN_HERE\"},\n \"expected_status\": 200\n },\n {\n \"name\": \"pricing-api\",\n \"url\": \"https://api.docs.example.com/v1/pricing\",\n \"interval_seconds\": 3600,\n\u2026" ], "included_files": [ "README.md", "examples/poller_config.json", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_faq.md", "src/api_poller.py" ], "file_count": 6, "download_size_bytes": 39712, "download_size_human": "38.8 KB", "product_url": "https://datanest-stores.com/automation-hub/#product-api-poller", "preview_url": "https://datanest-stores.com/automation-hub/preview-api-poller", "demo_url": "https://datanest-stores.com/automation-hub/demo-api-poller/" }, { "product_id": "automation-hub:automation-hub-pro-bundle", "slug": "automation-hub-pro-bundle", "store": "automation-hub", "store_name": "Automation Hub", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "automation", "bundle", "cloud", "cron jobs", "etl", "pipelines", "scripting", "workflows" ], "title": "Automation Hub Pro Bundle", "short_description": "All 13 automation tools \u2014 $287 bought separately, yours for $59. Save $228 (79%): scraping, scheduling, ETL, and workflow automation.", "detailed_description": "The complete automation infrastructure toolkit. All 10 products from the Automation Hub at a massive discount \u2014 everything you need to automate scraping, scheduling, ETL, notifications, file watching, and API monitoring.", "price_usd": 59, "currency": "usd", "is_bundle": true, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [ "automation-hub-pro-bundle/\n\u251c\u2500\u2500 api-poller/\n\u251c\u2500\u2500 batch-processor/\n\u251c\u2500\u2500 config-manager/\n\u251c\u2500\u2500 data-sync/\n\u251c\u2500\u2500 etl-pipeline/\n\u251c\u2500\u2500 file-watcher/\n\u251c\u2500\u2500 notification-dispatcher/\n\u251c\u2500\u2500 report-generator/\n\u251c\u2500\u2500 task-scheduler/\n\u251c\u2500\u2500 web-scraper/\n\u251c\u2500\u2500 webhook-router/\n\u251c\u2500\u2500 webhook-trigger/\n\u2514\u2500\u2500 workflow-engine/" ], "included_files": [ "README.md" ], "file_count": 1, "download_size_bytes": 392201, "download_size_human": "383.0 KB", "product_url": "https://datanest-stores.com/automation-hub/#product-automation-hub-pro-bundle", "preview_url": null, "demo_url": null }, { "product_id": "automation-hub:batch-processor", "slug": "batch-processor", "store": "automation-hub", "store_name": "Automation Hub", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "automation", "cloud", "cron jobs", "etl", "pipelines", "scripting", "workflows" ], "title": "Batch Processor", "short_description": "Configurable batch-job processing framework: reads work items, runs a transform pipeline in batches across a concurrent worker pool, with retry, backoff, and a dead-letter queue.", "detailed_description": "A configurable batch-job processing framework built on Python stdlib. Reads work items from an input source, runs them through a transform pipeline in configurable-size batches across a concurrent worker pool, with retry + exponential backoff and a dead-letter queue for permanently failed items. Includes: Concurrent worker pool \u2014 ThreadPoolExecutor with a configurable maxworkers limit; Configurable batch sizes \u2014 Split large item sets into fixed-size batches for controlled throughput; Retry with exponential backoff \u2014 Failed items are retried up to N times with increasing delays; Dead-letter queue \u2014 Items that exhaust all retries are captured with full error context for inspection; Transform pipeline \u2014 Chain cast, validate, map, rename, and enrich transforms in any order; Multiple input sources \u2014 Inline items in config, JSON files on disk, or generated demo data.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "{\n \"submitted\": 8,\n \"succeeded\": 6,\n \"failed\": 2,\n \"dead_lettered\": 2,\n \"total_retries\": 4,\n \"batches_processed\": 3,\n \"duration_seconds\": 0.412,\n \"errors\": [\n \"[ORD-1003] ValueError: Validation failed: amount=-5.0 (expected gt 0)\",\n \"[ORD-1006] ValueError: Validation failed: amount=0.0 (expected gt 0)\"\n ],\n \"dead_letter_queue\": [\n {\n\u2026" ], "included_files": [ "README.md", "examples/batch_processor_config.json", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_output.md", "guide/04_license.md", "src/batch_processor.py" ], "file_count": 7, "download_size_bytes": 31630, "download_size_human": "30.9 KB", "product_url": "https://datanest-stores.com/automation-hub/#product-batch-processor", "preview_url": "https://datanest-stores.com/automation-hub/preview-batch-processor", "demo_url": "https://datanest-stores.com/automation-hub/demo-batch-processor/" }, { "product_id": "automation-hub:config-manager", "slug": "config-manager", "store": "automation-hub", "store_name": "Automation Hub", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "automation", "cloud", "cron jobs", "etl", "pipelines", "scripting", "workflows" ], "title": "Config Manager", "short_description": "Layered configuration manager: merge defaults, files, env vars, and CLI overrides in precedence order with dot-path access, schema validation, secret masking, and diffing.", "detailed_description": "A layered configuration management library built on Python stdlib. Merge configuration from multiple sources in precedence order \u2014 built-in defaults, config files, environment variables, and CLI overrides \u2014 with dot-path access, schema validation, secret masking, and config diffing. Includes: Layered merge \u2014 Defaults < config file < environment variables < CLI overrides, with deep recursive merging; Multiple file formats \u2014 JSON, INI, and; Dot-path access \u2014 Get and set nested values with database; Schema validation \u2014 Validate types, required keys, allowed values, numeric ranges, and regex patterns; Type coercion \u2014 Automatic and explicit conversion between str, int, float, bool, and list types; Secret masking \u2014 Export configs with passwords, tokens, and API keys automatically redacted.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "{\n \"database\": {\n \"host\": \"db.docs.example.com\",\n \"port\": 5432,\n \"name\": \"app_production\",\n \"password\": \"s3cret_passw0rd\"\n },\n \"logging\": {\n \"level\": \"INFO\",\n \"format\": \"json\"\n }\n}", "{\n \"properties\": {\n \"database\": {\n \"type\": \"object\",\n \"required\": true,\n \"properties\": {\n \"host\": {\"type\": \"string\", \"required\": true},\n \"port\": {\"type\": \"integer\", \"min\": 1, \"max\": 65535}\n }\n }\n }\n}" ], "included_files": [ "README.md", "examples/config_manager_config.json", "examples/schema.json", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_output.md", "guide/04_license.md", "src/config_manager.py" ], "file_count": 8, "download_size_bytes": 32589, "download_size_human": "31.8 KB", "product_url": "https://datanest-stores.com/automation-hub/#product-config-manager", "preview_url": "https://datanest-stores.com/automation-hub/preview-config-manager", "demo_url": "https://datanest-stores.com/automation-hub/demo-config-manager/" }, { "product_id": "automation-hub:data-sync", "slug": "data-sync", "store": "automation-hub", "store_name": "Automation Hub", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "automation", "cloud", "cron jobs", "etl", "pipelines", "scripting", "workflows" ], "title": "Data Sync", "short_description": "Record synchronization between JSON, CSV, or SQLite endpoints with content-hash diffing, incremental sync, conflict resolution, dry-run, and bidirectional mode.", "detailed_description": "A record-synchronization tool that syncs datasets between two endpoints (JSON file, CSV file, or SQLite table). Keys records by a configurable field, computes a content hash per record, and derives a diff (added, changed, deleted, unchanged). Supports incremental sync, conflict resolution, dry-run mode, and bidirectional sync. Includes: Hash-based diff engine \u2014 SHA-256 content hashing detects added, changed, deleted, and unchanged records; Multi-format endpoints \u2014 Read and write JSON files, CSV files, and SQLite tables; Conflict resolution \u2014 Three strategies; Incremental sync \u2014 Persist state between runs to detect true conflicts (both sides changed); Dry-run mode \u2014 Preview the sync plan without writing any changes; Bidirectional sync \u2014 Propagate dest-only records back to source instead of deleting them.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "{\n \"source\": \"customers_export.json\",\n \"dest\": \"customers_master.json\",\n \"key\": \"customer_id\",\n \"strategy\": \"newest-wins\",\n \"timestamp_field\": \"updated_at\",\n \"state\": \"sync_state.json\",\n \"dry_run\": false,\n \"bidirectional\": false,\n \"source_table\": \"customers\",\n \"dest_table\": \"customers\"\n}", "============================================================\n DATA SYNC \u2014 DIFF REPORT\n============================================================\n\n ADDED (2 records)\n ----------------------------------------\n + [1004]\n customer_id: 1004\n name: New Customer\n email: new@docs.example.com\n + [1005]\n\n CHANGED (1 records)\n ----------------------------------------\n\u2026" ], "included_files": [ "README.md", "examples/data_sync_config.json", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_output.md", "guide/04_license.md", "src/data_sync.py" ], "file_count": 7, "download_size_bytes": 30617, "download_size_human": "29.9 KB", "product_url": "https://datanest-stores.com/automation-hub/#product-data-sync", "preview_url": "https://datanest-stores.com/automation-hub/preview-data-sync", "demo_url": "https://datanest-stores.com/automation-hub/demo-data-sync/" }, { "product_id": "automation-hub:etl-pipeline", "slug": "etl-pipeline", "store": "automation-hub", "store_name": "Automation Hub", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "automation", "cloud", "cron jobs", "etl", "pipelines", "scripting", "workflows" ], "title": "ETL Pipeline", "short_description": "Configurable Extract-Transform-Load pipeline framework for JSON, CSV, and SQLite data sources.", "detailed_description": "A configurable Extract-Transform-Load pipeline framework built on Python stdlib. Pull data from JSON files, CSV files, SQLite databases, or HTTP APIs. Transform with a composable plugin architecture. Load to JSON, CSV, SQLite, or stdout. Includes: Multi-source extraction \u2014 JSON files, CSV files, SQLite databases, HTTP APIs; Plugin-based transforms \u2014 Chain filters, mappers, renamers, and custom transforms; Multi-target loading \u2014 Write to JSON, CSV, SQLite, or stdout; Config file support \u2014 Define complete pipelines in JSON for repeatable ETL jobs; Batch processing \u2014 Configurable batch sizes for memory-efficient large datasets; Pipeline stats \u2014 Track records extracted, transformed, loaded, and errors.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# CSV to JSON conversion\npython src/etl_pipeline.py --source data.csv --dest output.json\n\n# JSON API to SQLite\npython src/etl_pipeline.py --source https://api.docs.example.com/v1/users --dest users.db --table users\n\n# Full pipeline from config\npython src/etl_pipeline.py --config examples/pipeline_config.json\n\n# Nested JSON extraction\npython src/etl_pipeline.py --source response.json --dest items.csv --records-path \"data.items\"", "{\n \"records_extracted\": 1500,\n \"records_transformed\": 1230,\n \"records_loaded\": 1230,\n \"records_errored\": 2,\n \"duration_seconds\": 3.42\n}" ], "included_files": [ "README.md", "examples/pipeline_config.json", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_output.md", "guide/04_license.md", "src/etl_pipeline.py" ], "file_count": 7, "download_size_bytes": 44378, "download_size_human": "43.3 KB", "product_url": "https://datanest-stores.com/automation-hub/#product-etl-pipeline", "preview_url": "https://datanest-stores.com/automation-hub/preview-etl-pipeline", "demo_url": "https://datanest-stores.com/automation-hub/demo-etl-pipeline/" }, { "product_id": "automation-hub:file-watcher", "slug": "file-watcher", "store": "automation-hub", "store_name": "Automation Hub", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "automation", "cloud", "cron jobs", "etl", "pipelines", "scripting", "workflows" ], "title": "File Watcher", "short_description": "File system watcher monitoring directories for changes with glob pattern matching and configurable actions.", "detailed_description": "A file system watcher that monitors directories for changes and triggers configurable actions. Supports glob pattern matching, recursive watching, debouncing, and multiple action types: shell commands, HTTP webhooks, and structured log entries. Includes: Directory monitoring \u2014 Poll-based filesystem watching (works on any OS); Glob pattern matching \u2014 Watch only; Recursive watching \u2014 Monitor subdirectories automatically; Debouncing \u2014 Configurable delay to batch rapid changes into single events; Shell command triggers \u2014 Run any command on file change; Webhook triggers \u2014 POST change events to any HTTP endpoint.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "{\n \"rules\": [\n {\n \"name\": \"python-tests\",\n \"watch_path\": \"./src\",\n \"patterns\": [\"*.py\"],\n \"recursive\": true,\n \"actions\": [\n {\"type\": \"command\", \"command\": \"python -m pytest tests/\"},\n {\"type\": \"log\"}\n ]\n },\n {\n \"name\": \"data-reload\",\n\u2026" ], "included_files": [ "README.md", "examples/watcher_config.json", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_faq.md", "src/file_watcher.py" ], "file_count": 6, "download_size_bytes": 39064, "download_size_human": "38.1 KB", "product_url": "https://datanest-stores.com/automation-hub/#product-file-watcher", "preview_url": "https://datanest-stores.com/automation-hub/preview-file-watcher", "demo_url": "https://datanest-stores.com/automation-hub/demo-file-watcher/" }, { "product_id": "automation-hub:notification-dispatcher", "slug": "notification-dispatcher", "store": "automation-hub", "store_name": "Automation Hub", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "automation", "cloud", "cron jobs", "etl", "pipelines", "scripting", "workflows" ], "title": "Notification Dispatcher", "short_description": "Multi-channel notification system for email, Slack webhooks, generic webhooks, and desktop notifications.", "detailed_description": "A multi-channel notification system built on Python stdlib. Send alerts via email (SMTP), Slack-compatible webhooks, generic HTTP webhooks, and SMS gateways. Supports template rendering, priority levels, retry logic, and delivery logging to SQLite. Includes: Multi-channel dispatch \u2014 Email (SMTP), Slack webhooks, HTTP webhooks, SMS gateways, console; Template rendering \u2014 Python string; Priority levels \u2014 low, normal, high, critical with per-channel routing; Retry with backoff \u2014 Exponential backoff on delivery failures (3 retries default); Delivery logging \u2014 Track every send attempt in SQLite; Batch dispatch \u2014 Send multiple notifications from a single config file.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies", "SMTP server access for email delivery", "Webhook URLs for Slack/webhook channels" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md", "examples/notifications_config.json", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_faq.md", "src/notification_dispatcher.py" ], "file_count": 6, "download_size_bytes": 41572, "download_size_human": "40.6 KB", "product_url": "https://datanest-stores.com/automation-hub/#product-notification-dispatcher", "preview_url": "https://datanest-stores.com/automation-hub/preview-notification-dispatcher", "demo_url": "https://datanest-stores.com/automation-hub/demo-notification-dispatcher/" }, { "product_id": "automation-hub:report-generator", "slug": "report-generator", "store": "automation-hub", "store_name": "Automation Hub", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "automation", "cloud", "cron jobs", "etl", "pipelines", "scripting", "workflows" ], "title": "Report Generator", "short_description": "Build formatted reports from CSV, JSON, or SQLite data: filter, group, aggregate, sort, and render to Markdown, HTML, CSV, or JSON with summary stats and ASCII charts.", "detailed_description": "Build formatted reports from tabular data (CSV, JSON, SQLite). Filter, group, aggregate, sort, and render to Markdown, HTML, CSV, or JSON \u2014 all with Python stdlib, no external dependencies. Includes summary statistics and inline ASCII bar charts for group aggregates. Includes: Multi-source input \u2014 CSV files, JSON files (flat or nested), SQLite databases; Row filtering \u2014 Keep rows matching conditions (eq, ne, gt, lt, gte, lte, contains, regex, in, notnull, isnull); Computed columns \u2014 Add derived fields via arithmetic expressions or string templates; Group-by aggregation \u2014 Group by one or more fields with count, sum, avg, min, max; Sorting \u2014 Sort results by any column, ascending or descending; Multiple output formats \u2014 Markdown tables, self-contained styled HTML, CSV, JSON.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "{\n \"title\": \"Report Title\",\n \"description\": \"Optional description text.\",\n \"source\": {\n \"type\": \"csv\",\n \"path\": \"path/to/data.csv\",\n \"delimiter\": \",\",\n \"encoding\": \"utf-8\",\n \"records_path\": \"\",\n \"table\": \"\",\n \"query\": \"\"\n },\n \"filters\": [\n {\"field\": \"status\", \"op\": \"eq\", \"value\": \"active\"}\n\u2026" ], "included_files": [ "README.md", "examples/report_generator_config.json", "examples/sample_data.csv", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_output.md", "guide/04_license.md", "src/report_generator.py" ], "file_count": 8, "download_size_bytes": 36584, "download_size_human": "35.7 KB", "product_url": "https://datanest-stores.com/automation-hub/#product-report-generator", "preview_url": "https://datanest-stores.com/automation-hub/preview-report-generator", "demo_url": "https://datanest-stores.com/automation-hub/demo-report-generator/" }, { "product_id": "automation-hub:task-scheduler", "slug": "task-scheduler", "store": "automation-hub", "store_name": "Automation Hub", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "automation", "cloud", "cron jobs", "etl", "pipelines", "scripting", "workflows" ], "title": "Task Scheduler", "short_description": "In-process job scheduler with interval, cron, and one-shot triggers, priority run queue, concurrency limits, retry with backoff, missed-run policies, and state persistence.", "detailed_description": "An in-process job scheduler built on Python stdlib. Schedule tasks by fixed interval, cron expression (5-field, with full wildcard/step/range/list syntax), or as a one-shot at a specific datetime. Features a priority-based run queue, configurable concurrency limits, retry with exponential backoff, missed-run policies, and automatic state persistence to JSON. Includes: Cron expressions \u2014 Full 5-field parser supporting , /n, a-b, a,b,c, and combinations; Interval scheduling \u2014 Run tasks every N seconds with drift-free tracking; One-shot tasks \u2014 Fire once at a specified datetime, then never again; Priority queue \u2014 Higher-priority tasks run first when multiple are due simultaneously; Concurrency control \u2014 Cap the number of tasks running in parallel via thread semaphore; Retry with backoff \u2014 Failed tasks retry up to N times with configurable exponential delay.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "{\n \"settings\": {\n \"max_concurrency\": 3,\n \"tick_interval\": 1.0,\n \"state_file\": \"scheduler_state.json\"\n },\n \"tasks\": [\n {\n \"name\": \"my-task\",\n \"schedule_type\": \"cron\",\n \"schedule_value\": \"*/5 * * * *\",\n \"command\": \"echo 'hello'\",\n \"priority\": 3,\n \"retry_limit\": 2,\n\u2026", "[\n {\n \"name\": \"system-heartbeat\",\n \"enabled\": true,\n \"schedule\": \"interval: 60\",\n \"priority\": 1,\n \"next_fire\": \"2026-07-01T08:00:00+00:00\",\n \"last_run\": null,\n \"last_status\": \"pending\",\n \"run_count\": 0,\n \"fail_count\": 0\n }\n]" ], "included_files": [ "README.md", "examples/scheduler_state.json", "examples/task_scheduler_config.json", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_output.md", "guide/04_license.md", "src/task_scheduler.py" ], "file_count": 8, "download_size_bytes": 34733, "download_size_human": "33.9 KB", "product_url": "https://datanest-stores.com/automation-hub/#product-task-scheduler", "preview_url": "https://datanest-stores.com/automation-hub/preview-task-scheduler", "demo_url": "https://datanest-stores.com/automation-hub/demo-task-scheduler/" }, { "product_id": "automation-hub:web-scraper", "slug": "web-scraper", "store": "automation-hub", "store_name": "Automation Hub", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "automation", "cloud", "cron jobs", "etl", "pipelines", "scripting", "workflows" ], "title": "Web Scraper", "short_description": "Python web scraping framework with CSS selectors, pagination, rate limiting, proxy rotation, and caching.", "detailed_description": "A complete web scraping framework built on Python stdlib. CSS-style selectors, automatic pagination, rate limiting, proxy rotation, and multi-format export \u2014 no pip installs needed. Includes: CSS-like selectors \u2014 Extract elements by tag,; Automatic pagination \u2014 Follow next-page links across multiple pages; Rate limiting \u2014 Configurable delay between requests (be a polite scraper); Proxy rotation \u2014 Round-robin through a list of proxy servers; Retry with backoff \u2014 Exponential backoff on 429/5xx errors; Multi-format export \u2014 Save to JSON, CSV, or SQLite.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "{\n \"url\": \"https://api.docs.example.com/products\",\n \"selector\": \".product-card\",\n \"follow_links\": true,\n \"next_page_selector\": \"a.next-page\",\n \"max_pages\": 5,\n \"delay\": 1.5,\n \"proxies\": [\"http://proxy1.docs.example.com:8080\"],\n \"output_format\": \"json\",\n \"output_path\": \"products.json\"\n}", "{\n \"text\": \"Product Name \u2014 $29.99\",\n \"href\": \"/products/widget-pro\",\n \"_page\": 1,\n \"_source_url\": \"https://docs.example.com/shop\",\n \"_scraped_at\": \"2026-03-14T12:00:00+00:00\"\n}" ], "included_files": [ "README.md", "examples/scrape_config.json", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_configuration-reference.md", "guide/04_faq.md", "src/web_scraper.py" ], "file_count": 7, "download_size_bytes": 40639, "download_size_human": "39.7 KB", "product_url": "https://datanest-stores.com/automation-hub/#product-web-scraper", "preview_url": "https://datanest-stores.com/automation-hub/preview-web-scraper", "demo_url": "https://datanest-stores.com/automation-hub/demo-web-scraper/" }, { "product_id": "automation-hub:webhook-router", "slug": "webhook-router", "store": "automation-hub", "store_name": "Automation Hub", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "automation", "cloud", "cron jobs", "etl", "pipelines", "scripting", "workflows" ], "title": "Webhook Router", "short_description": "Lightweight HTTP webhook receiver and router with URL-based routes, HMAC validation, and delivery tracking.", "detailed_description": "A lightweight HTTP webhook receiver and router built on Python stdlib. Define URL-based routes, validate payloads with HMAC signatures, filter by event type, retry failed forwards with exponential backoff, and log every event to SQLite. Includes: URL-based routing \u2014 Pattern-match incoming webhook paths to specific handlers; HMAC signature verification \u2014 Validate webhook authenticity (Stripe, GitHub, etc; Payload validation \u2014 JSON schema-style checks on incoming payloads; Event filtering \u2014 Route based on event type headers or payload fields; Retry with backoff \u2014 Forward events to downstream URLs with exponential backoff; Event persistence \u2014 Log all events to SQLite for audit and replay.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Start the router with default catch-all route on port 8080\npython src/webhook_router.py\n\n# Start with a config file\npython src/webhook_router.py --config examples/webhook_config.json\n\n# Custom port and logging\npython src/webhook_router.py --port 9000 --log-file webhooks.log --log-level DEBUG", "curl -X POST http://localhost:8080/hooks/test \\\n -H \"Content-Type: application/json\" \\\n -d '{\"event\": \"test.ping\", \"data\": {\"message\": \"hello\"}}'" ], "included_files": [ "README.md", "examples/webhook_config.json", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_faq.md", "src/webhook_router.py" ], "file_count": 6, "download_size_bytes": 42231, "download_size_human": "41.2 KB", "product_url": "https://datanest-stores.com/automation-hub/#product-webhook-router", "preview_url": "https://datanest-stores.com/automation-hub/preview-webhook-router", "demo_url": "https://datanest-stores.com/automation-hub/demo-webhook-router/" }, { "product_id": "automation-hub:webhook-trigger", "slug": "webhook-trigger", "store": "automation-hub", "store_name": "Automation Hub", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "automation", "cloud", "cron jobs", "etl", "pipelines", "scripting", "workflows" ], "title": "Webhook Trigger", "short_description": "Event-driven webhook dispatcher: map events to endpoints, render templated payloads, sign with HMAC-SHA256, POST via urllib, retry with backoff, and dead-letter failed deliveries.", "detailed_description": "An event-driven webhook dispatcher built on Python stdlib. Register triggers mapping event names to one or more webhook endpoints. On emit, render a payload from a configurable template, sign it with HMAC-SHA256, POST it via urllib, and retry with exponential backoff on failure. Permanently failed deliveries go to a dead-letter log. Includes: Event-to-endpoint mapping \u2014 Register triggers that fan out a single event to multiple webhook URLs; Payload templating \u2014 Render payloads from {{key}} templates substituted with event data; HMAC-SHA256 signing \u2014 Every outbound request carries a verifiable X-Webhook-Signature header; Retry with exponential backoff \u2014 Configurable max retries and base delay for transient failures; Dead-letter log \u2014 Permanently failed deliveries are persisted to a JSON file for later inspection; Per-endpoint rate limiting \u2014 Token-bucket rate limiter prevents overwhelming downstream services.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "{\n \"total_deliveries\": 2,\n \"delivered\": 0,\n \"failed\": 0,\n \"dry_run\": 2,\n \"dead_letters\": 0,\n \"deliveries\": [\n {\n \"event\": \"order.created\",\n \"endpoint_url\": \"https://api.docs.example.com/webhooks/orders\",\n \"payload\": {\"event\": \"order.created\", \"order_id\": \"42\", \"total\": \"99.95\", \"currency\": \"USD\"},\n \"signature\": \"sha256=abc123...\",\n \"status\": \"dry_run\",\n \"total_attempts\": 1,\n\u2026" ], "included_files": [ "README.md", "examples/webhook_trigger_config.json", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_output.md", "guide/04_license.md", "src/webhook_trigger.py" ], "file_count": 7, "download_size_bytes": 31926, "download_size_human": "31.2 KB", "product_url": "https://datanest-stores.com/automation-hub/#product-webhook-trigger", "preview_url": "https://datanest-stores.com/automation-hub/preview-webhook-trigger", "demo_url": "https://datanest-stores.com/automation-hub/demo-webhook-trigger/" }, { "product_id": "automation-hub:workflow-engine", "slug": "workflow-engine", "store": "automation-hub", "store_name": "Automation Hub", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "automation", "cloud", "cron jobs", "etl", "pipelines", "scripting", "workflows" ], "title": "Workflow Engine", "short_description": "DAG-based task execution engine with conditional branching, parallel tasks, state persistence, and retries.", "detailed_description": "DAG-based task execution engine with conditional branching, parallel tasks, state persistence, and retry policies. Define workflows in JSON, execute them with full observability. Includes: DAG execution \u2014 Tasks execute in dependency order with automatic parallelism; Conditional branching \u2014 Skip tasks based on Python expressions; Parallel execution \u2014 Tasks without dependencies run concurrently via thread pool; State persistence \u2014 Save and resume workflows after failures; Retry policies \u2014 Per-task retry limits with configurable backoff; DAG validation \u2014 Detects cycles and missing dependencies before execution.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Run a workflow\npython src/workflow_engine.py --config examples/workflow_config.json\n\n# Preview execution plan\npython src/workflow_engine.py --config examples/workflow_config.json --dry-run\n\n# Resume a failed workflow\npython src/workflow_engine.py --config examples/workflow_config.json --resume workflow_state.json", "{\n \"name\": \"my-pipeline\",\n \"max_workers\": 4,\n \"tasks\": [\n {\n \"name\": \"build\",\n \"command\": \"make build\",\n \"depends_on\": [\"lint\", \"test\"],\n \"condition\": \"\",\n \"retry_limit\": 2,\n \"timeout\": 300\n }\n ],\n \"on_complete\": \"echo 'Done!'\",\n\u2026" ], "included_files": [ "README.md", "examples/workflow_config.json", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_license.md", "src/workflow_engine.py" ], "file_count": 6, "download_size_bytes": 36208, "download_size_human": "35.4 KB", "product_url": "https://datanest-stores.com/automation-hub/#product-workflow-engine", "preview_url": "https://datanest-stores.com/automation-hub/preview-workflow-engine", "demo_url": "https://datanest-stores.com/automation-hub/demo-workflow-engine/" }, { "product_id": "cert-prep:terraform-associate-prep", "slug": "terraform-associate-prep", "store": "cert-prep", "store_name": "Certification Prep Pro", "domain": "career", "category": "Career, Creator & Business", "tags": [ "aws", "azure", "career", "certification", "cka", "ckad", "comptia", "exam prep", "kubernetes", "study guide", "terraform" ], "title": "Terraform Associate Cert Prep", "short_description": "Complete study guide for HashiCorp Terraform Associate with practice questions, lab exercises, and configuration examples.", "detailed_description": "Complete study guide for the HashiCorp Terraform Associate certification (003). Covers all exam objectives with thorough explanations, runnable HCL examples, 50 practice questions with explained answers, and an exam-day cram sheet.", "price_usd": 49, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "2.0.0", "changelog_at": "2026-06-16", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [], "supported_versions": [], "supported_technologies": [ "Terraform", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "# 1. Install Terraform (https://developer.hashicorp.com/terraform/install)\nterraform version\n\n# 2. Navigate to an example directory\ncd examples/\n\n# 3. Initialize (downloads providers)\nterraform init\n\n# 4. Preview changes\nterraform plan\n\n# 5. Apply (creates resources \u2014 use a sandbox account!)\nterraform apply\n\u2026" ], "included_files": [ "README.md", "config.example.yaml", "examples/01-basic-infrastructure.tf", "examples/02-variables-and-outputs.tf", "examples/03-modules/main.tf", "examples/03-modules/modules/web-server/main.tf", "examples/03-modules/modules/web-server/outputs.tf", "examples/03-modules/modules/web-server/variables.tf", "examples/04-remote-state.tf", "examples/05-provisioners.tf", "examples/06-data-sources.tf", "examples/07-expressions.tf", "guide/01-overview.md", "guide/02-terraform-core-concepts.md", "guide/03-terraform-state-and-remote-backends.md", "practice-questions/answer-key.md", "practice-questions/questions.md", "quick-reference/cheatsheet.md", "study-guide/01-iac-concepts.md", "study-guide/02-terraform-purpose-workflow.md", "study-guide/03-providers.md", "study-guide/04-state-management.md", "study-guide/05-modules.md", "study-guide/06-hcl-syntax.md", "study-guide/07-variables-outputs.md", "study-guide/08-provisioners.md", "study-guide/09-terraform-cloud.md" ], "file_count": 27, "download_size_bytes": 143961, "download_size_human": "140.6 KB", "product_url": "https://datanest-stores.com/cert-prep/#product-terraform-associate-prep", "preview_url": "https://datanest-stores.com/cert-prep/preview-terraform-associate-prep", "demo_url": "https://datanest-stores.com/cert-prep/demo-terraform-associate-prep/" }, { "product_id": "cert-prep:kubernetes-cka-labs", "slug": "kubernetes-cka-labs", "store": "cert-prep", "store_name": "Certification Prep Pro", "domain": "career", "category": "Career, Creator & Business", "tags": [ "aws", "azure", "career", "certification", "cka", "ckad", "comptia", "exam prep", "kubernetes", "study guide", "terraform" ], "title": "Kubernetes CKA Lab Workbook", "short_description": "Hands-on lab exercises for Certified Kubernetes Administrator: cluster setup, networking, storage, security, and troubleshooting.", "detailed_description": "Hands-on lab exercises for the Certified Kubernetes Administrator (CKA) exam. Practice every exam domain with realistic tasks, step-by-step solutions, verification commands, and ready-to-apply YAML manifests.", "price_usd": 79, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": "2026-06-16", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Linux command line", "Basic networking concepts", "Container fundamentals", "YAML syntax" ], "supported_versions": [], "supported_technologies": [ "Docker", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "## Task X: [Title]\n**Objective**: What you need to accomplish.\n**Exam weight**: Which domain this falls under.\n\n### Task Statement\nA realistic task description similar to what you'll see on the exam.\n\n### Solution\nStep-by-step commands and explanations.\n\n### Verification\nCommands to confirm your solution is correct.\n\n### Key Takeaways\n\u2026" ], "included_files": [ "README.md", "config.example.yaml", "guide/01-cluster-architecture.md", "guide/02-workloads-scheduling.md", "guide/03-services-networking.md", "guide/04-storage.md", "guide/05-troubleshooting.md", "guide/06-security-rbac.md", "guide/07-etcd-backup-restore.md", "labs/00-environment-setup.md", "labs/01-cluster-architecture.md", "labs/02-workloads-scheduling.md", "labs/03-services-networking.md", "labs/04-storage.md", "labs/05-troubleshooting.md", "labs/06-security-rbac.md", "labs/07-etcd-backup-restore.md", "manifests/cluster-setup/cluster-role-binding.yaml", "manifests/cluster-setup/kubeadm-config.yaml", "manifests/networking/networking.yaml", "manifests/security/security.yaml", "manifests/storage/storage.yaml", "manifests/workloads/cronjob.yaml", "manifests/workloads/deployment.yaml", "quick-reference/cka-cheatsheet.md" ], "file_count": 25, "download_size_bytes": 170916, "download_size_human": "166.9 KB", "product_url": "https://datanest-stores.com/cert-prep/#product-kubernetes-cka-labs", "preview_url": "https://datanest-stores.com/cert-prep/preview-kubernetes-cka-labs", "demo_url": "https://datanest-stores.com/cert-prep/demo-kubernetes-cka-labs/" }, { "product_id": "cert-prep:aws-sa-associate-guide", "slug": "aws-sa-associate-guide", "store": "cert-prep", "store_name": "Certification Prep Pro", "domain": "career", "category": "Career, Creator & Business", "tags": [ "aws", "azure", "career", "certification", "cka", "ckad", "comptia", "exam prep", "kubernetes", "study guide", "terraform" ], "title": "AWS SA Associate Study Guide", "short_description": "Comprehensive study guide covering all AWS Solutions Architect Associate domains with architecture diagrams and practice scenarios.", "detailed_description": "Comprehensive study guide covering all four exam domains for the AWS Solutions Architect Associate certification. Includes architecture decision scenarios, service comparison tables, 50 practice questions with explanations, and an exam-day cram sheet.", "price_usd": 49, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "2.0.0", "changelog_at": "2026-06-16", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [], "supported_versions": [], "supported_technologies": [ "Shell", "YAML" ], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md", "config.example.yaml", "examples/architecture-scenarios.md", "examples/cli-commands.sh", "guide/01-overview.md", "guide/02-aws-well-architected-framework.md", "guide/03-high-availability-architecture-design.md", "practice-questions/answer-key.md", "practice-questions/questions.md", "quick-reference/cheatsheet.md", "quick-reference/service-comparison-tables.md", "study-guide/01-design-secure-architectures.md", "study-guide/02-design-resilient-architectures.md", "study-guide/03-design-high-performing-architectures.md", "study-guide/04-design-cost-optimized-architectures.md" ], "file_count": 15, "download_size_bytes": 105975, "download_size_human": "103.5 KB", "product_url": "https://datanest-stores.com/cert-prep/#product-aws-sa-associate-guide", "preview_url": "https://datanest-stores.com/cert-prep/preview-aws-sa-associate-guide", "demo_url": "https://datanest-stores.com/cert-prep/demo-aws-sa-associate-guide/" }, { "product_id": "cert-prep:azure-fundamentals-guide", "slug": "azure-fundamentals-guide", "store": "cert-prep", "store_name": "Certification Prep Pro", "domain": "career", "category": "Career, Creator & Business", "tags": [ "aws", "azure", "career", "certification", "cka", "ckad", "comptia", "exam prep", "kubernetes", "study guide", "terraform" ], "title": "Azure Fundamentals (AZ-900) Guide", "short_description": "Complete AZ-900 study material covering cloud concepts, Azure services, security, pricing, and SLA fundamentals with practice questions.", "detailed_description": "Beginner-friendly study guide covering all four AZ-900 exam domains: cloud concepts, core Azure services, security/identity/compliance, and Azure pricing/SLA/lifecycle. Includes 40 practice questions with explained answers and an exam-day cram sheet.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "2.0.0", "changelog_at": "2026-06-16", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [], "supported_versions": [], "supported_technologies": [ "Shell", "YAML" ], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md", "config.example.yaml", "examples/az-cli-commands.sh", "guide/01-overview.md", "guide/02-cloud-concepts-and-azure-architecture.md", "guide/03-azure-security,-pricing-and-slas.md", "practice-questions/answer-key.md", "practice-questions/questions.md", "quick-reference/cheatsheet.md", "study-guide/01-cloud-concepts.md", "study-guide/02-core-azure-services.md", "study-guide/03-security-identity-compliance.md", "study-guide/04-pricing-sla-lifecycle.md" ], "file_count": 13, "download_size_bytes": 86608, "download_size_human": "84.6 KB", "product_url": "https://datanest-stores.com/cert-prep/#product-azure-fundamentals-guide", "preview_url": "https://datanest-stores.com/cert-prep/preview-azure-fundamentals-guide", "demo_url": "https://datanest-stores.com/cert-prep/demo-azure-fundamentals-guide/" }, { "product_id": "cert-prep:databricks-de-associate", "slug": "databricks-de-associate", "store": "cert-prep", "store_name": "Certification Prep Pro", "domain": "career", "category": "Career, Creator & Business", "tags": [ "aws", "azure", "career", "certification", "cka", "ckad", "comptia", "exam prep", "kubernetes", "study guide", "terraform" ], "title": "Databricks DE Associate Prep", "short_description": "Study guide for Databricks Data Engineer Associate covering Spark, Delta Lake, DLT, Unity Catalog, and workflow orchestration.", "detailed_description": "Complete study guide, 45 practice questions with explained answers, PySpark & SQL code examples, and an exam-day cheatsheet for the Databricks Certified Data Engineer Associate certification.", "price_usd": 69, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": "2026-06-16", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python", "SQL", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "Domain 1: Lakehouse Platform .............. ~24% \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\nDomain 2: ELT with Spark SQL & PySpark .... ~29% \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2591\u2591\nDomain 3: Incremental Data Processing ..... ~22% \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\nDomain 4: Production Pipelines ............ ~16% \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\nDomain 5: Data Governance ................. ~9% \u2588\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591", "# Replace these with your actual catalog and schema names\nCATALOG = \"catalog_gold_analytics\"\nSCHEMA = \"sales_data\"" ], "included_files": [ "README.md", "config.example.yaml", "examples/delta-lake-operations.py", "examples/dlt-pipeline-example.py", "examples/pyspark-examples.py", "examples/spark-sql-examples.sql", "examples/structured-streaming-example.py", "guide/01-overview.md", "guide/02-lakehouse-platform-architecture.md", "guide/03-elt-with-spark-sql-and-pyspark.md", "practice-questions/answer-key.md", "practice-questions/questions.md", "quick-reference/cheatsheet.md", "study-guide/01-lakehouse-platform.md", "study-guide/02-elt-spark-sql-pyspark.md", "study-guide/03-incremental-data-processing.md", "study-guide/04-production-pipelines.md", "study-guide/05-data-governance.md" ], "file_count": 18, "download_size_bytes": 142823, "download_size_human": "139.5 KB", "product_url": "https://datanest-stores.com/cert-prep/#product-databricks-de-associate", "preview_url": "https://datanest-stores.com/cert-prep/preview-databricks-de-associate", "demo_url": "https://datanest-stores.com/cert-prep/demo-databricks-de-associate/" }, { "product_id": "cert-prep:kubernetes-ckad-labs", "slug": "kubernetes-ckad-labs", "store": "cert-prep", "store_name": "Certification Prep Pro", "domain": "career", "category": "Career, Creator & Business", "tags": [ "aws", "azure", "career", "certification", "cka", "ckad", "comptia", "exam prep", "kubernetes", "study guide", "terraform" ], "title": "Kubernetes CKAD Lab 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A curated collection of 5 core serverless patterns (with 30+ pattern variations documented) that solve the most common distributed systems challenges on AWS. Each pattern includes deployable infrastructure-as-code, working Lambda functions, and deep-dive documentation on architecture decisions, cost optimization, and security.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "AWS CLI v2 with configured credentials", "Python 3.12+", "AWS account with appropriate IAM permissions", "No external Python packages required" ], "supported_versions": [ "Python 3.12+" ], "supported_technologies": [ "AWS", "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "# Run the interactive pattern selector\npython3 scripts/pattern_selector.py\n\n# Or see all patterns at a glance\npython3 scripts/pattern_selector.py --all", "# Compare all patterns at 1M requests/month\npython3 scripts/cost_calculator.py --requests 1000000\n\n# Full comparison across traffic levels\npython3 scripts/cost_calculator.py --compare" ], "included_files": [ "README.md", "cloudformation/api-gateway-lambda.yaml", "cloudformation/cqrs-event-sourcing.yaml", "cloudformation/event-driven-pipeline.yaml", "cloudformation/fan-out-pattern.yaml", "cloudformation/saga-orchestration.yaml", "docs/architecture.md", "docs/cost-optimization.md", "docs/deployment-guide.md", "docs/patterns-catalog.md", "docs/security-considerations.md", "examples/api-gateway-config.json", "examples/event-bridge-rules.json", "examples/step-functions-definition.json", "functions/api_handler.py", "functions/event_processor.py", "functions/saga_coordinator.py", "functions/stream_processor.py", "guide/01_what-s-included.md", "guide/02_architecture-overview.md", "guide/03_cost-estimates.md", "guide/04_license.md", "scripts/cost_calculator.py", "scripts/pattern_selector.py" ], "file_count": 24, "download_size_bytes": 165095, "download_size_human": "161.2 KB", "product_url": "https://datanest-stores.com/cloud-architecture/#product-serverless-patterns-collection", "preview_url": "https://datanest-stores.com/cloud-architecture/preview-serverless-patterns-collection", "demo_url": "https://datanest-stores.com/cloud-architecture/demo-serverless-patterns-collection/" }, { "product_id": "cloud-architecture:cloud-cost-optimization", "slug": "cloud-cost-optimization", "store": "cloud-architecture", "store_name": "Cloud Architecture Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "aws", "azure", "cloud", "cloud migration", "cost optimization", "gcp", "multi-cloud", "serverless" ], "title": "Cloud Cost Optimization Toolkit", "short_description": "Scripts and dashboards for identifying waste, right-sizing instances, reserved capacity planning, and budget alerts.", "detailed_description": "Identify waste, right-size instances, plan reserved capacity, and set budget alerts across AWS and Azure. 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Built for cloud security engineers, platform teams, and architects who need to pass SOC 2, ISO 27001, or PCI audits without scrambling. ---", "price_usd": 49, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "AWS Account", "Terraform >= 1.5.0", "Python >= 3.10", "AWS CLI v2 configured with admin credentials", "Permissions" ], "supported_versions": [], "supported_technologies": [ "Terraform", "AWS", "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "# SecurityHub with CIS conformance pack\naws cloudformation deploy \\\n --template-file cloudformation/security-hub-baseline.yaml \\\n --stack-name security-hub-baseline \\\n --parameter-overrides \\\n AlertEmail=security-team@docs.example.com \\\n EnableCISBenchmark=true \\\n --capabilities CAPABILITY_IAM\n\n# GuardDuty (organization-wide)\naws cloudformation deploy \\\n --template-file cloudformation/guardduty-org-setup.yaml \\\n --stack-name guardduty-org-setup \\\n --parameter-overrides \\\n\u2026", "# Full CIS benchmark compliance check\npython scripts/compliance_checker.py --profile production --format report\n\n# IAM-specific deep audit\npython scripts/iam_audit.py --profile production --output iam-audit-report.json\n\n# Overall security posture score\npython scripts/security_score_calculator.py --profile production" ], "included_files": [ "README.md", "cloudformation/guardduty-org-setup.yaml", "cloudformation/security-hub-baseline.yaml", "docs/architecture.md", "docs/cis_benchmark_guide.md", "docs/incident_response_runbook.md", "examples/compliance_config.yaml", "examples/security_policies.json", "guide/01_what-s-included.md", "guide/02_quick-start.md", "guide/03_product-files.md", "scripts/compliance_checker.py", "scripts/iam_audit.py", "scripts/security_score_calculator.py", "terraform/encryption_baseline.tf", "terraform/iam_baseline.tf", "terraform/logging_monitoring.tf", "terraform/network_security.tf", "terraform/variables.tf" ], "file_count": 19, "download_size_bytes": 174070, "download_size_human": "170.0 KB", "product_url": "https://datanest-stores.com/cloud-architecture/#product-cloud-security-baseline", "preview_url": "https://datanest-stores.com/cloud-architecture/preview-cloud-security-baseline", "demo_url": "https://datanest-stores.com/cloud-architecture/demo-cloud-security-baseline/" }, { "product_id": "cloud-architecture:event-driven-architecture", "slug": "event-driven-architecture", "store": "cloud-architecture", "store_name": "Cloud Architecture Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "aws", "azure", "cloud", "cloud migration", "cost optimization", "gcp", "multi-cloud", "serverless" ], "title": "Event-Driven Architecture Kit", "short_description": "Event sourcing, CQRS, and pub/sub patterns with Kafka, SQS/SNS, Event Grid implementations and schemas.", "detailed_description": "Production-ready infrastructure templates for building event-driven systems on AWS: SQS/SNS fan-out, EventBridge routing, Kinesis streaming, dead letter queue processing, and schema validation -- all codified in Terraform and CloudFormation. Stop building point-to-point integrations that become spaghetti. This toolkit gives you battle-tested patterns for decoupled, scalable event-driven architectures. Every pattern is implemented as deployable IaC with monitoring, DLQ handling, and schema enforcement built in. 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Skip the boilerplate. Start with 14 battle-tested DAGs covering ETL, data quality, ML pipelines, warehouse loading, CDC streaming, database replication, SLA monitoring, dynamic task mapping, and more. ---", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "2.0.0", "changelog_at": "2026-07-01", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Airflow", "Databricks", "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "# Copy DAGs\ncp dags/*.py $AIRFLOW_HOME/dags/\n\n# Copy plugins (preserves directory structure)\ncp -r plugins/ $AIRFLOW_HOME/plugins/\n\n# Copy configs (for reference / import)\ncp -r configs/ $AIRFLOW_HOME/configs/", "# Import connections\nairflow connections import configs/connections.yaml\n\n# Import variables\nairflow variables import configs/variables.yaml" ], "included_files": [ "README.md", "configs/connections.yaml", "configs/dag_factory_config.yaml", "configs/variables.yaml", "dags/api_ingestion_dag.py", "dags/backfill_dag.py", "dags/cdc_streaming_dag.py", "dags/cleanup_dag.py", "dags/cross_dag_dependency_dag.py", "dags/data_quality_dag.py", "dags/data_warehouse_load_dag.py", "dags/database_replication_dag.py", "dags/dbt_orchestration_dag.py", "dags/dynamic_task_mapping_dag.py", "dags/etl_pipeline_dag.py", "dags/file_sensor_dag.py", "dags/ml_pipeline_dag.py", "dags/sla_monitoring_dag.py", "guide/01_what-you-get.md", "guide/02_getting-started.md", "guide/03_architecture.md", "guide/04_faq.md", "guides/airflow-best-practices.md", "plugins/hooks/slack_webhook_hook.py", "plugins/hooks/teams_webhook_hook.py", "plugins/operators/data_quality_operator.py", "plugins/operators/databricks_notebook_operator.py", "plugins/operators/delta_table_sensor.py", "plugins/operators/spark_submit_operator.py", "plugins/sensors/s3_key_sensor_extended.py", "tests/test_dags.py" ], "file_count": 31, "download_size_bytes": 120156, "download_size_human": "117.3 KB", "product_url": "https://datanest-stores.com/data-engineering/#product-airflow-dag-templates", "preview_url": "https://datanest-stores.com/data-engineering/preview-airflow-dag-templates", "demo_url": "https://datanest-stores.com/data-engineering/demo-airflow-dag-templates/" }, { "product_id": "data-engineering:cdc-replication-toolkit", "slug": "cdc-replication-toolkit", "store": "data-engineering", "store_name": "DataStack Pro", "domain": "data", "category": "Data & Analytics", "tags": [ "cloud data", "data", "data engineering", "data pipelines", "databricks", "etl", "lakehouse", "spark" ], "title": "CDC Replication Toolkit", "short_description": "Change data capture pipeline for Databricks with Debezium parsing, merge into, and offset management.", "detailed_description": "Production-ready change data capture pipeline for Databricks \u2014 Debezium parsing, MERGE INTO application, offset management, and replication monitoring. By Datanest Digital | Version 1.0.0 | $49 --- Includes: CDC Event Processor \u2014 Process insert/update/delete events and apply to target Delta tables; Debezium Parser \u2014 Parse Debezium JSON envelope format with before/after images; MERGE Applier \u2014 Apply CDC changes via MERGE INTO with full operation type support; Offset Manager \u2014 Track Kafka offsets and LSN positions with checkpoint management; Schema Mapper \u2014 Map source database schemas to Delta targets with type conversion; Replication Monitor \u2014 Track replication lag, throughput, and error rates.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Databricks Runtime 13.3 LTS or later", "Unity Catalog enabled workspace", "Python 3.10+", "Apache Kafka cluster with Debezium connectors", "pyyaml library", "confluent-kafka" ], "supported_versions": [ "Python 3.10+", "Databricks Runtime 13.3" ], "supported_technologies": [ "Databricks", "Kafka", "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "source:\n type: postgres\n kafka_bootstrap_servers: \"broker1:9092,broker2:9092\"\n topic_prefix: \"dbserver1\"\n tables:\n - \"public.orders\"\n - \"public.customers\"", "from src.cdc_processor import CDCProcessor\n\nprocessor = CDCProcessor(config_path=\"configs/replication_config.yaml\")\nprocessor.start_streaming() # Starts structured streaming with checkpoints" ], "included_files": [ "README.md", "configs/replication_config.yaml", "configs/source_mappings/mysql_to_delta.yaml", "configs/source_mappings/postgres_to_delta.yaml", "configs/source_mappings/sqlserver_to_delta.yaml", "guide/01_what-you-get.md", "guide/02_getting-started.md", "guide/03_requirements.md", "guide/04_support.md", "guides/cdc-replication-guide.md", "notebooks/replication_status.py", "notebooks/start_replication.py", "src/cdc_processor.py", "src/debezium_parser.py", "src/merge_applier.py", "src/offset_manager.py", "src/replication_monitor.py", "src/schema_mapper.py", "tests/conftest.py", "tests/test_cdc_processor.py", "tests/test_debezium_parser.py" ], "file_count": 21, "download_size_bytes": 56419, "download_size_human": "55.1 KB", "product_url": "https://datanest-stores.com/data-engineering/#product-cdc-replication-toolkit", "preview_url": "https://datanest-stores.com/data-engineering/preview-cdc-replication-toolkit", "demo_url": "https://datanest-stores.com/data-engineering/demo-cdc-replication-toolkit/" }, { "product_id": "data-engineering:data-catalog-builder", "slug": "data-catalog-builder", "store": "data-engineering", "store_name": "DataStack Pro", "domain": "data", "category": "Data & Analytics", "tags": [ "cloud data", "data", "data engineering", "data pipelines", "databricks", "etl", "lakehouse", "spark" ], "title": "Data Catalog Builder", "short_description": "Automated metadata discovery, data dictionary generation, and searchable catalog for Unity Catalog.", "detailed_description": "Automated metadata discovery, data dictionary generation, quality scoring, and searchable catalog for Databricks Unity Catalog. By Datanest Digital | Version 1.0.0 | $39 --- Includes: Catalog Scanner \u2014 Discover tables, columns, and metadata across Unity Catalog schemas; Metadata Enricher \u2014 Add business descriptions, owners, tags, and classification labels; Lineage Mapper \u2014 Trace column-level lineage from bronze to gold layers; Search Index \u2014 Full-text search across table/column names, descriptions, and tags; Quality Scorer \u2014 Score tables on completeness, freshness, documentation, and conformance; Catalog Reporter \u2014 Generate Markdown data dictionaries from Jinja2 templates.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Databricks Runtime 13.3 LTS or later", "Unity Catalog enabled workspace", "Python 3.10+", "pyyaml and jinja2 libraries" ], "supported_versions": [ "Python 3.10+", "Databricks Runtime 13.3" ], "supported_technologies": [ "Databricks", "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "scanner:\n catalogs: [\"main\"]\n schemas: [\"bronze\", \"silver\", \"gold\"]\n exclude_patterns: [\"_tmp_*\", \"_staging_*\"]", "from src.catalog_scanner import CatalogScanner\n\nscanner = CatalogScanner(config_path=\"configs/catalog_config.yaml\")\ncatalog = scanner.scan_all()\nprint(f\"Discovered {catalog.table_count} tables across {catalog.schema_count} schemas\")" ], "included_files": [ "README.md", "configs/catalog_config.yaml", "configs/classification_rules.yaml", "configs/templates/table_doc.md.j2", "guide/01_what-you-get.md", "guide/02_getting-started.md", "guide/03_requirements.md", "guide/04_support.md", "guides/data-catalog-guide.md", "notebooks/catalog_dashboard.py", "notebooks/scan_catalog.py", "src/catalog_reporter.py", "src/catalog_scanner.py", "src/lineage_mapper.py", "src/metadata_enricher.py", "src/quality_scorer.py", "src/search_index.py", "tests/conftest.py", "tests/test_catalog_scanner.py", "tests/test_quality_scorer.py" ], "file_count": 20, "download_size_bytes": 55835, "download_size_human": "54.5 KB", "product_url": "https://datanest-stores.com/data-engineering/#product-data-catalog-builder", "preview_url": "https://datanest-stores.com/data-engineering/preview-data-catalog-builder", "demo_url": "https://datanest-stores.com/data-engineering/demo-data-catalog-builder/" }, { "product_id": "data-engineering:data-observability-setup", "slug": "data-observability-setup", "store": "data-engineering", "store_name": "DataStack Pro", "domain": "data", "category": "Data & Analytics", "tags": [ "cloud data", "data", "data engineering", "data pipelines", "databricks", "etl", "lakehouse", "spark" ], "title": "Data Observability Setup", "short_description": "Observability framework for Databricks with lineage tracking, anomaly detection, and SLA monitoring.", "detailed_description": "Complete observability framework for Databricks data pipelines \u2014 lineage tracking, anomaly detection, SLA monitoring, and alerting. By Datanest Digital | Version 1.0.0 | $49 --- Includes: Data Lineage Tracking \u2014 Trace every record from source through transformations to target, stored in Delta; Metric Collection \u2014 Automated capture of row counts, durations, data volumes, and error rates; Statistical Anomaly Detection \u2014 Z-score, IQR, and moving average methods to catch pipeline drift; Freshness Monitoring \u2014 SLA-aware data freshness checks with breach detection; Alert Routing \u2014 Multi-channel alerting via Slack, PagerDuty, and email with severity-based routing; Dashboard-Ready Data \u2014 Pre-aggregated health scores and metrics for observability dashboards.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Databricks Runtime 13.3 LTS or later", "Unity Catalog enabled workspace", "Python 3.10+", "PySpark", "requests library", "pyyaml library" ], "supported_versions": [ "Python 3.10+", "Databricks Runtime 13.3" ], "supported_technologies": [ "Databricks", "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "# In Databricks \u2014 run notebooks/setup_observability.py\n# Creates: observability.lineage, observability.metrics,\n# observability.alerts, observability.anomalies", "from src.lineage_tracker import LineageTracker\n\ntracker = LineageTracker(catalog=\"main\", schema=\"observability\")\ntracker.register_source(\"raw_orders\", \"s3://lake/raw/orders/\")\ntracker.register_transformation(\n \"clean_orders\",\n sql=\"SELECT * FROM raw_orders WHERE status != 'cancelled'\",\n input_datasets=[\"raw_orders\"],\n)\ntracker.register_target(\"curated_orders\", \"main.curated.orders\")\ntracker.commit_lineage(pipeline_name=\"orders_etl\")" ], "included_files": [ "README.md", "configs/alert_rules.yaml", "configs/observability_config.yaml", "guide/01_what-you-get.md", "guide/02_getting-started.md", "guide/03_requirements.md", "guide/04_support.md", "guides/data-observability-guide.md", "notebooks/observability_dashboard.py", "notebooks/setup_observability.py", "src/alert_manager.py", "src/anomaly_detector.py", "src/dashboard_data.py", "src/freshness_monitor.py", "src/lineage_tracker.py", "src/metric_collector.py", "tests/conftest.py", "tests/test_anomaly_detector.py", "tests/test_freshness_monitor.py" ], "file_count": 19, "download_size_bytes": 52917, "download_size_human": "51.7 KB", "product_url": "https://datanest-stores.com/data-engineering/#product-data-observability-setup", "preview_url": "https://datanest-stores.com/data-engineering/preview-data-observability-setup", "demo_url": "https://datanest-stores.com/data-engineering/demo-data-observability-setup/" }, { "product_id": "data-engineering:data-pipeline-testing", "slug": "data-pipeline-testing", "store": "data-engineering", "store_name": "DataStack Pro", "domain": "data", "category": "Data & Analytics", "tags": [ "cloud data", "data", "data engineering", "data pipelines", "databricks", "etl", "lakehouse", "spark" ], "title": "Data Pipeline Testing Kit", "short_description": "Comprehensive testing framework for PySpark data pipelines from unit tests to integration validation.", "detailed_description": "Comprehensive testing framework for PySpark data pipelines \u2014 from unit tests to integration validation. By Datanest Digital | Version 1.0.0 | $39 --- Includes: Test Framework \u2014 base classes and runners for PySpark unit/integration tests; Data Generators \u2014 realistic synthetic data factories for customers, orders, events; Custom Assertions \u2014 DataFrame-level assertions for schema, row count, nulls, uniqueness; Mock Utilities \u2014 helpers for mocking spark, dbutils, Delta tables, and external APIs; Snapshot Testing \u2014 golden-file comparison for pipeline output validation; Sample Fixtures \u2014 ready-to-use JSON test data (customers, orders, expected outputs).", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "PySpark 3.5+", "delta-spark 3.1+", "pytest 7+", "PyYAML 6+", "Java 11+" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Apache Spark", "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "pip install pyspark delta-spark pytest pyyaml", "from test_framework import SparkTestCase\n\nclass TestMyPipeline(SparkTestCase):\n def test_ingestion(self):\n # Create test data\n df = self.create_dataframe(\n [(\"Alice\", 100), (\"Bob\", 200)],\n schema=[\"name\", \"amount\"]\n )\n # Run your pipeline logic\n result = my_transform(df)\n # Assert results\n self.assert_row_count(result, 2)\n self.assert_no_nulls(result, [\"name\", \"amount\"])" ], "included_files": [ "README.md", "config.example.yaml", "configs/test_config.yaml", "fixtures/expected_outputs/customer_summary.json", "fixtures/sample_customers.json", "fixtures/sample_orders.json", "guide/01_what-you-get.md", "guide/02_getting-started.md", "guide/03_requirements.md", "guides/testing-data-pipelines.md", "pyproject.toml", "src/assertions.py", "src/data_generators.py", "src/mock_utils.py", "src/snapshot_testing.py", "src/test_framework.py", "tests/conftest.py", "tests/test_bronze_pipeline.py", "tests/test_gold_pipeline.py", "tests/test_silver_pipeline.py" ], "file_count": 20, "download_size_bytes": 66317, "download_size_human": "64.8 KB", "product_url": "https://datanest-stores.com/data-engineering/#product-data-pipeline-testing", "preview_url": "https://datanest-stores.com/data-engineering/preview-data-pipeline-testing", "demo_url": "https://datanest-stores.com/data-engineering/demo-data-pipeline-testing/" }, { "product_id": "data-engineering:data-quality-framework", "slug": "data-quality-framework", "store": "data-engineering", "store_name": "DataStack Pro", "domain": "data", "category": "Data & Analytics", "tags": [ "cloud data", "data", "data engineering", "data pipelines", "databricks", "etl", "lakehouse", "spark" ], "title": "Data Quality Framework", "short_description": "Pluggable quality engine with built-in checks for completeness, accuracy, consistency, and timeliness.", "detailed_description": "Trust your data. A pluggable quality engine with built-in checks for completeness, uniqueness, validity, freshness, and consistency \u2014 plus automated reporting to Slack, HTML, and Delta Lake. By Datanest Digital | Version 1.0.0 | $49 --- Includes: Quality Engine \u2014 Rule-based engine that loads checks from YAML, executes them; 6 Check Types \u2014 Completeness (null/empty), uniqueness (duplicates), validity; 3 Reporters \u2014 Slack webhook notifications, standalone HTML reports, and Delta Lake; YAML Configuration \u2014 Define rules and thresholds in human-readable YAML; no code; Databricks Notebook \u2014 Ready-to-run notebook for executing quality checks as a; Strategy Guide \u2014 Best practices for implementing data quality at scale.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Databricks Runtime 13.3 LTS or later", "Apache Spark 3.4+", "Delta Lake 2.4+", "Python 3.10+", "requests" ], "supported_versions": [], "supported_technologies": [ "Apache Spark", "Databricks", "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "rules:\n - name: \"customer_email_not_null\"\n table: \"analytics.silver.customers\"\n check_type: \"completeness\"\n columns: [\"email\"]\n threshold: 0.99 # 99% must be non-null\n\n - name: \"order_id_unique\"\n table: \"analytics.silver.orders\"\n check_type: \"uniqueness\"\n columns: [\"order_id\"]\n threshold: 1.0 # 100% unique", "from src.quality_engine import QualityEngine\n\nengine = QualityEngine.from_config(\n rules_path=\"configs/quality_rules.yaml\",\n thresholds_path=\"configs/thresholds.yaml\",\n)\n\n# Execute all rules and get a report\nreport = engine.run_all()\nprint(report.summary())\n\n# Check if all rules passed\nif not report.passed:\n print(f\"FAILED: {report.failed_count} of {report.total_count} checks failed\")" ], "included_files": [ "README.md", "configs/quality_rules.yaml", "configs/thresholds.yaml", "guide/01_what-you-get.md", "guide/02_getting-started.md", "guide/03_architecture.md", "guide/04_support.md", "guides/data-quality-strategy.md", "notebooks/run_quality_checks.py", "src/checks/completeness.py", "src/checks/consistency.py", "src/checks/custom.py", "src/checks/freshness.py", "src/checks/uniqueness.py", "src/checks/validity.py", "src/quality_engine.py", "src/reporters/delta_reporter.py", "src/reporters/html_reporter.py", "src/reporters/slack_reporter.py", "tests/conftest.py", "tests/test_quality_engine.py" ], "file_count": 21, "download_size_bytes": 51467, "download_size_human": "50.3 KB", "product_url": "https://datanest-stores.com/data-engineering/#product-data-quality-framework", "preview_url": "https://datanest-stores.com/data-engineering/preview-data-quality-framework", "demo_url": "https://datanest-stores.com/data-engineering/demo-data-quality-framework/" }, { "product_id": "data-engineering:databricks-audit-toolkit", "slug": "databricks-audit-toolkit", "store": "data-engineering", "store_name": "DataStack Pro", "domain": "data", "category": "Data & Analytics", "tags": [ "cloud data", "data", "data engineering", "data pipelines", "databricks", "etl", "lakehouse", "spark" ], "title": "Databricks Audit Toolkit", "short_description": "Production-ready audit and inventory toolkit for Databricks workspaces. Runs 12 independent audit modules across security, governance, cost, and configuration with consolidated reporting.", "detailed_description": "A comprehensive, production-ready audit and inventory toolkit for Databricks workspaces. Runs 12 independent audit modules covering security, governance, compute costs, access control, and data lineage \u2014 then generates professional HTML and JSON reports with risk scores and actionable recommendations. Built by practitioners who manage large-scale Databricks environments daily. --- Includes: 12 audit modules covering Unity Catalog, access control, secrets, compute,; Professional HTML reports with risk scores, severity distribution,; Machine-readable JSON output for SIEM ingestion, dashboards, and trend; Risk scoring engine that aggregates findings into a 0-100 risk score; Standalone modules \u2014 run all 12 audits or pick specific ones; Rate limiting and retry logic \u2014 respects Databricks API limits with.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Databricks", "Python", "Shell" ], "compatibility_notes": null, "preview_snippets": [ "# 1. Clone or unzip the toolkit\ncd databricks-audit-toolkit\n\n# 2. Install Python dependencies\npip install -r requirements.txt\n\n# 3. Set your credentials\nexport DATABRICKS_HOST='https://your-workspace.cloud.databricks.com'\nexport DATABRICKS_TOKEN='dapi...'\n\n# 4. Run all audits\n./run_audit.sh --all\n\n# 5. Open the report\n\u2026", "# Option 1: pip install dependencies\npip install -r requirements.txt\n\n# Option 2: pip install in a virtual environment\npython3 -m venv .venv\nsource .venv/bin/activate\npip install -r requirements.txt" ], "included_files": [ "README.md", "audits/__init__.py", "audits/a01_catalog_inventory.py", "audits/a02_job_inventory.py", "audits/a03_access_audit.py", "audits/a04_secret_inventory.py", "audits/a05_compute_analysis.py", "audits/a06_security_findings.py", "audits/a07_audit_logs.py", "audits/a08_external_storage.py", "audits/a09_token_inventory.py", "audits/a10_query_history.py", "audits/a11_data_lineage.py", "audits/a12_table_permissions.py", "config.py", "examples/sample_output.json", "examples/scheduling_guide.md", "guide/01_table-of-contents.md", "guide/02_installation.md", "guide/03_reports.md", "guide/04_security-considerations.md", "output/.gitkeep", "reports/__init__.py", "reports/html_report.py", "reports/json_report.py", "reports/templates/audit_report.html", "requirements.txt", "run_audit.sh", "utils.py" ], "file_count": 29, "download_size_bytes": 66634, "download_size_human": "65.1 KB", "product_url": "https://datanest-stores.com/data-engineering/#product-databricks-audit-toolkit", "preview_url": "https://datanest-stores.com/data-engineering/preview-databricks-audit-toolkit", "demo_url": "https://datanest-stores.com/data-engineering/demo-databricks-audit-toolkit/" }, { "product_id": "data-engineering:databricks-starter-kit", "slug": "databricks-starter-kit", "store": "data-engineering", "store_name": "DataStack Pro", "domain": "data", "category": "Data & Analytics", "tags": [ "cloud data", "data", "data engineering", "data pipelines", "databricks", "etl", "lakehouse", "spark" ], "title": "Databricks Starter Kit", "short_description": "Production-ready starter templates for building data platforms on Databricks with Unity Catalog and Delta Lake: workspace bootstrap, medallion layout, and job scaffolding.", "detailed_description": "Production-ready templates for building data platforms on Databricks with Unity Catalog and Delta Lake. Skip the months of trial and error. This kit gives you the same patterns and architecture used by data platform teams at scale \u2014 fully documented, customizable, and ready to deploy. ---", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Databricks", "Python", "SQL", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "/Repos//databricks-starter-kit/", "databricks repos create \\\n --url https://github.com/your-org/databricks-starter-kit \\\n --provider github" ], "included_files": [ "README.md", "cicd_templates/azure_devops_pipeline.yml", "cicd_templates/deploy_notebooks.py", "cicd_templates/github_actions_workflow.yml", "cicd_templates/run_tests.py", "config/environment.py", "config/logging_config.py", "config/secrets.py", "guide/01_what-s-inside.md", "guide/02_module-deep-dives.md", "guide/03_frequently-asked-questions.md", "ingestion_templates/api_ingestion.py", "ingestion_templates/base_pipeline.py", "ingestion_templates/database_ingestion.py", "ingestion_templates/file_ingestion.py", "ingestion_templates/streaming_ingestion.py", "medallion_bootstrap/01_create_catalogs.py", "medallion_bootstrap/02_create_schemas.py", "medallion_bootstrap/03_grant_permissions.py", "medallion_bootstrap/config.py", "unity_catalog_setup/data_governance_policies.md", "unity_catalog_setup/setup_catalogs.sql", "unity_catalog_setup/setup_credentials.sql", "unity_catalog_setup/setup_external_locations.sql" ], "file_count": 24, "download_size_bytes": 70235, "download_size_human": "68.6 KB", "product_url": "https://datanest-stores.com/data-engineering/#product-databricks-starter-kit", "preview_url": "https://datanest-stores.com/data-engineering/preview-databricks-starter-kit", "demo_url": "https://datanest-stores.com/data-engineering/demo-databricks-starter-kit/" }, { "product_id": "data-engineering:databricks-workspace-toolkit", "slug": "databricks-workspace-toolkit", "store": "data-engineering", "store_name": "DataStack Pro", "domain": "data", "category": "Data & Analytics", "tags": [ "cloud data", "data", "data engineering", "data pipelines", "databricks", "etl", "lakehouse", "spark" ], "title": "Databricks Workspace Toolkit", "short_description": "Automate Databricks workspace management including clusters, jobs, secrets, and permissions.", "detailed_description": "Automate Databricks workspace management \u2014 clusters, jobs, secrets, Unity Catalog, and permissions. Stop clicking through the UI. Manage your entire Databricks workspace programmatically with production-ready Python wrappers around the Databricks REST APIs. --- Includes: Workspace management \u2014 List, create, delete, import/export notebooks programmatically; Cluster automation \u2014 Create clusters, resize, manage pools, enforce auto-termination policies; Job orchestration \u2014 Create multi-task workflows, manage schedules, configure notifications; Secret management \u2014 Create scopes, store secrets, manage ACLs for secure credential handling; Unity Catalog setup \u2014 Bootstrap catalogs, schemas, tables, grants, and external locations; Permissions manager \u2014 Configure RBAC for clusters, jobs, notebooks, and SQL warehouses.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "requests library", "pyyaml library", "Databricks workspace with admin or workspace-level access", "Personal access token or service principal credentials" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Databricks", "Python", "Shell", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "# configs/workspace_config.yaml\nworkspace:\n host: \"https://adb-1234567890.12.azuredatabricks.net\"\n token_env_var: \"DATABRICKS_TOKEN\"", "from src.cluster_manager import ClusterManager\n\nmgr = ClusterManager.from_config(\"configs/workspace_config.yaml\")\ncluster_id = mgr.create_cluster(\n name=\"etl-cluster-prod\",\n node_type_id=\"Standard_DS3_v2\",\n num_workers=4,\n auto_terminate_minutes=30,\n spark_conf={\"spark.sql.shuffle.partitions\": \"200\"},\n)" ], "included_files": [ "README.md", "config.example.yaml", "configs/cluster_policies.json", "configs/job_templates/etl_job.json", "configs/job_templates/ml_training_job.json", "configs/workspace_config.yaml", "guide/01_what-you-get.md", "guide/02_getting-started.md", "guide/03_requirements.md", "guides/workspace-management.md", "notebooks/admin_dashboard.py", "pyproject.toml", "scripts/export_workspace.sh", "scripts/setup_workspace.sh", "src/cluster_manager.py", "src/job_manager.py", "src/permissions_manager.py", "src/secret_manager.py", "src/unity_catalog_setup.py", "src/workspace_manager.py" ], "file_count": 20, "download_size_bytes": 57680, "download_size_human": "56.3 KB", "product_url": "https://datanest-stores.com/data-engineering/#product-databricks-workspace-toolkit", "preview_url": "https://datanest-stores.com/data-engineering/preview-databricks-workspace-toolkit", "demo_url": "https://datanest-stores.com/data-engineering/demo-databricks-workspace-toolkit/" }, { "product_id": "data-engineering:delta-lake-patterns", "slug": "delta-lake-patterns", "store": "data-engineering", "store_name": "DataStack Pro", "domain": "data", "category": "Data & Analytics", "tags": [ "cloud data", "data", "data engineering", "data pipelines", "databricks", "etl", "lakehouse", "spark" ], "title": "Delta Lake Patterns", "short_description": "Production-ready Delta Lake merge, optimization, and maintenance patterns for Databricks.", "detailed_description": "Production-ready Delta Lake merge, optimization, and maintenance patterns for Databricks. Master the full spectrum of Delta Lake operations \u2014 from SCD Type 2 merges to Liquid Clustering migration, Change Data Feed processing, and automated table maintenance. --- Includes: 5 merge strategies \u2014 SCD1, SCD2, upsert, delete+insert, and conditional merge with full PySpark implementations; Table optimization toolkit \u2014 OPTIMIZE, ZORDER, vacuum scheduling, and ANALYZE TABLE automation; Time travel operations \u2014 Version history queries, point-in-time restore, and audit trail generation; Change Data Feed processing \u2014 Incremental CDF readers with watermark tracking and replay support; Liquid Clustering \u2014 Setup, migration from ZORDER, and monitoring utilities; Table utilities \u2014 Clone, convert-to-delta, property management, and schema inspection.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Databricks Runtime 13.3 LTS or later", "Delta Lake 2.4+", "Python 3.10+", "PyYAML", "Unity Catalog recommended for table management" ], "supported_versions": [ "Python 3.10+", "Databricks Runtime 13.3" ], "supported_technologies": [ "Databricks", "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "from src.merge_patterns import scd2_merge\n\nscd2_merge(\n target_table=\"catalog.silver.dim_customer\",\n source_df=incoming_customers,\n merge_keys=[\"customer_id\"],\n tracked_columns=[\"email\", \"address\", \"phone\"],\n effective_date_col=\"updated_at\",\n)", "from src.optimization import run_maintenance_schedule\nimport yaml\n\nwith open(\"configs/table_maintenance.yaml\") as f:\n config = yaml.safe_load(f)\n\nrun_maintenance_schedule(config[\"tables\"])" ], "included_files": [ "README.md", "config.example.yaml", "configs/table_maintenance.yaml", "configs/table_properties.yaml", "docs/checklists/pre-deployment.md", "docs/overview.md", "guide/01_what-you-get.md", "guide/02_getting-started.md", "guide/03_requirements.md", "guides/delta-lake-best-practices.md", "notebooks/cdf_processor.py", "notebooks/maintenance_runner.py", "notebooks/setup_tables.py", "src/change_data_feed.py", "src/liquid_clustering.py", "src/merge_patterns.py", "src/optimization.py", "src/table_utilities.py", "src/time_travel.py", "templates/config.yaml", "tests/conftest.py", "tests/test_merge_patterns.py" ], "file_count": 22, "download_size_bytes": 69395, "download_size_human": "67.8 KB", "product_url": "https://datanest-stores.com/data-engineering/#product-delta-lake-patterns", "preview_url": "https://datanest-stores.com/data-engineering/preview-delta-lake-patterns", "demo_url": "https://datanest-stores.com/data-engineering/demo-delta-lake-patterns/" }, { "product_id": "data-engineering:medallion-architecture-guide", "slug": "medallion-architecture-guide", "store": "data-engineering", "store_name": "DataStack Pro", "domain": "data", "category": "Data & Analytics", "tags": [ "cloud data", "data", "data engineering", "data pipelines", "databricks", "etl", "lakehouse", "spark" ], "title": "Medallion Architecture Guide", "short_description": "A comprehensive decision framework and implementation guide for building production-grade medallion (bronze/silver/gold) architectures in Databricks.", "detailed_description": "A comprehensive decision framework and implementation guide for building production-grade medallion architectures in Databricks. Product: Medallion Architecture Guide Version: 1.0.0 Price: $19 Publisher: DataStack Pro License: MIT ---", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Databricks workspace", "Unity Catalog enabled", "PySpark knowledge", "Delta Lake basics", "Databricks Runtime 13.3 LTS or later", "Delta Lake 2.4+" ], "supported_versions": [ "Python 3.10+", "Databricks Runtime 13.3" ], "supported_technologies": [ "Databricks", "AWS", "Azure", "GCP", "Python" ], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md", "cheatsheets/layer_comparison.md", "cheatsheets/migration_checklist.md", "cheatsheets/naming_conventions_cheatsheet.md", "code_examples/bronze_ingestion.py", "code_examples/cross_layer_pipeline.py", "code_examples/gold_aggregation.py", "code_examples/naming_convention_generator.py", "code_examples/schema_migration.py", "code_examples/silver_transformation.py", "diagrams/data_flow.md", "diagrams/decision_tree.md", "diagrams/medallion_overview.md", "guide/01_introduction.md", "guide/02_decision_framework.md", "guide/03_bronze_layer.md", "guide/04_silver_layer.md", "guide/05_gold_layer.md", "guide/06_naming_conventions.md", "guide/07_schema_evolution.md", "guide/08_data_quality_gates.md", "guide/09_anti_patterns.md", "guide/10_reference_architectures.md" ], "file_count": 23, "download_size_bytes": 124404, "download_size_human": "121.5 KB", "product_url": "https://datanest-stores.com/data-engineering/#product-medallion-architecture-guide", "preview_url": "https://datanest-stores.com/data-engineering/preview-medallion-architecture-guide", "demo_url": "https://datanest-stores.com/data-engineering/demo-medallion-architecture-guide/" }, { "product_id": "data-engineering:pyspark-utils-library", "slug": "pyspark-utils-library", "store": "data-engineering", "store_name": "DataStack Pro", "domain": "data", "category": "Data & Analytics", "tags": [ "cloud data", "data", "data engineering", "data pipelines", "databricks", "etl", "lakehouse", "spark" ], "title": "PySpark Utils Library", "short_description": "Battle-tested PySpark utility functions for transformations, data quality, SCD, schema evolution, and lineage.", "detailed_description": "Battle-tested utility functions for PySpark data engineering \u2014 transformations, data quality, SCD, schema evolution, logging, dedup, and DataFrame diffing. Stop rewriting the same PySpark boilerplate on every project. This library gives you the production-ready building blocks that data engineering teams use daily \u2014 fully typed, tested, and documented. ---", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Apache Spark", "Databricks", "Python" ], "compatibility_notes": null, "preview_snippets": [ "pip install pyspark-utils-library", "pip install -e ." ], "included_files": [ "README.md", "examples/usage_examples.py", "guide/01_what-s-inside.md", "guide/02_module-deep-dives.md", "guide/03_testing.md", "guide/04_support.md", "pyproject.toml", "pyspark_utils/__init__.py", "pyspark_utils/data_quality.py", "pyspark_utils/dedup.py", "pyspark_utils/diff.py", "pyspark_utils/logging_utils.py", "pyspark_utils/scd.py", "pyspark_utils/schema_utils.py", "pyspark_utils/transformations.py", "setup.py", "tests/__init__.py", "tests/conftest.py", "tests/test_data_quality.py", "tests/test_dedup.py", "tests/test_diff.py", "tests/test_scd.py", "tests/test_schema_utils.py", "tests/test_transformations.py" ], "file_count": 24, "download_size_bytes": 154240, "download_size_human": "150.6 KB", "product_url": "https://datanest-stores.com/data-engineering/#product-pyspark-utils-library", "preview_url": "https://datanest-stores.com/data-engineering/preview-pyspark-utils-library", "demo_url": "https://datanest-stores.com/data-engineering/demo-pyspark-utils-library/" }, { "product_id": "data-engineering:schema-evolution-toolkit", "slug": "schema-evolution-toolkit", "store": "data-engineering", "store_name": "DataStack Pro", "domain": "data", "category": "Data & Analytics", "tags": [ "cloud data", "data", "data engineering", "data pipelines", "databricks", "etl", "lakehouse", "spark" ], "title": "Schema Evolution Toolkit", "short_description": "Detect, validate, migrate, and analyze schema changes across Delta Lake tables safely and automatically.", "detailed_description": "Detect, validate, migrate, and analyze schema changes across Delta Lake tables \u2014 safely and automatically. By Datanest Digital | Version 2.0.0 | $39 --- Includes: Schema Detector \u2014 compare live table schemas against expected definitions, detect drift; Schema Diff \u2014 detailed field-level diff between any two schema versions (no Spark required); Schema Migrator \u2014 apply safe migrations (add columns, widen types, rename) with rollback; Migration Generator \u2014 auto-generate SQL forward, rollback, and Python migration scripts; Impact Analyzer \u2014 check which pipelines, views, and dashboards break before deploying; Compatibility Checker \u2014 verify backward/forward compatibility before deploying changes.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "2.0.0", "changelog_at": "2026-07-01", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.9+", "Schema Diff, Migration Generator, Impact Analyzer", "Schema Detector, Migrator, Registry, Validator", "Schema Registry", "Policy config loading" ], "supported_versions": [ "Python 3.9+", "Databricks Runtime 13.3" ], "supported_technologies": [ "Apache Spark", "Databricks", "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "from schema_detector import SchemaDetector\n\ndetector = SchemaDetector(spark)\n\n# Compare a live table against an expected JSON schema\nreport = detector.compare_table_to_schema(\n table_name=\"catalog.bronze.customers\",\n expected_schema_path=\"/Volumes/schemas/v2_customer.json\",\n)\n\nprint(report.summary())\n# SchemaReport: 2 added columns, 1 type change, 0 removed columns", "from schema_diff import SchemaDiff\n\ndiffer = SchemaDiff()\ndiff = differ.diff_from_files(\"v1_customer.json\", \"v2_customer.json\")\n\n# Print a categorized report\nprint(diff.to_text())\n\n# Get Markdown for PR comments\nprint(diff.to_markdown())\n\n# Check for breaking changes\nif diff.has_breaking_changes:\n for bc in diff.breaking_changes:\n\u2026" ], "included_files": [ "README.md", "configs/schema_policy.yaml", "configs/schemas/v1_customer.json", "configs/schemas/v1_order.json", "configs/schemas/v2_customer.json", "configs/schemas/v2_order.json", "configs/schemas/v3_customer.json", "guide/01_what-you-get.md", "guide/02_getting-started.md", "guide/03_configuration.md", "guide/04_license.md", "guides/schema-evolution-strategy.md", "notebooks/detect_drift.py", "notebooks/evolve_schema.py", "notebooks/generate_migration.py", "src/compatibility_checker.py", "src/impact_analyzer.py", "src/migration_generator.py", "src/schema_detector.py", "src/schema_diff.py", "src/schema_migrator.py", "src/schema_registry.py", "src/schema_validator.py", "tests/conftest.py", "tests/test_compatibility.py", "tests/test_impact_analyzer.py", "tests/test_migration_generator.py", "tests/test_schema_detector.py", "tests/test_schema_diff.py" ], "file_count": 29, "download_size_bytes": 94230, "download_size_human": "92.0 KB", "product_url": "https://datanest-stores.com/data-engineering/#product-schema-evolution-toolkit", "preview_url": "https://datanest-stores.com/data-engineering/preview-schema-evolution-toolkit", "demo_url": "https://datanest-stores.com/data-engineering/demo-schema-evolution-toolkit/" }, { "product_id": "data-engineering:spark-etl-framework", "slug": "spark-etl-framework", "store": "data-engineering", "store_name": "DataStack Pro", "domain": "data", "category": "Data & Analytics", "tags": [ "cloud data", "data", "data engineering", "data pipelines", "databricks", "etl", "lakehouse", "spark" ], "title": "Spark ETL Framework", "short_description": "Production-ready medallion architecture ETL framework for Databricks and Apache Spark.", "detailed_description": "Production-ready medallion architecture ETL framework for Databricks and Apache Spark. Build reliable, observable, and maintainable data pipelines with a battle-tested extract-transform-load pattern that scales from prototype to petabyte. --- Includes: Abstract ETL base class with built-in logging, metrics collection, and error handling; Medallion architecture (Bronze / Silver / Gold) with production patterns baked in; Data quality gates between every layer \u2014 catch issues before they propagate; YAML-driven configuration with environment overrides and secret scope integration; Source extractors for JDBC databases, file systems, and REST APIs with pagination and retry logic; SCD handler for Type 1 and Type 2 slowly changing dimension merges with soft deletes.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Databricks Runtime 13.3 LTS or later", "Python 3.10+", "Delta Lake", "PyYAML for configuration management" ], "supported_versions": [], "supported_technologies": [ "Apache Spark", "Databricks", "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "pipeline:\n name: \"customer_orders\"\n schedule: \"0 6 * * *\"\n\nsource:\n format: \"json\"\n path: \"/mnt/raw/customer_orders/\"\n\ndestination:\n database: \"analytics\"\n table: \"customer_orders\"", "from src.config_manager import ConfigManager\nfrom src.bronze_loader import BronzeLoader\nfrom src.silver_transformer import SilverTransformer\nfrom src.gold_aggregator import GoldAggregator\n\nconfig = ConfigManager(\"configs/pipeline_config.yaml\")\n\n# Bronze: ingest raw data\nbronze = BronzeLoader(config)\nbronze_df = bronze.run()\n\n# Silver: clean and deduplicate\nsilver = SilverTransformer(config)\nsilver_df = silver.run()\n\u2026" ], "included_files": [ "README.md", "configs/pipeline_config.yaml", "configs/quality_rules.yaml", "guide/01_what-you-get.md", "guide/02_source-extractors.md", "guide/03_requirements.md", "guide/04_faq.md", "guides/etl-patterns.md", "notebooks/backfill.py", "notebooks/run_pipeline.py", "src/bronze_loader.py", "src/config_manager.py", "src/etl_base.py", "src/extractors/__init__.py", "src/extractors/api_extractor.py", "src/extractors/file_extractor.py", "src/extractors/jdbc_extractor.py", "src/gold_aggregator.py", "src/quality_gate.py", "src/silver_transformer.py", "src/transformers/__init__.py", "src/transformers/deduplication.py", "src/transformers/scd_handler.py", "src/utils/__init__.py", "src/utils/lineage_tracker.py", "src/utils/metrics_collector.py", "tests/conftest.py", "tests/test_etl_base.py", "tests/test_extractors.py" ], "file_count": 29, "download_size_bytes": 108437, "download_size_human": "105.9 KB", "product_url": "https://datanest-stores.com/data-engineering/#product-spark-etl-framework", "preview_url": "https://datanest-stores.com/data-engineering/preview-spark-etl-framework", "demo_url": "https://datanest-stores.com/data-engineering/demo-spark-etl-framework/" }, { "product_id": "data-engineering:spark-performance-masterclass", "slug": "spark-performance-masterclass", "store": "data-engineering", "store_name": "DataStack Pro", "domain": "data", "category": "Data & Analytics", "tags": [ "cloud data", "data", "data engineering", "data pipelines", "databricks", "etl", "lakehouse", "spark" ], "title": "Spark Performance Masterclass", "short_description": "Definitive guide to optimizing Apache Spark performance on Databricks with 25+ patterns.", "detailed_description": "The definitive guide to squeezing every drop of performance from Apache Spark on Databricks. ![Price](https://datastack.pro) ![Spark](https://spark.apache.org) ![Databricks](https://databricks.com) ![Delta Lake](https://delta.io) ![License: MIT](LICENSE) ---", "price_usd": 49, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Working knowledge of PySpark or Spark SQL", "Access to a Databricks workspace", "Familiarity with Delta Lake basics", "Python" ], "supported_versions": [], "supported_technologies": [ "Apache Spark", "Databricks", "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "Recommended reading order:\n Ch 1 (Execution Model) -> Ch 3 (Shuffles) -> Ch 7 (Joins) -> Ch 4 (Memory)\n -> Ch 2 (AQE) -> Ch 8 (I/O) -> Ch 5 (Delta) -> Ch 6 (Photon)\n -> Ch 9 (Streaming) -> Ch 10 (Troubleshooting Runbook)", "# On a Databricks cluster with DBR 14+\n# Upload the benchmarks/ directory to your workspace\n\n# Run the benchmark suite\nfrom benchmark_runner import BenchmarkRunner\n\nrunner = BenchmarkRunner(spark, output_path=\"/tmp/benchmark_results\")\nresults = runner.run_all()\nrunner.save_results(results)" ], "included_files": [ "README.md", "benchmarks/README.md", "benchmarks/analysis/visualize_results.py", "benchmarks/benchmark_configs.yaml", "benchmarks/benchmark_runner.py", "benchmarks/benchmarks/__init__.py", "benchmarks/benchmarks/aggregation_benchmarks.py", "benchmarks/benchmarks/delta_benchmarks.py", "benchmarks/benchmarks/io_benchmarks.py", "benchmarks/benchmarks/join_benchmarks.py", "cheatsheets/delta_optimization_cheatsheet.md", "cheatsheets/spark_config_cheatsheet.md", "cheatsheets/troubleshooting_flowchart.md", "configs/README.md", "configs/config_validator.py", "configs/large_workload.py", "configs/medium_workload.py", "configs/small_workload.py", "configs/streaming_workload.py", "guide/01_spark_execution_model.md", "guide/02_aqe_deep_dive.md", "guide/03_shuffle_optimization.md", "guide/04_memory_tuning.md", "guide/05_delta_lake_optimization.md", "guide/06_photon_engine.md", "guide/07_join_strategies.md", "guide/08_io_optimization.md", "guide/09_streaming_performance.md", "guide/10_troubleshooting_runbook.md", "spark_ui_guide/README.md", "spark_ui_guide/common_patterns.md", "spark_ui_guide/sql_tab.md", "spark_ui_guide/stages_tab.md", "spark_ui_guide/storage_tab.md" ], "file_count": 34, "download_size_bytes": 296574, "download_size_human": "289.6 KB", "product_url": "https://datanest-stores.com/data-engineering/#product-spark-performance-masterclass", "preview_url": "https://datanest-stores.com/data-engineering/preview-spark-performance-masterclass", "demo_url": "https://datanest-stores.com/data-engineering/demo-spark-performance-masterclass/" }, { "product_id": "data-engineering:streaming-pipeline-kit", "slug": "streaming-pipeline-kit", "store": "data-engineering", "store_name": "DataStack Pro", "domain": "data", "category": "Data & Analytics", "tags": [ "cloud data", "data", "data engineering", "data pipelines", "databricks", "etl", "lakehouse", "spark" ], "title": "Streaming Pipeline Kit", "short_description": "Real-time data pipelines with Spark Structured Streaming, Kafka integration, and monitoring.", "detailed_description": "Real-time data pipelines that just work. Production-ready Spark Structured Streaming templates with Kafka integration, exactly-once semantics, and Delta Lake sinks. 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Enforce schema guarantees, SLA compliance, and breaking change detection with a structured, version-controlled approach to data ownership.", "price_usd": 59, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.9+", "PyYAML >= 6.0", "jsonschema >= 4.0", "Databricks Runtime 13.0+", "Delta Lake" ], "supported_versions": [ "Python 3.9+", "Databricks Runtime 13.0" ], "supported_technologies": [ "Databricks", "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "contract:\n name: customer_events\n version: 1.0.0\n owner:\n team: data-engineering\n contact: team-lead@company.com\n schema:\n fields:\n - name: event_id\n type: string\n required: true\n description: Unique event identifier\n - name: event_timestamp\n type: timestamp\n\u2026", "python cli/contract_generator.py \\\n --catalog main \\\n --schema analytics \\\n --table customer_events \\\n --output spec/example_contracts/" ], "included_files": [ "README.md", "cli/contract_generator.py", "cli/contract_validator.py", "guide/implementation_guide.md", "guides/implementation_guide.md", "notebooks/breaking_change_detector.py", "notebooks/contract_compliance_dashboard.py", "notebooks/sla_monitoring.py", "registry/contract_registry.py", "spec/contract_schema.yaml", "spec/example_contracts/customer_events.yaml", "spec/example_contracts/financial_transactions.yaml", "spec/example_contracts/product_catalog.yaml", "templates/contract_templates/api_source.yaml", "templates/contract_templates/database_source.yaml", "templates/contract_templates/file_source.yaml", "templates/producer_consumer_agreement.md" ], "file_count": 17, "download_size_bytes": 100100, "download_size_human": "97.8 KB", "product_url": "https://datanest-stores.com/datanest-platform-pro/#product-data-contract-framework", "preview_url": "https://datanest-stores.com/datanest-platform-pro/preview-data-contract-framework", "demo_url": "https://datanest-stores.com/datanest-platform-pro/demo-data-contract-framework/" }, { "product_id": "datanest-platform-pro:medallion-architecture-accelerator", "slug": "medallion-architecture-accelerator", "store": "datanest-platform-pro", "store_name": "Datanest Platform Pro", "domain": "data", "category": "Data & Analytics", "tags": [ "architecture-guide", "azure", "bronze-silver-gold", "cheatsheets", "ci/cd", "data", "data mesh", "data platform", "data-engineering", "data-quality", "databricks", "delta lake", "delta-lake", "etl", "medallion-architecture", "naming-conventions" ], "title": "Medallion Architecture Accelerator", "short_description": "Production implementation of Bronze/Silver/Gold architecture on Databricks with metadata-driven pipeline generator and quality gates.", "detailed_description": "By Datanest Digital | Version 2.0.0 | $79 Production-ready framework for implementing the Medallion Architecture (Bronze / Silver / Gold) on Databricks with Delta Lake. Eliminate weeks of boilerplate and enforce best practices from day one. v2.0 adds a comprehensive 10-chapter implementation guide, quick-reference cheatsheets, architecture diagrams, plus naming convention and schema migration utilities. ---", "price_usd": 79, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "2.0.0", "changelog_at": "2026-03-12", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Databricks Runtime 13.3 LTS or later", "Delta Lake 2.4+", "Unity Catalog", "Python 3.10+", "PySpark 3.4+" ], "supported_versions": [ "Python 3.10+", "Databricks Runtime 13.3" ], "supported_technologies": [ "Databricks", "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "# config/sources/orders.yaml\nsource:\n name: orders\n type: auto_loader\n format: json\n path: \"s3://raw-bucket/orders/\"\n schema_hints:\n order_id: long\n customer_id: 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"governance/federated_governance_model.md", "governance/platform_team_responsibilities.md", "guide/01_overview.md", "guide/02_getting-started.md", "guide/03_customization.md", "guide/04_support.md", "templates/data_product_scorecard.md", "templates/data_product_template.py", "terraform/domain-workspace/main.tf", "terraform/domain-workspace/outputs.tf", "terraform/domain-workspace/variables.tf", "workshops/domain_decomposition.md", "workshops/stakeholder_alignment.md" ], "file_count": 19, "download_size_bytes": 132751, "download_size_human": "129.6 KB", "product_url": "https://datanest-stores.com/datanest-platform-pro/#product-data-mesh-starter-kit", "preview_url": "https://datanest-stores.com/datanest-platform-pro/preview-data-mesh-starter-kit", "demo_url": "https://datanest-stores.com/datanest-platform-pro/demo-data-mesh-starter-kit/" }, { "product_id": "datanest-platform-pro:real-time-streaming-toolkit", "slug": "real-time-streaming-toolkit", "store": "datanest-platform-pro", "store_name": "Datanest Platform Pro", "domain": "data", "category": "Data & Analytics", "tags": [ "auto-loader", "azure", "cdc", "ci/cd", "data", "data mesh", "data platform", "databricks", "delta lake", "delta-live-tables", "event-hubs", "kafka", "medallion-architecture", "monitoring", "pyspark", "spark" ], "title": "Real-Time Streaming Toolkit", "short_description": "Production streaming patterns for Structured Streaming and Delta Live Tables with Kafka/Event Hub integration and monitoring dashboards.", "detailed_description": "Production streaming patterns for Structured Streaming and Delta Live Tables with Kafka/Event Hub integration and monitoring dashboards.", "price_usd": 69, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Databricks Runtime 13.3 LTS or later", "Unity Catalog enabled workspace", "Apache Spark 3.4+", "Delta Lake 2.4+", "Python" ], "supported_versions": [ "Databricks Runtime 13.3" ], "supported_technologies": [ "Apache Spark", "Databricks", "Kafka", "Azure", "Python", "SQL" ], "compatibility_notes": null, "preview_snippets": [ "Source (Kafka / Event Hubs / Files)\n |\n v\n [Bronze Layer] -- raw ingestion, append-only, schema-on-read\n |\n v\n [Silver Layer] -- cleansed, deduplicated, typed, validated\n |\n v\n [Gold Layer] -- aggregated, business-ready, optimized for queries\n |\n v\n Delta Tables in Unity Catalog" ], "included_files": [ "README.md", "config/autoscaling_config.md", "config/checkpoint_management.md", "dlt/cdc_processing.py", "dlt/expectations_library.py", "dlt/streaming_medallion.py", "guide/failure_recovery.md", "guide/performance_tuning.md", "guides/failure_recovery.md", "guides/performance_tuning.md", "monitoring/streaming_alerts.sql", "monitoring/streaming_dashboard.sql", 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framework with extractors for 8 source types (SQL Server, PostgreSQL, Oracle, MySQL, files, APIs), validation, and cutover runbooks.", "detailed_description": "Industrialized migration framework with extractors for 8 source types (SQL Server, PostgreSQL, Oracle, MySQL, files, APIs), validation, and cutover runbooks.", "price_usd": 79, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Databricks Runtime 13.3 LTS or later", "Unity Catalog enabled", "Python 3.10+", "Network connectivity to source systems" ], "supported_versions": [ "Python 3.10+", "Databricks Runtime 13.3" ], "supported_technologies": [ "Databricks", "Python", "SQL" ], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md", "assessment/migration_wave_planner.py", 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"datanest-platform-pro", "store_name": "Datanest Platform Pro", "domain": "data", "category": "Data & Analytics", "tags": [ "azure", "ci/cd", "data", "data mesh", "data platform", "databricks", "delta lake", "developer-experience", "devops", "enterprise", "internal-developer-platform", "operations", "platform-engineering", "spark", "sre", "team-management" ], "title": "Platform Team Playbook", "short_description": "Operating manual for data platform teams with structure templates, RACI generator, incident management, and scaling guide.", "detailed_description": "Product ID: platform-team-playbook Version: 1.0.0 Price: $59 Category: Enterprise Author: Datanest Digital Website: https://datanest.dev ---", "price_usd": 59, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": "2026-03-09", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": 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Automation Suite", "short_description": "Automated compliance for GDPR, SOC 2, ISO 27001, and NIS2. Evidence collection scripts, DPIA templates, and audit preparation guide.", "detailed_description": "Automated compliance for GDPR, SOC 2, ISO 27001, and NIS2. Evidence collection scripts, DPIA templates, and audit preparation guide.", "price_usd": 89, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "2.0.0", "changelog_at": "2026-03-12", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [ "Python 3.9+" ], "supported_technologies": [ "Python", "Shell", "SQL" ], "compatibility_notes": null, "preview_snippets": [ "# Set required environment variables\nexport DATABRICKS_HOST='https://your-workspace.cloud.databricks.com'\nexport DATABRICKS_TOKEN='dapi_YOUR_TOKEN_HERE'\nexport AUDIT_COMPANY_NAME='Acme Corp'\n\n# Run all 9 operational audits with both HTML and JSON output\nchmod +x run_audit.sh\n./run_audit.sh --all --format both", "# Run only the token inventory audit\n./run_audit.sh --audit token_inventory --format json\n\n# Run only compute analysis\n./run_audit.sh --audit compute_analysis --format html" ], "included_files": [ "README.md", "audits/__init__.py", "audits/catalog_inventory.py", "audits/compute_analysis.py", "audits/config.py", "audits/data_lineage.py", "audits/external_storage.py", "audits/job_inventory.py", "audits/query_history.py", "audits/secret_inventory.py", "audits/table_permissions.py", "audits/token_inventory.py", "audits/utils.py", "dashboards/compliance_dashboard.sql", "gdpr/consent_tracking.py", "gdpr/dpia_template.md", "gdpr/dsr_handler.py", "gdpr/ropa_generator.py", "guide/01_overview.md", "guide/02_quick-start.md", "guide/03_compliance-frameworks-covered.md", "guide/04_license.md", "mappings/gdpr_databricks_controls.md", "mappings/iso27001_controls.md", "mappings/nis2_checklist.md", "mappings/soc2_azure_controls.md", "output/.gitkeep", "reports/__init__.py", "reports/html_report.py", "reports/json_report.py", "reports/templates/audit_report.html", "run_audit.sh", "scripts/access_control_audit.py", "scripts/audit_log_validation.py", "scripts/data_retention_check.py", "scripts/encryption_verification.py", "scripts/network_security_check.py", "templates/audit_preparation.md", "templates/scheduling_guide.md", "templates/soc2_evidence_binder.md" ], "file_count": 40, "download_size_bytes": 271133, "download_size_human": "264.8 KB", "product_url": "https://datanest-stores.com/datanest-platform-pro/#product-compliance-automation-suite", "preview_url": "https://datanest-stores.com/datanest-platform-pro/preview-compliance-automation-suite", "demo_url": "https://datanest-stores.com/datanest-platform-pro/demo-compliance-automation-suite/" }, { "product_id": "datanest-platform-pro:databricks-disaster-recovery-kit", "slug": "databricks-disaster-recovery-kit", "store": "datanest-platform-pro", "store_name": "Datanest Platform Pro", "domain": "data", "category": "Data & Analytics", "tags": [ "azure", "business-continuity", "ci/cd", "data", "data mesh", "data platform", "databricks", "delta 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Includes: Generate Terraform configs for AWS-style infrastructure (provider-agnostic patterns); Scaffolds complete module structure; VPC/networking with public and private subnets; Compute instances with configurable size and count; RDS-style database resources with secure defaults; S3-compatible storage buckets with versioning and encryption.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Generate Terraform configs from the example spec\npython src/terraform_generator.py --config examples/infra_spec.json\n\n# Write to a target directory\npython src/terraform_generator.py --config examples/infra_spec.json --output-dir ./terraform/\n\n# Generate only specific components\npython src/terraform_generator.py --config examples/infra_spec.json --components vpc,compute\n\n# Validate config without generating\npython src/terraform_generator.py --config examples/infra_spec.json --validate-only" ], "included_files": [ "README.md", "examples/infra_spec.json", "guide/01_features.md", "guide/02_configuration-reference.md", "guide/03_generated-files.md", "src/terraform_generator.py" ], "file_count": 6, "download_size_bytes": 42052, "download_size_human": "41.1 KB", "product_url": "https://datanest-stores.com/deploy-kit/#product-terraform-starter", "preview_url": "https://datanest-stores.com/deploy-kit/preview-terraform-starter", "demo_url": "https://datanest-stores.com/deploy-kit/demo-terraform-starter/" }, { "product_id": "devops-toolkit:dockerfile-hardening-checklist", "slug": "dockerfile-hardening-checklist", "store": "devops-toolkit", "store_name": "DevOps Toolkit Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "checklist", "ci/cd", "cloud", "containers", "devops", "docker", "github actions", "hardening", "infrastructure as code", "kubernetes", "security", "terraform" ], "title": "Dockerfile Hardening Checklist", "short_description": "A do-this-now checklist to turn a typical Dockerfile into a small, secure, non-root, multi-stage production image, with a worked before/after and a .dockerignore starter.", "detailed_description": "A focused, do-this-now checklist for turning a typical Dockerfile into a small, secure, production-grade image \u2014 plus a fully worked before/after and a .dockerignore starter. 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Includes VPC networking, ECS Fargate compute, RDS databases, S3 storage, CloudFront CDN, and IAM \u2014 all wired together with remote state, locking, and a GitHub Actions pipeline. --- Includes: 6 Terraform modules \u2014 VPC, ECS, RDS, S3, IAM, CloudFront \u2014 each production-hardened; Environment configs \u2014 Dev and prod with isolated state and tuned defaults; Remote state \u2014 S3 + DynamoDB backend with encryption and locking; CI/CD pipeline \u2014 GitHub Actions workflow; Bootstrap script \u2014 One command to create state bucket and lock table; Best practices guide \u2014 1,500+ words on Terraform patterns, module design, and state management.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [], "supported_versions": [], "supported_technologies": [ "Terraform", "AWS", "Shell", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "# Create the S3 bucket and DynamoDB lock table\n./scripts/init-backend.sh my-org-tf-state us-east-1", "cp terraform.tfvars.example terraform.tfvars\n# Edit terraform.tfvars with your project name, region, CIDR, etc." ], "included_files": [ ".github/workflows/terraform.yml", "Makefile", "README.md", "backend.tf", "environments/dev/main.tf", "environments/prod/main.tf", "guide/01_terraform-best-practices.md", "guide/02_terraform-starter-kit.md", "guide/03_what-you-get.md", "guides/terraform-best-practices.md", "modules/cloudfront/main.tf", "modules/ecs/main.tf", "modules/iam/main.tf", "modules/rds/main.tf", "modules/s3/main.tf", "modules/vpc/main.tf", "outputs.tf", "scripts/init-backend.sh", "terraform.tfvars.example", "variables.tf" ], "file_count": 20, "download_size_bytes": 25383, "download_size_human": "24.8 KB", "product_url": "https://datanest-stores.com/devops-toolkit/#product-terraform-starter-kit", "preview_url": "https://datanest-stores.com/devops-toolkit/preview-terraform-starter-kit", "demo_url": "https://datanest-stores.com/devops-toolkit/demo-terraform-starter-kit/" }, { "product_id": "devops-toolkit:docker-compose-templates", "slug": "docker-compose-templates", "store": "devops-toolkit", "store_name": "DevOps Toolkit Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "ci/cd", "cloud", "devops", "docker", "github actions", "infrastructure as code", "kubernetes", "terraform" ], "title": "Docker Compose Templates", "short_description": "50+ production Docker Compose configurations for common stacks: LAMP, MEAN, Django, Rails, microservices.", "detailed_description": "Production-ready Docker Compose stacks for web apps, monitoring, logging, databases, and CI runners. 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Includes a Traefik reverse proxy with automatic TLS and a stack manager script. --- Includes: 7 ready-to-deploy stacks \u2014 Web app, monitoring, ELK, CI runner, database, WordPress, Traefik; Shared reverse proxy \u2014 Traefik with automatic Let's Encrypt certificates; Stack manager script \u2014 Deploy, stop, restart, and check status of any stack; Environment template \u2014 Documented; Patterns guide \u2014 Networking, volumes, health checks, secrets, and multi-service patterns.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [], "supported_versions": [], "supported_technologies": [ "Docker", "Redis", "Prometheus", "Grafana", "Shell", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "cp shared/.env.example .env\n# Edit .env with your domain, email, passwords, etc.", "docker compose -f shared/traefik/docker-compose.yml up -d" ], "included_files": [ "Dockerfile.templates/node-next", "Dockerfile.templates/python-fastapi", "README.md", "guide/01_docker-compose-patterns.md", "guide/02_docker-compose-templates.md", "guide/03_what-you-get.md", "guides/docker-compose-patterns.md", "scripts/stack-manager.sh", "shared/.env.example", "shared/traefik/docker-compose.yml", "stacks/ci-runner/docker-compose.yml", "stacks/database/docker-compose.yml", "stacks/django/docker-compose.yml", "stacks/elk/docker-compose.yml", "stacks/lamp/docker-compose.yml", "stacks/microservices/docker-compose.yml", "stacks/monitoring/docker-compose.yml", "stacks/nextjs/docker-compose.yml", "stacks/web-app/docker-compose.yml", "stacks/wordpress/docker-compose.yml" ], "file_count": 20, "download_size_bytes": 25961, "download_size_human": "25.4 KB", "product_url": "https://datanest-stores.com/devops-toolkit/#product-docker-compose-templates", "preview_url": "https://datanest-stores.com/devops-toolkit/preview-docker-compose-templates", "demo_url": "https://datanest-stores.com/devops-toolkit/demo-docker-compose-templates/" }, { "product_id": "devops-toolkit:kubernetes-manifests-pack", "slug": "kubernetes-manifests-pack", "store": "devops-toolkit", "store_name": "DevOps Toolkit Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "ci/cd", "cloud", "devops", "docker", "github actions", "infrastructure as code", "kubernetes", "terraform" ], "title": "Kubernetes Manifests Pack", "short_description": "Complete K8s manifest library with deployments, services, ingress, RBAC, network policies, and Helm charts.", "detailed_description": "Production-ready Kubernetes manifests with Kustomize overlays, Helm values, batch jobs, and Prometheus monitoring. Deploy a complete application stack to any Kubernetes cluster. Includes base manifests following the app.kubernetes.io labeling convention, Kustomize overlays for dev/prod, Helm value files, database migration and cleanup jobs, and Prometheus ServiceMonitor + alerting rules. --- Includes: 9 base manifests \u2014 Namespace, Deployment, Service, Ingress, HPA, PDB, NetworkPolicy, ConfigMap, Secret; Kustomize overlays \u2014 Dev and production variants with environment-specific patches; Helm value files \u2014 Chart with dev and prod overrides for flexible templating; Batch jobs \u2014 Database migration Job and scheduled cleanup CronJob; Monitoring \u2014 Prometheus ServiceMonitor and alerting rules; RBAC & network policies \u2014 Least-privilege Role and default-deny network policy.", "price_usd": 49, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [], "supported_versions": [], "supported_technologies": [ "Prometheus", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "# Create the namespace first\nkubectl apply -f base/namespace.yaml\n\n# Apply all base resources\nkubectl apply -f base/\n\n# Apply RBAC and network policies\nkubectl apply -f rbac/\nkubectl apply -f network-policies/", "# Deploy to dev\nkubectl apply -k overlays/dev/\n\n# Deploy to production\nkubectl apply -k overlays/prod/" ], "included_files": [ "README.md", "base/configmap.yaml", "base/deployment.yaml", "base/hpa.yaml", "base/ingress.yaml", "base/namespace.yaml", "base/networkpolicy.yaml", "base/pdb.yaml", "base/secret.yaml", "base/service.yaml", "guide/01_kubernetes-deployment-patterns.md", "guide/02_kubernetes-manifests-pack.md", "guide/03_what-you-get.md", "guides/kubernetes-deployment-patterns.md", "helm/Chart.yaml", "helm/values-dev.yaml", "helm/values-prod.yaml", "helm/values.yaml", "jobs/cronjob-cleanup.yaml", "jobs/db-migration.yaml", "monitoring/prometheus-rules.yaml", "monitoring/servicemonitor.yaml", "network-policies/default-deny.yaml", "overlays/dev/kustomization.yaml", "overlays/prod/kustomization.yaml", "rbac/role.yaml" ], "file_count": 26, "download_size_bytes": 29330, "download_size_human": "28.6 KB", "product_url": "https://datanest-stores.com/devops-toolkit/#product-kubernetes-manifests-pack", "preview_url": "https://datanest-stores.com/devops-toolkit/preview-kubernetes-manifests-pack", "demo_url": "https://datanest-stores.com/devops-toolkit/demo-kubernetes-manifests-pack/" }, { "product_id": "devops-toolkit:github-actions-workflows", "slug": "github-actions-workflows", "store": "devops-toolkit", "store_name": "DevOps Toolkit Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "ci/cd", "cloud", "devops", "docker", "github actions", "infrastructure as code", "kubernetes", "terraform" ], "title": "GitHub Actions Workflows", "short_description": "40+ reusable GitHub Actions workflows for CI/CD, testing, deployment, security scanning, and release automation.", "detailed_description": "Production-ready CI/CD workflows for Python, Node.js, Docker, Terraform, and repository maintenance. Drop these into your .github/workflows/ directory and customize the variables. Each workflow is self-contained with inline comments explaining every decision. Includes: 10 workflow files covering CI, deployment, Docker, releases, security, testing, and maintenance; 2 composite actions for reusable setup steps (Python environment, Docker build & push); 1 comprehensive guide on GitHub Actions patterns and best practices (1500+ words); All workflows use current action versions (v4/v5) and follow GitHub's security recommendations.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [], "supported_versions": [ "Python 3.10", "Node 18" ], "supported_technologies": [ "Terraform", "Docker", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "# Copy all workflows\ncp workflows/*.yml your-repo/.github/workflows/\n\n# Copy composite actions\ncp -r composite-actions/ your-repo/.github/composite-actions/", "# terraform-plan.yml \u2014 change these for your project\nenv:\n TF_VERSION: \"1.7.4\"\n TF_WORKING_DIR: \"terraform\"\n AWS_REGION: \"us-east-1\"" ], "included_files": [ "README.md", "composite-actions/docker-build-push/action.yml", "composite-actions/setup-python/action.yml", "guide/01_github-actions-patterns.md", "guide/02_github-actions-workflows.md", "guide/03_what-you-get.md", "guides/github-actions-patterns.md", "workflows/ci.yml", "workflows/dependency-update.yml", "workflows/deploy-staging.yml", "workflows/docker-build.yml", "workflows/node-test.yml", "workflows/python-test.yml", "workflows/release.yml", "workflows/security-scan.yml", "workflows/stale-issues.yml", "workflows/terraform-plan.yml" ], "file_count": 17, "download_size_bytes": 22828, "download_size_human": "22.3 KB", "product_url": "https://datanest-stores.com/devops-toolkit/#product-github-actions-workflows", "preview_url": "https://datanest-stores.com/devops-toolkit/preview-github-actions-workflows", "demo_url": "https://datanest-stores.com/devops-toolkit/demo-github-actions-workflows/" }, { "product_id": "devops-toolkit:ansible-playbook-collection", "slug": "ansible-playbook-collection", "store": "devops-toolkit", "store_name": "DevOps Toolkit Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "ci/cd", "cloud", "devops", "docker", "github actions", "infrastructure as code", "kubernetes", "terraform" ], "title": "Ansible Playbook Collection", "short_description": "Server provisioning and configuration playbooks for web servers, databases, monitoring, and security hardening.", "detailed_description": "Production-ready playbooks for server provisioning, Docker, Nginx, PostgreSQL, monitoring, and security hardening. Drop-in playbooks for Ubuntu 22.04+ servers. Each playbook is self-contained with inline comments, uses fully qualified collection names (FQCN), and follows Ansible best practices for idempotency and security. Includes: 6 production playbooks covering the full server lifecycle from bare metal to hardened production; 1 reusable role (common) with shared tasks, defaults, and handlers; Multi-environment inventory with production and staging groups; Centralized variables in groupvars with sensible defaults; Comprehensive guide on Ansible best practices (1500+ words).", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [], "supported_versions": [], "supported_technologies": [ "Ansible", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "# Ubuntu/Debian\nsudo apt update && sudo apt install -y ansible\n\n# macOS\nbrew install ansible\n\n# pip (any platform)\npip install ansible", "all:\n children:\n production:\n children:\n prod_web:\n hosts:\n web-prod-01:\n ansible_host: 10.0.1.10 # <-- Your server IP" ], "included_files": [ "README.md", "ansible.cfg", "config.example.yaml", "group_vars/all.yml", "guide/01_ansible-best-practices.md", "guide/02_ansible-playbook-collection.md", "guide/03_what-you-get.md", "guides/ansible-best-practices.md", "inventory/hosts.yml", "playbooks/docker-install.yml", "playbooks/monitoring-setup.yml", "playbooks/nginx-setup.yml", "playbooks/postgres-setup.yml", "playbooks/security-hardening.yml", "playbooks/server-setup.yml", "roles/common/defaults/main.yml", "roles/common/handlers/main.yml", "roles/common/tasks/main.yml" ], "file_count": 18, "download_size_bytes": 29046, "download_size_human": "28.4 KB", "product_url": "https://datanest-stores.com/devops-toolkit/#product-ansible-playbook-collection", "preview_url": "https://datanest-stores.com/devops-toolkit/preview-ansible-playbook-collection", "demo_url": "https://datanest-stores.com/devops-toolkit/demo-ansible-playbook-collection/" }, { "product_id": "devops-toolkit:nginx-config-templates", "slug": "nginx-config-templates", "store": "devops-toolkit", "store_name": "DevOps Toolkit Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "ci/cd", "cloud", "devops", "docker", "github actions", "infrastructure as code", "kubernetes", "terraform" ], "title": "Nginx Config Templates", "short_description": "Battle-tested Nginx configurations for reverse proxy, load balancing, SSL termination, rate limiting, and caching.", "detailed_description": "Production-ready Nginx configurations for every use case. Stop copy-pasting from StackOverflow. Ship secure, optimized Nginx configs in minutes. ![License: MIT](LICENSE) ![Nginx](https://nginx.org/) ![Price](https://datanest.dev) --- Includes: Main Nginx config with security hardening, gzip compression, and structured logging; 5 site configurations; 4 reusable snippets; Automation scripts; Performance tuning guide with real-world recommendations.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Nginx 1.24+", "OpenSSL 1.1.1+", "Certbot", "Ubuntu 22.04+ / Debian 12+ / RHEL 9+" ], "supported_versions": [], "supported_technologies": [ "Shell" ], "compatibility_notes": null, "preview_snippets": [ "# Back up your existing config\nsudo cp /etc/nginx/nginx.conf /etc/nginx/nginx.conf.bak\n\n# Install the new main config\nsudo cp configs/nginx.conf /etc/nginx/nginx.conf", "# Example: Set up a reverse proxy\nsudo cp configs/sites/reverse-proxy.conf /etc/nginx/sites-available/myapp.conf\n\n# Edit the server_name and proxy_pass values\nsudo nano /etc/nginx/sites-available/myapp.conf\n\n# Enable the site\nsudo ln -s /etc/nginx/sites-available/myapp.conf /etc/nginx/sites-enabled/" ], "included_files": [ "README.md", "configs/nginx.conf", "configs/sites/api-gateway.conf", "configs/sites/reverse-proxy.conf", "configs/sites/spa-app.conf", "configs/sites/static-site.conf", "configs/sites/wordpress.conf", "configs/snippets/caching.conf", "configs/snippets/rate-limiting.conf", "configs/snippets/security-headers.conf", "configs/snippets/ssl-params.conf", "guide/01_nginx-performance-tuning.md", "guide/02_nginx-config-templates.md", "guide/03_what-you-get.md", "guides/nginx-performance-tuning.md", "scripts/certbot-setup.sh", "scripts/nginx-test.sh" ], "file_count": 17, "download_size_bytes": 29041, "download_size_human": "28.4 KB", "product_url": "https://datanest-stores.com/devops-toolkit/#product-nginx-config-templates", "preview_url": "https://datanest-stores.com/devops-toolkit/preview-nginx-config-templates", "demo_url": "https://datanest-stores.com/devops-toolkit/demo-nginx-config-templates/" }, { "product_id": "devops-toolkit:monitoring-stack-setup", "slug": "monitoring-stack-setup", "store": "devops-toolkit", "store_name": "DevOps Toolkit Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "ci/cd", "cloud", "devops", "docker", "github actions", "infrastructure as code", "kubernetes", "terraform" ], "title": "Monitoring Stack Setup", "short_description": "Complete Prometheus + Grafana + Alertmanager setup with dashboards, alert rules, and service discovery configs.", "detailed_description": "Complete observability stack with Prometheus, Grafana, Loki, and AlertManager. One docker compose up to full production monitoring. Stop flying blind. ![License: MIT](LICENSE) ![Docker](https://docs.docker.com/compose/) ![Price](https://datanest.dev) --- Includes: Docker Compose stack; 30+ alert rules covering infrastructure, containers, and application metrics; 3 Grafana dashboards; Log aggregation with Loki + Promtail (search logs in Grafana); One-command setup script with health checks; Monitoring strategy guide.", "price_usd": 49, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Docker 24+ and Docker Compose v2", "2 GB RAM minimum", "10 GB disk for metrics and log retention", "Ports" ], "supported_versions": [], "supported_technologies": [ "Docker", "Shell", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "cp .env.example .env\n# Edit .env with your settings (Slack webhook, PagerDuty key, etc.)\nnano .env", "# One-command setup (creates dirs, starts stack, waits for health)\n./scripts/setup.sh\n\n# Or manually:\ndocker compose up -d" ], "included_files": [ ".env.example", "README.md", "alertmanager/alertmanager.yml", "docker-compose.yml", "grafana/dashboards/application-metrics.json", "grafana/dashboards/docker-overview.json", "grafana/dashboards/node-overview.json", "grafana/provisioning/dashboards.yaml", "grafana/provisioning/datasources.yaml", "guide/01_monitoring-strategy.md", "guide/02_monitoring-stack-setup.md", "guide/03_what-you-get.md", "guides/monitoring-strategy.md", "loki/loki-config.yml", "prometheus/alert-rules.yml", "prometheus/prometheus.yml", "prometheus/recording-rules.yml", "promtail/promtail-config.yml", "scripts/setup.sh" ], "file_count": 19, "download_size_bytes": 30757, "download_size_human": "30.0 KB", "product_url": "https://datanest-stores.com/devops-toolkit/#product-monitoring-stack-setup", "preview_url": "https://datanest-stores.com/devops-toolkit/preview-monitoring-stack-setup", "demo_url": "https://datanest-stores.com/devops-toolkit/demo-monitoring-stack-setup/" }, { "product_id": "devops-toolkit:cicd-pipeline-blueprints", "slug": "cicd-pipeline-blueprints", "store": "devops-toolkit", "store_name": "DevOps Toolkit Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "ci/cd", "cloud", "devops", "docker", "github actions", "infrastructure as code", "kubernetes", "terraform" ], "title": "CI/CD Pipeline Blueprints", "short_description": "Pipeline templates for Jenkins, GitLab CI, CircleCI, and GitHub Actions with multi-env deployment strategies.", "detailed_description": "Production-ready pipeline templates for GitHub Actions, GitLab CI, Jenkins, and Azure DevOps. Copy, configure, deploy. Stop writing CI/CD pipelines from scratch. ![License: MIT](LICENSE) ![Pipelines](what-you-get) ![Price](https://datanest.dev) --- Includes: GitHub Actions; GitLab CI; Jenkins; Azure DevOps; Shared assets; Pipeline selector.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Git and your CI/CD platform account", "Docker 24+", "Python 3.10+ or Node.js 20+", "Platform-specific CLI tools" ], "supported_versions": [ "Python 3.10+", "Node 20+" ], "supported_technologies": [ "Docker", "Shell", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "# Interactive selector \u2014 asks questions, copies the right template\n./scripts/pipeline-selector.sh", "# Example: GitHub Actions for a Python app\ncp github-actions/python-app.yml .github/workflows/ci.yml\n\n# Example: GitLab CI for a Docker app\ncp gitlab-ci/docker-app.yml .gitlab-ci.yml\n\n# Grab the shared Dockerfile too\ncp shared/Dockerfile.python Dockerfile\ncp shared/.dockerignore .dockerignore" ], "included_files": [ "README.md", "azure-devops/docker-pipeline.yml", "azure-devops/python-pipeline.yml", "github-actions/docker-app.yml", "github-actions/node-app.yml", "github-actions/python-app.yml", "gitlab-ci/docker-app.yml", "gitlab-ci/python-app.yml", "guide/01_cicd-patterns.md", "guide/02_ci-cd-pipeline-blueprints.md", "guide/03_what-you-get.md", "guides/cicd-patterns.md", "jenkins/Jenkinsfile-docker", "jenkins/Jenkinsfile-python", "scripts/pipeline-selector.sh", "shared/.dockerignore", "shared/Dockerfile.node", "shared/Dockerfile.python" ], "file_count": 18, "download_size_bytes": 30715, "download_size_human": "30.0 KB", "product_url": "https://datanest-stores.com/devops-toolkit/#product-cicd-pipeline-blueprints", "preview_url": "https://datanest-stores.com/devops-toolkit/preview-cicd-pipeline-blueprints", "demo_url": "https://datanest-stores.com/devops-toolkit/demo-cicd-pipeline-blueprints/" }, { "product_id": "devops-toolkit:linux-hardening-scripts", "slug": "linux-hardening-scripts", "store": "devops-toolkit", "store_name": "DevOps Toolkit Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "ci/cd", "cloud", "devops", "docker", "github actions", "infrastructure as code", "kubernetes", "terraform" ], "title": "Linux Hardening Scripts", "short_description": "Automated server hardening scripts implementing CIS benchmarks: firewall, SSH, audit logging, user management.", "detailed_description": "Production-ready server hardening automation implementing CIS benchmarks and industry best practices. Automated Bash scripts and configuration files for securing Ubuntu/Debian and RHEL/CentOS Linux servers. Covers SSH hardening, firewall setup, intrusion detection, kernel tuning, audit logging, and more \u2014 all aligned with CIS Benchmark Level 1 and Level 2 recommendations. Includes: 9 modular hardening scripts \u2014 run individually or all at once; 4 hardened configuration files \u2014 drop-in replacements for critical services; CIS Benchmark checklist \u2014 track your compliance progress; Comprehensive security guide \u2014 understand the \"why\" behind each control.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "OS", "Access", "Packages", "Connectivity" ], "supported_versions": [], "supported_technologies": [ "Shell" ], "compatibility_notes": null, "preview_snippets": [ "unzip linux-hardening-scripts.zip\ncd linux-hardening-scripts", "# Review the hardened SSH config\ncat configs/sshd_config.hardened\n\n# Check kernel parameters\ncat configs/sysctl-hardened.conf" ], "included_files": [ "README.md", "checklists/cis-benchmark-ubuntu.md", "configs/audit.rules", "configs/jail.local", "configs/sshd_config.hardened", "configs/sysctl-hardened.conf", "guide/01_linux-security-guide.md", "guide/02_linux-hardening-scripts.md", "guide/03_what-you-get.md", "guides/linux-security-guide.md", "scripts/audit-setup.sh", "scripts/auto-updates.sh", "scripts/filesystem-hardening.sh", "scripts/full-hardening.sh", "scripts/harden-ssh.sh", "scripts/kernel-hardening.sh", "scripts/setup-fail2ban.sh", "scripts/setup-firewall.sh", "scripts/user-management.sh" ], "file_count": 19, "download_size_bytes": 44038, "download_size_human": "43.0 KB", "product_url": "https://datanest-stores.com/devops-toolkit/#product-linux-hardening-scripts", "preview_url": "https://datanest-stores.com/devops-toolkit/preview-linux-hardening-scripts", "demo_url": "https://datanest-stores.com/devops-toolkit/demo-linux-hardening-scripts/" }, { "product_id": "devops-toolkit:log-management-toolkit", "slug": "log-management-toolkit", "store": "devops-toolkit", "store_name": "DevOps Toolkit Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "ci/cd", "cloud", "devops", "docker", "github actions", "infrastructure as code", "kubernetes", "terraform" ], "title": "Log Management Toolkit", "short_description": "ELK/EFK stack configurations, log rotation scripts, structured logging templates, and log analysis queries.", "detailed_description": "Production-ready ELK stack deployment with structured logging pipelines and operational dashboards. Complete Elasticsearch, Logstash, Kibana, and Filebeat configurations for centralized log management. Includes ready-to-use parsing pipelines for Nginx, application JSON logs, and system logs, plus operational scripts for index management, backups, and a comprehensive log strategy guide. Includes: Docker Compose stack \u2014 One-command ELK deployment with resource limits; 5 Logstash pipelines \u2014 Parse Nginx, JSON apps, syslog, and custom formats; Filebeat configuration \u2014 Ship logs from any server to your stack; Kibana dashboards \u2014 Pre-built visualizations for immediate insight; 3 operational scripts \u2014 Setup, index rotation, and Kibana backup; Strategy guide \u2014 Log levels, retention, alerting, and compliance.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Docker 20.10+ and Docker Compose v2.0+", "RAM", "Disk", "Network" ], "supported_versions": [], "supported_technologies": [ "Docker", "Shell", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "log-management-toolkit/\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 manifest.json\n\u251c\u2500\u2500 .env.example\n\u251c\u2500\u2500 docker-compose.yml\n\u251c\u2500\u2500 elasticsearch/\n\u2502 \u2514\u2500\u2500 elasticsearch.yml # ES node configuration\n\u251c\u2500\u2500 logstash/\n\u2502 \u251c\u2500\u2500 pipeline/\n\u2502 \u2502 \u251c\u2500\u2500 main.conf # Main routing pipeline\n\u2502 \u2502 \u251c\u2500\u2500 nginx-access.conf # Nginx access log parser\n\u2502 \u2502 \u2514\u2500\u2500 app-json.conf # JSON application log parser\n\u2502 \u2514\u2500\u2500 patterns/\n\u2026", "cp .env.example .env\n# Edit .env with your settings (passwords, retention, memory limits)\nvim .env" ], "included_files": [ ".env.example", "README.md", "docker-compose.yml", "elasticsearch/elasticsearch.yml", "filebeat/filebeat.yml", "filebeat/modules.d/nginx.yml", "filebeat/modules.d/system.yml", "guide/01_log-management-strategy.md", "guide/02_log-management-toolkit.md", "guide/03_what-you-get.md", "guides/log-management-strategy.md", "kibana/export/dashboards.ndjson", "logstash/patterns/custom-patterns", "logstash/pipeline/app-json.conf", "logstash/pipeline/main.conf", "logstash/pipeline/nginx-access.conf", "scripts/backup-kibana.sh", "scripts/rotate-indices.sh", "scripts/setup.sh" ], "file_count": 19, "download_size_bytes": 35819, "download_size_human": "35.0 KB", "product_url": "https://datanest-stores.com/devops-toolkit/#product-log-management-toolkit", "preview_url": "https://datanest-stores.com/devops-toolkit/preview-log-management-toolkit", "demo_url": "https://datanest-stores.com/devops-toolkit/demo-log-management-toolkit/" }, { "product_id": "devops-toolkit:backup-disaster-recovery", "slug": "backup-disaster-recovery", "store": "devops-toolkit", "store_name": "DevOps Toolkit Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "ci/cd", "cloud", "devops", "docker", "github actions", "infrastructure as code", "kubernetes", "terraform" ], "title": "Backup & DR Toolkit", "short_description": "Automated backup scripts for databases, file systems, and cloud resources with DR runbooks and testing procedures.", "detailed_description": "Battle-tested backup scripts and disaster recovery runbooks for production infrastructure. ![License: MIT](LICENSE) ![Shell]() ![Docker]() Automate your backups, verify their integrity, and recover in minutes -- not hours. --- Includes: 6 production backup & restore scripts covering PostgreSQL, MySQL, filesystems, and Docker volumes; Backup verification with integrity checks, checksum validation, and test restores; Configurable schedules & retention via YAML (daily, weekly, monthly, yearly); Multi-channel notifications (Slack, email, PagerDuty) on success or failure; Docker-based backup agent for containerized environments; Disaster recovery runbooks with RPO/RTO analysis and step-by-step procedures.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [], "supported_versions": [], "supported_technologies": [ "Docker", "AWS", "Shell", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "# PostgreSQL backup with compression and S3 upload\n./scripts/backup-postgres.sh \\\n --host db.docs.example.com \\\n --database myapp_production \\\n --bucket s3://my-backups/postgres\n\n# Filesystem backup with encryption\n./scripts/backup-filesystem.sh \\\n --source /var/www/app/uploads \\\n --destination s3://my-backups/files \\\n --encrypt\n\n# Verify backup integrity\n./scripts/verify-backups.sh --bucket s3://my-backups --type postgres", "cd docker/\ndocker compose up -d\n# MinIO UI available at http://localhost:9001 (minioadmin/minioadmin)\n\n# Run a test backup against local MinIO\nBACKUP_BUCKET=\"s3://test-backups\" \\\nAWS_ENDPOINT_URL=\"http://localhost:9000\" \\\n ../scripts/backup-postgres.sh --host localhost --database testdb" ], "included_files": [ "README.md", "configs/backup-schedule.yaml", "configs/notification.yaml", "configs/retention-policy.yaml", "docker/backup-agent/Dockerfile", "docker/backup-agent/entrypoint.sh", "docker/docker-compose.yml", "guide/01_backup-strategy.md", "guide/02_backup-disaster-recovery-kit.md", "guide/03_what-you-get.md", "guides/backup-strategy.md", "runbooks/backup-verification-checklist.md", "runbooks/disaster-recovery-plan.md", "scripts/backup-docker-volumes.sh", "scripts/backup-filesystem.sh", "scripts/backup-mysql.sh", "scripts/backup-postgres.sh", "scripts/restore-filesystem.sh", "scripts/restore-postgres.sh", "scripts/verify-backups.sh" ], "file_count": 20, "download_size_bytes": 43316, "download_size_human": "42.3 KB", "product_url": "https://datanest-stores.com/devops-toolkit/#product-backup-disaster-recovery", "preview_url": "https://datanest-stores.com/devops-toolkit/preview-backup-disaster-recovery", "demo_url": "https://datanest-stores.com/devops-toolkit/demo-backup-disaster-recovery/" }, { "product_id": "devops-toolkit:infrastructure-as-code-patterns", "slug": "infrastructure-as-code-patterns", "store": "devops-toolkit", "store_name": "DevOps Toolkit Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "ci/cd", "cloud", "devops", "docker", "github actions", "infrastructure as code", "kubernetes", "terraform" ], "title": "IaC Patterns Library", "short_description": "Design patterns for Infrastructure as Code: modules, composition, testing, drift detection, and cost management.", "detailed_description": "Production-ready Terraform patterns and Terragrunt configurations for AWS infrastructure. ![License: MIT](LICENSE) ![Terraform]() ![AWS]() Stop reinventing the wheel. Battle-tested IaC patterns for VPC, ECS, RDS, Lambda, and more. --- Includes: 5 production Terraform patterns covering the most common AWS architectures; Reusable tagging module for consistent resource labeling across all stacks; Terragrunt configurations with DRY environment management (dev/prod); Terraform wrapper script with locking, state management, and plan output; Comprehensive guide on IaC patterns, module design, state management, and cost estimation.", "price_usd": 49, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [], "supported_versions": [], "supported_technologies": [ "Terraform", "AWS", "Shell" ], "compatibility_notes": null, "preview_snippets": [ "infrastructure-as-code-patterns/\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 manifest.json\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 patterns/\n\u2502 \u251c\u2500\u2500 vpc-three-tier/ # VPC with public/private/data subnets \u00d7 3 AZs\n\u2502 \u2502 \u251c\u2500\u2500 main.tf\n\u2502 \u2502 \u251c\u2500\u2500 variables.tf\n\u2502 \u2502 \u2514\u2500\u2500 outputs.tf\n\u2502 \u251c\u2500\u2500 ecs-fargate-service/ # ECS Fargate + ALB + auto-scaling\n\u2502 \u2502 \u251c\u2500\u2500 main.tf\n\u2502 \u2502 \u251c\u2500\u2500 variables.tf\n\u2502 \u2502 \u2514\u2500\u2500 outputs.tf\n\u2502 \u251c\u2500\u2500 rds-aurora/ # Aurora cluster with read replicas\n\u2026", "cd patterns/vpc-three-tier\n\n# Initialize and plan\nterraform init\nterraform plan -var=\"project=myapp\" -var=\"environment=dev\"\n\n# Apply\nterraform apply -var=\"project=myapp\" -var=\"environment=dev\"" ], "included_files": [ "README.md", "guide/01_iac-patterns.md", "guide/02_infrastructure-as-code-patterns.md", "guide/03_what-you-get.md", "guides/iac-patterns.md", "modules/tags/main.tf", "modules/tags/variables.tf", "patterns/ecs-fargate-service/main.tf", "patterns/ecs-fargate-service/outputs.tf", "patterns/ecs-fargate-service/variables.tf", "patterns/lambda-api/main.tf", "patterns/lambda-api/variables.tf", "patterns/rds-aurora/main.tf", "patterns/rds-aurora/variables.tf", "patterns/static-site/main.tf", "patterns/static-site/variables.tf", "patterns/vpc-three-tier/main.tf", "patterns/vpc-three-tier/outputs.tf", "patterns/vpc-three-tier/variables.tf", "scripts/tf-wrapper.sh", "terragrunt/environments/dev/terragrunt.hcl", "terragrunt/environments/prod/terragrunt.hcl", "terragrunt/terragrunt.hcl" ], "file_count": 23, "download_size_bytes": 42283, "download_size_human": "41.3 KB", "product_url": "https://datanest-stores.com/devops-toolkit/#product-infrastructure-as-code-patterns", "preview_url": "https://datanest-stores.com/devops-toolkit/preview-infrastructure-as-code-patterns", "demo_url": "https://datanest-stores.com/devops-toolkit/demo-infrastructure-as-code-patterns/" }, { "product_id": "devops-toolkit:container-security-toolkit", "slug": "container-security-toolkit", "store": "devops-toolkit", "store_name": "DevOps Toolkit Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "ci/cd", "cloud", "devops", "docker", "github actions", "infrastructure as code", "kubernetes", "terraform" ], "title": "Container Security Toolkit", "short_description": "Dockerfile linting, image scanning configs, runtime security policies, and Kubernetes admission controllers.", "detailed_description": "Harden, scan, and enforce security policies across your container infrastructure. Datanest Digital \u2014 datanest.dev --- Includes: 4 Hardened Dockerfiles \u2014 Multi-stage builds for Python, Node; 3 Scanner Configurations \u2014 Pre-tuned configs for Trivy, Hadolint, and Grype vulnerability scanners; 2 OPA Policies \u2014 Rego policies for Dockerfile best practices and Kubernetes pod security; 3 Kyverno Policies \u2014 Cluster policies enforcing non-root, resource limits, and image digests; 2 Runtime Profiles \u2014 Seccomp and AppArmor profiles restricting dangerous syscalls; 2 Automation Scripts \u2014 Image scanning and runtime auditing scripts for CI/CD.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Docker 24.0+", "Trivy 0.50+", "Hadolint 2.12+", "Grype 0.74+", "conftest 0.50+", "Kyverno 1.11+" ], "supported_versions": [], "supported_technologies": [ "Kubernetes", "Docker", "Shell", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "container-security-toolkit/\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 manifest.json\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 dockerfiles/\n\u2502 \u251c\u2500\u2500 python-secure.Dockerfile\n\u2502 \u251c\u2500\u2500 node-secure.Dockerfile\n\u2502 \u251c\u2500\u2500 go-secure.Dockerfile\n\u2502 \u2514\u2500\u2500 java-secure.Dockerfile\n\u251c\u2500\u2500 scanners/\n\u2502 \u251c\u2500\u2500 trivy.yaml\n\u2502 \u251c\u2500\u2500 hadolint.yaml\n\u2502 \u2514\u2500\u2500 grype.yaml\n\u251c\u2500\u2500 policies/\n\u2026", "docker build -f dockerfiles/python-secure.Dockerfile -t myapp:latest ." ], "included_files": [ "README.md", "dockerfiles/go-secure.Dockerfile", "dockerfiles/java-secure.Dockerfile", "dockerfiles/node-secure.Dockerfile", "dockerfiles/python-secure.Dockerfile", "guide/01_container-security-guide.md", "guide/02_container-security-toolkit.md", "guide/03_what-you-get.md", "guides/container-security-guide.md", "policies/kyverno/require-image-digest.yaml", "policies/kyverno/require-non-root.yaml", "policies/kyverno/require-resource-limits.yaml", "policies/opa/dockerfile-policy.rego", "policies/opa/k8s-pod-security.rego", "runtime/apparmor-profile", "runtime/seccomp-profile.json", "scanners/grype.yaml", "scanners/hadolint.yaml", "scanners/trivy.yaml", "scripts/audit-runtime.sh", "scripts/scan-image.sh", "workflows/container-security.yml" ], "file_count": 22, "download_size_bytes": 35294, "download_size_human": "34.5 KB", "product_url": "https://datanest-stores.com/devops-toolkit/#product-container-security-toolkit", "preview_url": "https://datanest-stores.com/devops-toolkit/preview-container-security-toolkit", "demo_url": "https://datanest-stores.com/devops-toolkit/demo-container-security-toolkit/" }, { "product_id": "devops-toolkit:gitops-workflow-templates", "slug": "gitops-workflow-templates", "store": "devops-toolkit", "store_name": "DevOps Toolkit Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "ci/cd", "cloud", "devops", "docker", "github actions", "infrastructure as code", "kubernetes", "terraform" ], "title": "GitOps Workflow Templates", "short_description": "ArgoCD and Flux configurations for GitOps deployments with environment promotion and rollback strategies.", "detailed_description": "Production-ready ArgoCD, FluxCD, and Kustomize configurations for declarative infrastructure delivery. Part of the DevOps Toolkit by Datanest Digital --- Includes: 5 ArgoCD configurations \u2014 Application, ApplicationSet, AppProject, sync policies with waves/hooks, Slack notifications; 5 FluxCD configurations \u2014 GitRepository, Kustomization, HelmRelease, notification provider, alerts; 4 Kustomize environments \u2014 Base + dev/staging/prod overlays with progressive resource scaling; 3 automation scripts \u2014 ArgoCD bootstrap, FluxCD bootstrap, environment promotion; 1 comprehensive guide \u2014 GitOps patterns, strategies, and best practices.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Kubernetes 1.26+", "ArgoCD 2.9+ or FluxCD 2.2+", "kubectl configured with cluster access", "Git repository for GitOps state", "bash 4.0+, git, yq" ], "supported_versions": [ "Kubernetes 1.26" ], "supported_technologies": [ "Kubernetes", "Shell", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "# Bootstrap ArgoCD in your cluster\nchmod +x scripts/bootstrap-argocd.sh\n./scripts/bootstrap-argocd.sh\n\n# Deploy an application\nkubectl apply -f argocd/project.yaml\nkubectl apply -f argocd/application.yaml\n\n# Generate apps for multiple environments\nkubectl apply -f argocd/applicationset.yaml", "# Bootstrap FluxCD with your GitHub repository\nchmod +x scripts/bootstrap-fluxcd.sh\n./scripts/bootstrap-fluxcd.sh --owner=your-org --repo=your-repo\n\n# Apply source and reconciliation\nkubectl apply -f fluxcd/git-repository.yaml\nkubectl apply -f fluxcd/app-kustomization.yaml\n\n# Deploy Helm charts via GitOps\nkubectl apply -f fluxcd/app-helmrelease.yaml" ], "included_files": [ "README.md", "argocd/application.yaml", "argocd/applicationset.yaml", "argocd/notifications/slack-template.yaml", "argocd/project.yaml", "argocd/sync-policy.yaml", "fluxcd/alert.yaml", "fluxcd/app-helmrelease.yaml", "fluxcd/app-kustomization.yaml", "fluxcd/git-repository.yaml", "fluxcd/slack-provider.yaml", "guide/01_gitops-patterns.md", "guide/02_gitops-workflow-templates.md", "guide/03_what-you-get.md", "guides/gitops-patterns.md", "kustomize/base/kustomization.yaml", "kustomize/overlays/dev/kustomization.yaml", "kustomize/overlays/prod/kustomization.yaml", "kustomize/overlays/staging/kustomization.yaml", "scripts/bootstrap-argocd.sh", "scripts/bootstrap-fluxcd.sh", "scripts/promote.sh" ], "file_count": 22, "download_size_bytes": 36551, "download_size_human": "35.7 KB", "product_url": "https://datanest-stores.com/devops-toolkit/#product-gitops-workflow-templates", "preview_url": "https://datanest-stores.com/devops-toolkit/preview-gitops-workflow-templates", "demo_url": "https://datanest-stores.com/devops-toolkit/demo-gitops-workflow-templates/" }, { "product_id": "devops-toolkit:devops-complete-bundle", "slug": "devops-complete-bundle", "store": "devops-toolkit", "store_name": "DevOps Toolkit Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "bundle", "ci/cd", "cloud", "devops", "docker", "github actions", "infrastructure as code", "kubernetes", "terraform" ], "title": "DevOps Complete Bundle", "short_description": "All 14 DevOps tools \u2014 $516 bought separately, yours for $149. Save $367 (71%): Terraform, Kubernetes, CI/CD, and automation scripts.", "detailed_description": "All 14 DevOps tools \u2014 $516 bought separately, yours for $149. Save $367 (71%): Terraform, Kubernetes, CI/CD, and automation scripts.", "price_usd": 149, "currency": "usd", "is_bundle": true, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [], "supported_versions": [], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "dockerfile-hardening-checklist (full product)", "terraform-starter-kit (full product)", "docker-compose-templates (full product)", "kubernetes-manifests-pack (full product)", "github-actions-workflows (full product)", "ansible-playbook-collection (full product)", "nginx-config-templates (full product)", "monitoring-stack-setup (full product)", "cicd-pipeline-blueprints (full product)", "linux-hardening-scripts (full product)", "log-management-toolkit (full product)", "backup-disaster-recovery (full product)", "infrastructure-as-code-patterns (full product)", "container-security-toolkit (full product)", "gitops-workflow-templates (full product)" ], "file_count": 15, "download_size_bytes": 462945, "download_size_human": "452.1 KB", "product_url": "https://datanest-stores.com/devops-toolkit/#product-devops-complete-bundle", "preview_url": null, "demo_url": null }, { "product_id": "email-arsenal:bounce-handler", "slug": "bounce-handler", "store": "email-arsenal", "store_name": "Email Arsenal", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "deliverability", "dev", "dkim", "email", "email marketing", "email templates", "newsletters" ], "title": "Bounce Handler", "short_description": "Parse email bounces, extract diagnostic info, and automatically clean mailing lists.", "detailed_description": "Parse and categorize email bounces (hard/soft), extract diagnostic info, and automatically clean your mailing lists by removing permanently undeliverable addresses. Includes: Hard/soft/unknown classification \u2014 Categorizes bounces by SMTP response codes; SMTP code matching \u2014 Recognizes 550, 421, and dozens of enhanced status codes; Regex pattern matching \u2014 Catches bounces from non-standard error messages; Raw; List cleaning \u2014 Removes hard-bounced addresses from your mailing list; JSON/CSV output \u2014 Export categorized bounces for further processing.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Categorize bounces from a JSON file\npython src/bounce_handler.py --input examples/sample_bounces.json\n\n# Parse a raw bounce email (.eml)\npython src/bounce_handler.py --parse-raw bounce_notification.eml\n\n# Clean a mailing list by removing hard bounces\npython src/bounce_handler.py --input bounces.json --clean emails.txt --output clean.txt", "[\n {\n \"email\": \"user@docs.example.com\",\n \"smtp_code\": \"550\",\n \"diagnostic\": \"5.1.1 The email account does not exist\",\n \"timestamp\": \"2026-03-14T12:00:00Z\"\n },\n {\n \"email\": \"other@docs.example.com\",\n \"smtp_code\": \"421\",\n \"diagnostic\": \"Try again later\",\n \"timestamp\": \"2026-03-14T12:01:00Z\"\n }\n]" ], "included_files": [ "README.md", "examples/sample_bounces.json", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_bounce-categories.md", "guide/04_faq.md", "src/bounce_handler.py" ], "file_count": 7, "download_size_bytes": 35435, "download_size_human": "34.6 KB", "product_url": "https://datanest-stores.com/email-arsenal/#product-bounce-handler", "preview_url": "https://datanest-stores.com/email-arsenal/preview-bounce-handler", "demo_url": "https://datanest-stores.com/email-arsenal/demo-bounce-handler/" }, { "product_id": "email-arsenal:deliverability-checker", "slug": "deliverability-checker", "store": "email-arsenal", "store_name": "Email Arsenal", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "deliverability", "dev", "dkim", "email", "email marketing", "email templates", "newsletters" ], "title": "Deliverability Checker", "short_description": "Check SPF, DKIM, and DMARC DNS records to diagnose email deliverability issues.", "detailed_description": "Check SPF, DKIM, and DMARC DNS records for any domain and diagnose deliverability issues. Know why your emails land in spam. Includes: SPF record validation \u2014 Parses and validates SPF TXT records, detects too many DNS lookups; DMARC record checking \u2014 Validates policy, alignment, and reporting configuration; DKIM record lookup \u2014 Checks DKIM DNS records with multi-selector support; MX record resolution \u2014 Verifies mail exchange records exist and resolve; Deliverability scoring \u2014 0-100 score based on authentication completeness; Batch domain checking \u2014 Check multiple domains from a file.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Check a single domain\npython src/deliverability_checker.py docs.example.com\n\n# Verbose output with full record details\npython src/deliverability_checker.py docs.example.com --verbose\n\n# Check with a specific DKIM selector\npython src/deliverability_checker.py docs.example.com --dkim-selector google\n\n# Check multiple domains from a file\npython src/deliverability_checker.py --domains examples/sample_domains.txt --output report.json", "{\n \"domain\": \"docs.example.com\",\n \"score\": 85,\n \"spf\": {\n \"found\": true,\n \"record\": \"v=spf1 include:_spf.google.com ~all\",\n \"valid\": true,\n \"issues\": []\n },\n \"dmarc\": {\n \"found\": true,\n \"policy\": \"reject\",\n \"alignment\": \"strict\"\n },\n\u2026" ], "included_files": [ "README.md", "examples/sample_domains.txt", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_configuration.md", "guide/04_license.md", "src/deliverability_checker.py" ], "file_count": 7, "download_size_bytes": 40201, "download_size_human": "39.3 KB", "product_url": "https://datanest-stores.com/email-arsenal/#product-deliverability-checker", "preview_url": "https://datanest-stores.com/email-arsenal/preview-deliverability-checker", "demo_url": "https://datanest-stores.com/email-arsenal/demo-deliverability-checker/" }, { "product_id": "email-arsenal:dkim-validator", "slug": "dkim-validator", "store": "email-arsenal", "store_name": "Email Arsenal", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "deliverability", "dev", "dkim", "email", "email marketing", "email templates", "newsletters" ], "title": "DKIM Validator", "short_description": "Generate and validate DKIM signatures, debug authentication failures, and scan domains.", "detailed_description": "Generate and validate DKIM signatures and DNS records. Debug authentication failures and scan domains for existing DKIM configurations. Includes: DNS record checking \u2014 Validates DKIM TXT records for any domain + selector; Multi-selector scanning \u2014 Probes 15 common selectors (Google, Microsoft 365, Mandrill, etc; DKIM header parsing \u2014 Extracts and explains all tags from DKIM-Signature headers; DNS record template generation \u2014 Creates ready-to-paste TXT records for new domains; Key size validation \u2014 Warns on weak keys (< 1024 bits); Tag descriptions \u2014 Human-readable explanations of every DKIM tag (v, a, b, bh, c, d, h, s, etc.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md", "examples/sample_dkim_header.txt", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_dkim-tag-reference.md", "guide/04_license.md", "src/dkim_validator.py" ], "file_count": 7, "download_size_bytes": 37937, "download_size_human": "37.0 KB", "product_url": "https://datanest-stores.com/email-arsenal/#product-dkim-validator", "preview_url": "https://datanest-stores.com/email-arsenal/preview-dkim-validator", "demo_url": "https://datanest-stores.com/email-arsenal/demo-dkim-validator/" }, { "product_id": "email-arsenal:email-arsenal-pro-bundle", "slug": "email-arsenal-pro-bundle", "store": "email-arsenal", "store_name": "Email Arsenal", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "bundle", "deliverability", "dev", "dkim", "email", "email marketing", "email templates", "newsletters" ], "title": "Email Arsenal Pro Bundle", "short_description": "All 10 email tools \u2014 $250 bought separately, yours for $59. Save $191 (76%): verification, warmup, DKIM, delivery, and newsletter analytics.", "detailed_description": "All 10 email tools \u2014 $250 bought separately, yours for $59. Save $191 (76%): verification, warmup, DKIM, delivery, and newsletter analytics.", "price_usd": 59, "currency": "usd", "is_bundle": true, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [ "email-arsenal-pro-bundle/\n\u251c\u2500\u2500 email-verifier/\n\u251c\u2500\u2500 warmup-scheduler/\n\u251c\u2500\u2500 signature-generator/\n\u251c\u2500\u2500 transactional-templates/\n\u251c\u2500\u2500 deliverability-checker/\n\u251c\u2500\u2500 bounce-handler/\n\u251c\u2500\u2500 list-hygiene-tool/\n\u251c\u2500\u2500 dkim-validator/\n\u251c\u2500\u2500 email-preview-renderer/\n\u2514\u2500\u2500 newsletter-analytics/" ], "included_files": [ "README.md" ], "file_count": 1, "download_size_bytes": 260178, "download_size_human": "254.1 KB", "product_url": "https://datanest-stores.com/email-arsenal/#product-email-arsenal-pro-bundle", "preview_url": "https://datanest-stores.com/email-arsenal/preview-email-arsenal-pro-bundle", "demo_url": null }, { "product_id": "email-arsenal:email-preview-renderer", "slug": "email-preview-renderer", "store": "email-arsenal", "store_name": "Email Arsenal", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "deliverability", "dev", "dkim", "email", "email marketing", "email templates", "newsletters" ], "title": "Email Preview Renderer", "short_description": "Render HTML emails and check compatibility across email clients before sending.", "detailed_description": "Render HTML emails and check compatibility across different email clients. Catch layout bugs before you hit send. Includes: Email client rules \u2014 Built-in rendering profiles for Outlook Desktop, Gmail, Apple Mail, Yahoo Mail, and Outlook Web; HTML auditing \u2014 Checks accessibility, CSS compatibility, and common rendering issues; CSS inlining \u2014 Converts ", "--wf-color-primary: #4f46e5; /* Your primary brand color */\n--wf-color-secondary: #0ea5e9; /* Your secondary color */\n--wf-color-accent: #f59e0b; /* Accent / highlight color */" ], "included_files": [ "README.md", "components/cta/cta-banner.html", "components/cta/cta-countdown.html", "components/cta/cta-floating-bar.html", "components/cta/cta-gradient.html", "components/cta/cta-minimal.html", "components/cta/cta-newsletter.html", "components/cta/cta-split.html", "components/features/features-alternating.html", "components/features/features-bento.html", "components/features/features-cards.html", "components/features/features-comparison.html", "components/features/features-grid-icons.html", "components/features/features-tabs.html", "components/features/features-timeline.html", "components/footers/footer-app-download.html", "components/footers/footer-centered.html", "components/footers/footer-four-column.html", "components/footers/footer-mega.html", "components/footers/footer-minimal.html", "components/footers/footer-newsletter.html", "components/forms/form-checkout.html", "components/forms/form-contact.html", "components/forms/form-login.html", "components/forms/form-multi-step.html", "components/forms/form-newsletter.html", "components/forms/form-signup.html", "components/forms/form-survey.html", "components/hero/hero-animated.html", "components/hero/hero-app-showcase.html", "components/hero/hero-centered.html", "components/hero/hero-gradient.html", "components/hero/hero-minimal.html", "components/hero/hero-split.html", "components/hero/hero-video-bg.html", "components/navigation/navbar-announcement.html", "components/navigation/navbar-centered.html", "components/navigation/navbar-hamburger.html", "components/navigation/navbar-mega-menu.html", "components/navigation/navbar-sidebar.html", "components/navigation/navbar-simple.html", "components/navigation/navbar-sticky.html", "components/navigation/navbar-transparent.html", "components/pricing/pricing-cards.html", "components/pricing/pricing-comparison.html", "components/pricing/pricing-enterprise.html", "components/pricing/pricing-minimal.html", "components/pricing/pricing-three-tier.html", "components/pricing/pricing-toggle.html", "components/testimonials/testimonials-avatar-row.html", "components/testimonials/testimonials-cards.html", "components/testimonials/testimonials-masonry.html", "components/testimonials/testimonials-quote.html", "components/testimonials/testimonials-slider.html", "components/testimonials/testimonials-video.html", "guide/01_CLASS-NAMING.md", "guide/02_IMPORT-GUIDE.md", "guide/03_RESPONSIVE-GUIDE.md", "guides/CLASS-NAMING.md", "guides/IMPORT-GUIDE.md", "guides/RESPONSIVE-GUIDE.md", "styles/reset.css", "styles/variables.css" ], "file_count": 63, "download_size_bytes": 224122, "download_size_human": "218.9 KB", "product_url": "https://datanest-stores.com/nocode-builder/#product-webflow-component-library", "preview_url": "https://datanest-stores.com/nocode-builder/preview-webflow-component-library", "demo_url": "https://datanest-stores.com/nocode-builder/demo-webflow-component-library/" }, { "product_id": "nocode-builder:n8n-workflow-templates", "slug": "n8n-workflow-templates", "store": "nocode-builder", "store_name": "No-Code Builder Pro", "domain": "career", "category": "Career, Creator & Business", "tags": [ "airtable", "automation", "bubble", "career", "low-code", "make", "no-code", "retool", "webflow", "zapier" ], "title": "n8n Self-Hosted Workflow Templates", "short_description": "30+ n8n workflow templates for data processing, notifications, monitoring, reporting, and API integrations.", "detailed_description": "30 ready-to-import n8n workflow templates for data processing, notifications, monitoring, reporting, and API integrations. Import a workflow JSON into your self-hosted n8n instance, configure the credentials, and you have a working automation in minutes. Each workflow includes realistic node configurations, JavaScript code transforms, and connection patterns you can adapt to your infrastructure. --- Includes: 30 workflow JSON files organized into 5 categories, each importable directly into n8n; Data Processing (6); Notifications (6); Monitoring (6); Reporting (6); API Integrations (6).", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [], "supported_versions": [], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [ "n8n-workflow-templates/\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 workflows/\n\u2502 \u251c\u2500\u2500 data-processing/\n\u2502 \u2502 \u251c\u2500\u2500 csv-to-json-converter.json\n\u2502 \u2502 \u251c\u2500\u2500 database-deduplicator.json\n\u2502 \u2502 \u251c\u2500\u2500 json-schema-validator.json\n\u2502 \u2502 \u251c\u2500\u2500 data-enrichment-pipeline.json\n\u2502 \u2502 \u251c\u2500\u2500 xml-to-json-transformer.json\n\u2502 \u2502 \u2514\u2500\u2500 batch-record-importer.json\n\u2502 \u251c\u2500\u2500 notifications/\n\u2502 \u2502 \u251c\u2500\u2500 slack-alert-on-error.json\n\u2502 \u2502 \u251c\u2500\u2500 email-digest-sender.json\n\u2026" ], "included_files": [ "README.md", "guide/01_CREDENTIALS.md", "guide/02_ERROR-HANDLING.md", "guide/03_SELF-HOSTING.md", "guides/CREDENTIALS.md", "guides/ERROR-HANDLING.md", "guides/SELF-HOSTING.md", "workflows/api-integrations/airtable-to-postgres.json", "workflows/api-integrations/github-issue-sync.json", "workflows/api-integrations/google-sheets-sync.json", "workflows/api-integrations/jira-ticket-importer.json", "workflows/api-integrations/openai-content-generator.json", "workflows/api-integrations/stripe-payment-webhook.json", "workflows/data-processing/batch-record-importer.json", "workflows/data-processing/csv-to-json-converter.json", "workflows/data-processing/data-enrichment-pipeline.json", "workflows/data-processing/database-deduplicator.json", "workflows/data-processing/json-schema-validator.json", "workflows/data-processing/xml-to-json-transformer.json", "workflows/monitoring/api-health-dashboard.json", "workflows/monitoring/container-health-checker.json", "workflows/monitoring/database-connection-monitor.json", "workflows/monitoring/disk-space-monitor.json", "workflows/monitoring/ssl-certificate-expiry.json", "workflows/monitoring/website-uptime-checker.json", "workflows/notifications/email-digest-sender.json", "workflows/notifications/multi-channel-broadcaster.json", "workflows/notifications/pagerduty-incident-creator.json", "workflows/notifications/slack-alert-on-error.json", "workflows/notifications/sms-alert-pipeline.json", "workflows/notifications/webhook-to-discord.json", "workflows/reporting/database-size-report.json", "workflows/reporting/error-rate-report.json", "workflows/reporting/invoice-generator.json", "workflows/reporting/sla-compliance-tracker.json", "workflows/reporting/user-activity-summary.json", "workflows/reporting/weekly-kpi-report.json" ], "file_count": 37, "download_size_bytes": 97379, "download_size_human": "95.1 KB", "product_url": "https://datanest-stores.com/nocode-builder/#product-n8n-workflow-templates", "preview_url": "https://datanest-stores.com/nocode-builder/preview-n8n-workflow-templates", "demo_url": "https://datanest-stores.com/nocode-builder/demo-n8n-workflow-templates/" }, { "product_id": "nocode-builder:nocode-mvp-playbook", "slug": "nocode-mvp-playbook", "store": "nocode-builder", "store_name": "No-Code Builder Pro", "domain": "career", "category": "Career, Creator & Business", "tags": [ "airtable", "automation", "bubble", "career", "low-code", "make", "no-code", "retool", "webflow", "zapier" ], "title": "No-Code MVP Playbook", "short_description": "Step-by-step guide to building and launching an MVP with no-code tools. 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Most no-code builders skip testing because there's no \"test suite\" button. This guide gives you structured testing processes adapted to visual builders \u2014 manual QA checklists, automated monitoring setups, regression testing workflows, and UAT templates that work whether you built with Bubble, Webflow, Airtable, or any other platform. ---", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [], "supported_versions": [], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md", "checklists/manual-qa-automation-workflow.md", "checklists/manual-qa-mobile-app.md", "checklists/manual-qa-web-app.md", "examples/example-test-plan-marketplace.md", "examples/monitoring-config-examples.md", "guide/01_automated-monitoring-setup.md", "guide/02_bug-tracking-workflow.md", "guide/03_qa-strategy-overview.md", "guide/04_regression-testing-process.md", "guides/automated-monitoring-setup.md", "guides/bug-tracking-workflow.md", "guides/qa-strategy-overview.md", "guides/regression-testing-process.md", "templates/test-case-template-library.md", "templates/test-report-template.md", "templates/uat-plan-template.md" ], "file_count": 17, "download_size_bytes": 109244, "download_size_human": "106.7 KB", "product_url": "https://datanest-stores.com/nocode-builder/#product-automation-testing-guide", "preview_url": "https://datanest-stores.com/nocode-builder/preview-automation-testing-guide", "demo_url": "https://datanest-stores.com/nocode-builder/demo-automation-testing-guide/" }, { "product_id": "nocode-builder:nocode-security-checklist", "slug": "nocode-security-checklist", "store": "nocode-builder", "store_name": "No-Code Builder Pro", "domain": "career", "category": "Career, Creator & Business", "tags": [ "airtable", "automation", "bubble", "career", "low-code", "make", "no-code", "retool", "webflow", "zapier" ], "title": "No-Code Security Checklist", "short_description": "Security best practices for no-code platforms: authentication, data protection, API security, and compliance considerations.", "detailed_description": "Price: $19 | Format: Checklists, guides, templates, worksheets A comprehensive security system for no-code applications. No-code platforms handle some security for you (hosting, SSL, infrastructure) but leave critical gaps: authentication configuration, data access rules, API exposure, third-party risk, and compliance. 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Includes: Monthly Recurring Revenue (MRR) with breakdown; Annual Recurring Revenue (ARR) and average revenue per customer (ARPC); Customer churn rate and revenue churn rate; Customer Lifetime Value (LTV); Failed payment analysis with error code ranking and revenue at risk; Revenue forecasting with month-over-month growth projection.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md", "examples/payments.json", "guide/01_features.md", "guide/02_cli-reference.md", "guide/03_metrics-explained.md", "src/payment_analytics.py" ], "file_count": 6, "download_size_bytes": 40708, "download_size_human": "39.8 KB", "product_url": "https://datanest-stores.com/payment-stack/#product-payment-analytics", "preview_url": "https://datanest-stores.com/payment-stack/preview-payment-analytics", "demo_url": "https://datanest-stores.com/payment-stack/demo-payment-analytics/" }, { "product_id": "payment-stack:payment-retry-logic", "slug": "payment-retry-logic", "store": "payment-stack", "store_name": "Payment Stack", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "billing", "checkout", "dev", "invoicing", "payment processing", "stripe", "subscription" ], "title": "Payment Retry Logic", "short_description": "Payment retry system with smart scheduling, declining card detection, and recovery workflows.", "detailed_description": "Part of the Payment Stack by CodeVault A smart payment retry system with exponential backoff, failure categorization, and recovery analytics. Knows which failures to retry and when. Includes: 20+ error codes classified into 7 failure categories; Exponential backoff with configurable jitter; Category-aware retry strategies (network vs; Retry task queue with scheduling and execution tracking; Recovery rate analytics and statistics; Hard decline detection (never retry stolen/expired cards).", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Run the full demo\npython src/payment_retry_logic.py --action demo\n\n# Classify an error code\npython src/payment_retry_logic.py --action classify --error-code insufficient_funds\n\n# Schedule a retry\npython src/payment_retry_logic.py --action schedule --payment-id pay_001 --error-code network_error\n\n# View backoff tables\npython src/payment_retry_logic.py --action backoff-table\n\n# Process pending retries\npython src/payment_retry_logic.py --action process-queue" ], "included_files": [ "README.md", "examples/failed_payments.json", "guide/01_features.md", "guide/02_cli-reference.md", "guide/03_license.md", "src/payment_retry_logic.py" ], "file_count": 6, "download_size_bytes": 38950, "download_size_human": "38.0 KB", "product_url": "https://datanest-stores.com/payment-stack/#product-payment-retry-logic", "preview_url": "https://datanest-stores.com/payment-stack/preview-payment-retry-logic", "demo_url": "https://datanest-stores.com/payment-stack/demo-payment-retry-logic/" }, { "product_id": "payment-stack:payment-stack-pro-bundle", "slug": "payment-stack-pro-bundle", "store": "payment-stack", "store_name": "Payment Stack", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "billing", "bundle", "checkout", "dev", "invoicing", "payment processing", "stripe", "subscription" ], "title": "Payment Stack \u2014 Pro Bundle", "short_description": "All 10 payment tools \u2014 $240 bought separately, yours for $59. Save $181 (75%): Stripe, crypto, invoices, subscriptions, and tax.", "detailed_description": "By CodeVault \u2014 Ship faster. Build smarter. The complete payment infrastructure toolkit for serious builders. All 10 individual tools from the Payment Stack, covering every stage of the payment lifecycle \u2014 from checkout to analytics, from subscriptions to refunds, from tax compliance to crypto payments.", "price_usd": 59, "currency": "usd", "is_bundle": true, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [ "Customer visits checkout (Checkout Builder)\n \u2192 Selects plan (Subscription Billing)\n \u2192 Tax calculated (Tax Calculator)\n \u2192 Payment processed (Stripe Integration Kit)\n \u2192 Success \u2192 Receipt sent (Receipt Emailer)\n \u2192 \u2192 Invoice generated (Invoice Generator)\n \u2192 \u2192 Analytics tracked (Payment Analytics)\n \u2192 Failure \u2192 Retry scheduled (Payment Retry Logic)\n \u2192 Crypto option (Crypto Payment Handler)\n \u2192 Refund requested (Refund Processor)" ], "included_files": [ "README.md" ], "file_count": 1, "download_size_bytes": 271588, "download_size_human": "265.2 KB", "product_url": "https://datanest-stores.com/payment-stack/#product-payment-stack-pro-bundle", "preview_url": "https://datanest-stores.com/payment-stack/preview-payment-stack-pro-bundle", "demo_url": null }, { "product_id": "payment-stack:receipt-emailer", "slug": "receipt-emailer", "store": "payment-stack", "store_name": "Payment Stack", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "billing", "checkout", "dev", "invoicing", "payment processing", "stripe", "subscription" ], "title": "Receipt Emailer", "short_description": "Automated receipt email system with customizable templates and delivery confirmation.", "detailed_description": "Part of the Payment Stack by CodeVault Generate branded HTML payment receipts with itemized charges, download links, and refund notices. Simulates SMTP email delivery for testing. Includes: Professional, email-client-compatible HTML receipts; Inline CSS with table-based layout (works in Gmail, Outlook, etc; Itemized charges with quantity, price, and tax per item; Download links for digital products; Payment method display (masked card, PayPal, crypto); Discount and tax breakdown.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Generate a demo receipt\npython src/receipt_emailer.py --action demo --output receipt.html\n\n# Generate from config\npython src/receipt_emailer.py --config examples/receipt_data.json --output receipt.html\n\n# Simulate sending an email\npython src/receipt_emailer.py --action send --config examples/receipt_data.json\n\n# Output receipt data as JSON\npython src/receipt_emailer.py --action demo --format json" ], "included_files": [ "README.md", "examples/receipt_data.json", "guide/01_features.md", "guide/02_cli-reference.md", "guide/03_license.md", "src/receipt_emailer.py" ], "file_count": 6, "download_size_bytes": 36578, "download_size_human": "35.7 KB", "product_url": "https://datanest-stores.com/payment-stack/#product-receipt-emailer", "preview_url": "https://datanest-stores.com/payment-stack/preview-receipt-emailer", "demo_url": "https://datanest-stores.com/payment-stack/demo-receipt-emailer/" }, { "product_id": "payment-stack:refund-processor", "slug": "refund-processor", "store": "payment-stack", "store_name": "Payment Stack", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "billing", "checkout", "dev", "invoicing", "payment processing", "stripe", "subscription" ], "title": "Refund Processor", "short_description": "Refund processing toolkit with partial/full refunds, reason tracking, and reconciliation.", "detailed_description": "Part of the Payment Stack by CodeVault A comprehensive payment refund management system with support for full and partial refunds, reason tracking, balance adjustments, policy enforcement, and complete audit trails. Includes: Full and partial refund support with balance tracking; 8 standardized refund reason codes with policy rules; Auto-approval for duplicate, fraudulent, and pricing error refunds; Refund policy engine (window limits, amount validation, partial refund caps); Complete audit trail on every refund action; Payment balance adjustment and status tracking.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md", "examples/refund_scenarios.json", "guide/01_features.md", "guide/02_cli-reference.md", "guide/03_policy-rules.md", "src/refund_processor.py" ], "file_count": 6, "download_size_bytes": 39986, "download_size_human": "39.0 KB", "product_url": "https://datanest-stores.com/payment-stack/#product-refund-processor", "preview_url": "https://datanest-stores.com/payment-stack/preview-refund-processor", "demo_url": "https://datanest-stores.com/payment-stack/demo-refund-processor/" }, { "product_id": "payment-stack:stripe-integration-kit", "slug": "stripe-integration-kit", "store": "payment-stack", "store_name": "Payment Stack", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "billing", "checkout", "dev", "invoicing", "payment processing", "stripe", "subscription" ], "title": "Stripe Integration Kit", "short_description": "Python Stripe integration with checkout, webhook handling, refunds, and subscription management.", "detailed_description": "Part of the Payment Stack by CodeVault A complete local simulation of Stripe payment workflows: checkout sessions, payment intents, customer management, and webhook signature verification. Understand every pattern before touching the real API. Includes: Full checkout session lifecycle (create \u2192 complete \u2192 webhook); Payment intent state machine (requiresmethod \u2192 processing \u2192 succeeded); Customer CRUD with metadata support; Webhook signature generation and HMAC-SHA256 verification; Idempotency key handling to prevent duplicate charges; JSON file-based persistence (swap for your real database).", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md", "examples/checkout_config.json", "examples/webhook_event.json", "guide/01_features.md", "guide/02_cli-reference.md", "guide/03_configuration.md", "guide/04_license.md", "src/stripe_integration_kit.py" ], "file_count": 8, "download_size_bytes": 44745, "download_size_human": "43.7 KB", "product_url": "https://datanest-stores.com/payment-stack/#product-stripe-integration-kit", "preview_url": "https://datanest-stores.com/payment-stack/preview-stripe-integration-kit", "demo_url": "https://datanest-stores.com/payment-stack/demo-stripe-integration-kit/" }, { "product_id": "payment-stack:subscription-billing", "slug": "subscription-billing", "store": "payment-stack", "store_name": "Payment Stack", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "billing", "checkout", "dev", "invoicing", "payment processing", "stripe", "subscription" ], "title": "Subscription Billing Engine", "short_description": "Subscription billing engine with recurring payments, proration, dunning, and plan management.", "detailed_description": "Part of the Payment Stack by CodeVault A complete subscription lifecycle manager: plan creation, trial periods, proration, grace periods, and dunning workflows. Understand every billing pattern before integrating with Stripe Billing. Includes: Plan management (create, list, activate/deactivate); Subscription lifecycle; Mid-cycle plan changes with automatic proration; Trial periods with configurable duration; Grace periods for failed payments; Dunning workflow.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md", "examples/plans_config.json", "guide/01_features.md", "guide/02_cli-reference.md", "guide/03_license.md", "src/subscription_billing.py" ], "file_count": 6, "download_size_bytes": 40468, "download_size_human": "39.5 KB", "product_url": "https://datanest-stores.com/payment-stack/#product-subscription-billing", "preview_url": "https://datanest-stores.com/payment-stack/preview-subscription-billing", "demo_url": "https://datanest-stores.com/payment-stack/demo-subscription-billing/" }, { "product_id": "payment-stack:tax-calculator", "slug": "tax-calculator", "store": "payment-stack", "store_name": "Payment Stack", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "billing", "checkout", "dev", "invoicing", "payment processing", "stripe", "subscription" ], "title": "Tax Calculator", "short_description": "Sales tax calculator with multi-jurisdiction support, exemption handling, and reporting.", "detailed_description": "Part of the Payment Stack by CodeVault A multi-jurisdiction tax calculation engine supporting sales tax (US), VAT (EU/UK), and GST (AU/NZ/CA/IN). Handles geo-based rate lookups, product category adjustments, buyer exemptions, and multi-item invoice calculations. Includes: 45+ country tax rates with standard and reduced tiers; All 50 US state sales tax rates (including zero-tax states); 6 product categories with rate adjustments (digital, physical, food, education, etc; 5 buyer types with exemption rules (individual, business, nonprofit, reseller, etc; Tax-inclusive and tax-exclusive calculation modes; Multi-item invoice tax calculation with per-line breakdowns.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md", "examples/invoice_items.json", "guide/01_features.md", "guide/02_cli-reference.md", "guide/03_buyer-types.md", "src/tax_calculator.py" ], "file_count": 6, "download_size_bytes": 41008, "download_size_human": "40.0 KB", "product_url": "https://datanest-stores.com/payment-stack/#product-tax-calculator", "preview_url": "https://datanest-stores.com/payment-stack/preview-tax-calculator", "demo_url": "https://datanest-stores.com/payment-stack/demo-tax-calculator/" }, { "product_id": "pm-toolkit:product-requirements-templates", "slug": "product-requirements-templates", "store": "pm-toolkit", "store_name": "PM Toolkit Pro", "domain": "career", "category": "Career, Creator & Business", "tags": [ "career", "metrics", "okr", "prd", "product manager", "roadmap", "sprint", "stakeholder", "user research" ], "title": "Product Requirements Document Templates", "short_description": "10+ PRD templates for features, platform changes, API products, and infrastructure projects with stakeholder mapping.", "detailed_description": "PM Toolkit Pro | $29 A complete library of PRD templates for technical product managers. Covers 10 distinct project types \u2014 from user-facing features to API products to infrastructure overhauls \u2014 plus a stakeholder mapping template, a comprehensive writing guide, and a fully worked example PRD you can reference. ---", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [], "supported_versions": [], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md", "examples/example-prd-search-api.md", "guide/01_prd-writing-guide.md", "guide/02_product-requirements-document-templates.md", "guide/03_what-s-inside.md", "guides/prd-writing-guide.md", "templates/prd-api-product.md", "templates/prd-data-pipeline.md", "templates/prd-design-system-component.md", "templates/prd-feature-launch.md", "templates/prd-infrastructure.md", "templates/prd-integration.md", "templates/prd-mobile-feature.md", "templates/prd-performance-optimization.md", "templates/prd-platform-migration.md", "templates/prd-security-initiative.md", "templates/stakeholder-map.md" ], "file_count": 17, "download_size_bytes": 139309, "download_size_human": "136.0 KB", "product_url": "https://datanest-stores.com/pm-toolkit/#product-product-requirements-templates", "preview_url": "https://datanest-stores.com/pm-toolkit/preview-product-requirements-templates", "demo_url": "https://datanest-stores.com/pm-toolkit/demo-product-requirements-templates/" }, { "product_id": "pm-toolkit:roadmap-planning-system", "slug": "roadmap-planning-system", "store": "pm-toolkit", "store_name": "PM Toolkit Pro", "domain": "career", "category": "Career, Creator & Business", "tags": [ "career", "metrics", "okr", "prd", "product manager", "roadmap", "sprint", "stakeholder", "user research" ], "title": "Roadmap Planning System", "short_description": "Quarterly and annual roadmap templates with prioritization frameworks (RICE, ICE, MoSCoW), capacity planning, and stakeholder views.", "detailed_description": "PM Toolkit Pro | $39 A complete roadmap planning system for technical product managers. Includes quarterly and annual roadmap templates, three prioritization framework spreadsheets (RICE, ICE, MoSCoW) with realistic sample data, capacity planning tools, stakeholder-specific view templates, and a comprehensive roadmapping guide. ---", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [], "supported_versions": [], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md", "frameworks/prioritization-guide.md", "guide/01_roadmapping-guide.md", "guide/02_roadmap-planning-system.md", "guide/03_what-s-inside.md", "guides/roadmapping-guide.md", "spreadsheets/capacity-tracker.csv", "spreadsheets/ice-prioritization.csv", "spreadsheets/moscow-prioritization.csv", "spreadsheets/rice-prioritization.csv", "templates/annual-roadmap.md", "templates/capacity-planning.md", "templates/now-next-later.md", "templates/quarterly-roadmap.md", "templates/stakeholder-view-engineering.md", "templates/stakeholder-view-executive.md" ], "file_count": 16, "download_size_bytes": 87045, "download_size_human": "85.0 KB", "product_url": "https://datanest-stores.com/pm-toolkit/#product-roadmap-planning-system", "preview_url": "https://datanest-stores.com/pm-toolkit/preview-roadmap-planning-system", "demo_url": "https://datanest-stores.com/pm-toolkit/demo-roadmap-planning-system/" }, { "product_id": "pm-toolkit:sprint-management-kit", "slug": "sprint-management-kit", "store": "pm-toolkit", "store_name": "PM Toolkit Pro", "domain": "career", "category": "Career, Creator & Business", "tags": [ "career", "metrics", "okr", "prd", "product manager", "roadmap", "sprint", "stakeholder", "user research" ], "title": "Sprint Management Kit", "short_description": "Sprint planning templates, standup formats, retrospective frameworks, velocity tracking, and burndown chart generators.", "detailed_description": "PM Toolkit Pro | $29 Everything a technical PM or scrum master needs to run effective sprints. 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Includes: 12 pattern implementations across creational, structural, behavioral, and concurrency categories; Thread-safe singleton, async producer/consumer, and circuit breaker patterns; Modern Python \u2014 dataclasses, Protocol, functools; Comprehensive tests covering every pattern with real-world scenarios; Decision guide \u2014 know exactly when to reach for which pattern.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies", "pytest for running tests" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "from patterns.creational.singleton import Singleton\n\nclass AppConfig(metaclass=Singleton):\n def __init__(self) -> None:\n self.debug = False\n\na = AppConfig()\nb = AppConfig()\nassert a is b # Same instance", "from patterns.creational.factory import Factory\n\nclass NotificationFactory(Factory):\n pass\n\n@NotificationFactory.register(\"email\")\nclass EmailNotification:\n def send(self, msg: str) -> None:\n print(f\"Email: {msg}\")\n\nnotifier = NotificationFactory.create(\"email\")\nnotifier.send(\"Hello!\")" ], "included_files": [ "README.md", "guide/when-to-use-patterns.md", "guides/when-to-use-patterns.md", "patterns/behavioral/chain_of_responsibility.py", "patterns/behavioral/command.py", "patterns/behavioral/observer.py", "patterns/behavioral/strategy.py", "patterns/concurrency/circuit_breaker.py", "patterns/concurrency/producer_consumer.py", "patterns/creational/builder.py", "patterns/creational/factory.py", "patterns/creational/singleton.py", "patterns/structural/adapter.py", "patterns/structural/decorator.py", "patterns/structural/facade.py", "tests/test_patterns.py" ], "file_count": 16, "download_size_bytes": 58119, "download_size_human": "56.8 KB", "product_url": "https://datanest-stores.com/python-developer/#product-python-design-patterns", "preview_url": "https://datanest-stores.com/python-developer/preview-python-design-patterns", "demo_url": "https://datanest-stores.com/python-developer/demo-python-design-patterns/" }, { "product_id": "python-developer:data-validation-toolkit", "slug": "data-validation-toolkit", "store": "python-developer", "store_name": "Python Developer Pro", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "automation", "dev", "django", "fastapi", "flask", "microservices", "python", "testing" ], "title": "Data Validation Toolkit", "short_description": "Pydantic models, custom validators, schema evolution patterns, and data quality frameworks for Python applications.", "detailed_description": "Type-safe, pipeline-driven validation with schema support, business rules, and detailed error reporting. Includes: 5 validator types \u2014 base, type, schema, business rules, and file validators; Composable pipeline \u2014 chain validators in sequence with short-circuit or collect-all modes; YAML-driven schemas \u2014 define validation rules in config, not code; Rich error reports \u2014 JSON, plain text, or structured output with field paths; Function decorators \u2014 validate arguments automatically on function call.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "PyYAML", "pytest for running tests" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "from src.validators.type_validator import TypeValidator\n\nvalidator = TypeValidator({\"name\": str, \"age\": int, \"active\": bool})\nerrors = validator.validate({\"name\": \"Alice\", \"age\": \"thirty\", \"active\": True})\n# errors[0].field == \"age\", errors[0].message == \"Expected int, got str\"", "from src.validators.schema_validator import SchemaValidator\n\nvalidator = SchemaValidator.from_yaml(\"configs/schemas/user_schema.yaml\")\nerrors = validator.validate({\"email\": \"not-an-email\", \"age\": -5})" ], "included_files": [ "README.md", "configs/rules/business_rules.yaml", "configs/schemas/user_schema.yaml", "examples/validate_user_data.py", "guide/data-validation-guide.md", "guides/data-validation-guide.md", "src/decorators.py", "src/pipeline.py", "src/reporters.py", "src/validators/base.py", "src/validators/business_rules.py", "src/validators/file_validator.py", "src/validators/schema_validator.py", "src/validators/type_validator.py", "tests/test_validators.py" ], "file_count": 15, "download_size_bytes": 53191, "download_size_human": "51.9 KB", "product_url": "https://datanest-stores.com/python-developer/#product-data-validation-toolkit", "preview_url": "https://datanest-stores.com/python-developer/preview-data-validation-toolkit", "demo_url": "https://datanest-stores.com/python-developer/demo-data-validation-toolkit/" }, { "product_id": "python-developer:python-logging-config", "slug": "python-logging-config", "store": "python-developer", "store_name": "Python Developer Pro", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "automation", "dev", "django", "fastapi", "flask", "microservices", "python", "testing" ], "title": "Python Logging & Config", "short_description": "Structured logging with structlog, environment-based configuration, secrets management, and 12-factor app patterns.", "detailed_description": "Structured logging, request context, ASGI/WSGI middleware, and environment-specific configs in one drop-in package. Includes: One-call setup \u2014 configurelogging(\"prod\") loads the right YAML config; Structured JSON formatter \u2014 machine-readable logs for Datadog, ELK, CloudWatch; Request context \u2014 automatic requestid, userid in every log line; ASGI & WSGI middleware \u2014 plug into FastAPI, Starlette, Flask, Django; Smart filters \u2014 suppress noisy loggers, rate-limit repeated messages; 3 YAML configs \u2014 dev (colorized console), prod (JSON to file + stdout), test (minimal).", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "PyYAML", "pytest for running tests" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "from src.setup import configure_logging\n\nconfigure_logging(\"dev\") # Colorized console output\n\nimport logging\nlogger = logging.getLogger(__name__)\nlogger.info(\"Application started\")", "configure_logging(\"prod\")\nlogger.info(\"Order placed\", extra={\"order_id\": \"ORD-123\", \"total\": 49.99})\n# {\"timestamp\": \"2026-03-10T12:00:00Z\", \"level\": \"INFO\", \"message\": \"Order placed\", \"order_id\": \"ORD-123\", ...}" ], "included_files": [ "README.md", "configs/logging_dev.yaml", "configs/logging_prod.yaml", "configs/logging_test.yaml", "examples/celery_example.py", "examples/fastapi_example.py", "guide/logging-guide.md", "guides/logging-guide.md", "src/context.py", "src/filters.py", "src/formatters.py", "src/handlers.py", "src/middleware.py", "src/setup.py", "tests/test_logging.py" ], "file_count": 15, "download_size_bytes": 49050, "download_size_human": "47.9 KB", "product_url": "https://datanest-stores.com/python-developer/#product-python-logging-config", "preview_url": "https://datanest-stores.com/python-developer/preview-python-logging-config", "demo_url": "https://datanest-stores.com/python-developer/demo-python-logging-config/" }, { "product_id": "python-developer:python-performance-toolkit", "slug": "python-performance-toolkit", "store": "python-developer", "store_name": "Python Developer Pro", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "automation", "dev", "django", "fastapi", "flask", "microservices", "python", "testing" ], "title": "Python Performance Toolkit", "short_description": "Profiling tools, caching strategies, memory optimization, async patterns, and Cython/Numba acceleration guides.", "detailed_description": "Profile, benchmark, and optimize your Python code with confidence. Stop guessing where your bottlenecks are. Measure, compare, and ship faster code. ![Version](https://datanest.dev) ![Python](https://python.org) ![License](LICENSE) --- Includes: CPU Profiler \u2014 cProfile wrapper with flamegraph-compatible output; Memory Profiler \u2014 tracemalloc-based leak detection and allocation tracking; Line Profiler \u2014 Decorator-based line-by-line execution timing; Benchmark Runner \u2014 Statistical benchmark suite with comparison reports; Caching Strategies \u2014 LRU, TTL, memoize, and disk-backed cache; Lazy Loading \u2014 Lazy properties, deferred imports, and computation.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.9+", "No external dependencies for core profiling" ], "supported_versions": [ "Python 3.9+" ], "supported_technologies": [ "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "python-performance-toolkit/\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 manifest.json\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 src/\n\u2502 \u251c\u2500\u2500 profilers/\n\u2502 \u2502 \u251c\u2500\u2500 cpu_profiler.py\n\u2502 \u2502 \u251c\u2500\u2500 memory_profiler.py\n\u2502 \u2502 \u2514\u2500\u2500 line_profiler.py\n\u2502 \u251c\u2500\u2500 benchmarks/\n\u2502 \u2502 \u251c\u2500\u2500 benchmark_runner.py\n\u2502 \u2502 \u2514\u2500\u2500 decorators.py\n\u2502 \u2514\u2500\u2500 optimizers/\n\u2502 \u251c\u2500\u2500 caching.py\n\u2026", "from src.profilers.cpu_profiler import CPUProfiler\n\nprofiler = CPUProfiler()\n\nwith profiler.profile():\n my_expensive_function()\n\nprofiler.print_stats(top_n=20)\nprofiler.export_flamegraph(\"profile.folded\")" ], "included_files": [ "README.md", "configs/benchmark_config.yaml", "examples/optimize_api.py", "examples/optimize_data_processing.py", "guide/python-performance-guide.md", "guides/python-performance-guide.md", "src/benchmarks/benchmark_runner.py", "src/benchmarks/decorators.py", "src/optimizers/batch_processing.py", "src/optimizers/caching.py", "src/optimizers/data_structures.py", "src/optimizers/lazy_loading.py", "src/profilers/cpu_profiler.py", "src/profilers/line_profiler.py", "src/profilers/memory_profiler.py", "tests/test_benchmarks.py", "tests/test_profilers.py" ], "file_count": 17, "download_size_bytes": 71665, "download_size_human": "70.0 KB", "product_url": "https://datanest-stores.com/python-developer/#product-python-performance-toolkit", "preview_url": "https://datanest-stores.com/python-developer/preview-python-performance-toolkit", "demo_url": "https://datanest-stores.com/python-developer/demo-python-performance-toolkit/" }, { "product_id": "python-developer:oauth-auth-library", "slug": "oauth-auth-library", "store": "python-developer", "store_name": "Python Developer Pro", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "automation", "dev", "django", "fastapi", "flask", "microservices", "python", "testing" ], "title": "OAuth & Auth Library", "short_description": "Complete authentication library with OAuth2, JWT, RBAC, session management, and social login integration.", "detailed_description": "Production-ready authentication and authorization for Python web applications. JWT tokens, OAuth2 flows, RBAC, MFA, and session management \u2014 all in one library. ![Version](https://datanest.dev) ![Python](https://python.org) ![License](LICENSE) --- Includes: JWT Handler \u2014 Create, verify, refresh, and revoke tokens with RS256/HS256; Password Security \u2014 Argon2/bcrypt hashing with strength validation; OAuth2 Client \u2014 Authorization code, client credentials, and PKCE flows; Provider Integrations \u2014 Google and GitHub OAuth with extensible base class; RBAC \u2014 Role-based access control with permission decorators; Auth Middleware \u2014 Token extraction, verification, and user injection.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.9+", "pyjwt[crypto] \u2014 JWT encoding/decoding with RS256", "argon2-cffi or bcrypt \u2014 Password hashing", "httpx \u2014 Async HTTP for OAuth2 flows", "redis \u2014 Session storage", "pyotp \u2014 TOTP for MFA" ], "supported_versions": [ "Python 3.9+" ], "supported_technologies": [ "Redis", "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "oauth-auth-library/\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 manifest.json\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 src/auth/\n\u2502 \u251c\u2500\u2500 jwt_handler.py\n\u2502 \u251c\u2500\u2500 password.py\n\u2502 \u251c\u2500\u2500 oauth2_client.py\n\u2502 \u251c\u2500\u2500 providers/\n\u2502 \u2502 \u251c\u2500\u2500 base.py\n\u2502 \u2502 \u251c\u2500\u2500 google.py\n\u2502 \u2502 \u2514\u2500\u2500 github.py\n\u2502 \u251c\u2500\u2500 rbac.py\n\u2502 \u251c\u2500\u2500 middleware.py\n\u2026", "from src.auth.jwt_handler import JWTHandler\n\nhandler = JWTHandler(secret_key=\"your-secret\", algorithm=\"HS256\")\n\ntoken = handler.create_access_token(\n subject=\"user_123\",\n claims={\"role\": \"admin\"},\n expires_minutes=60,\n)\n\npayload = handler.verify_token(token)\nprint(payload[\"sub\"]) # \"user_123\"" ], "included_files": [ "README.md", "configs/auth_config.yaml", "examples/fastapi_auth.py", "examples/flask_auth.py", "guide/auth-security-guide.md", "guides/auth-security-guide.md", "src/auth/jwt_handler.py", "src/auth/mfa.py", "src/auth/middleware.py", "src/auth/oauth2_client.py", "src/auth/password.py", "src/auth/providers/base.py", "src/auth/providers/github.py", "src/auth/providers/google.py", "src/auth/rbac.py", "src/auth/session.py", "tests/test_jwt.py", "tests/test_oauth2.py", "tests/test_rbac.py" ], "file_count": 19, "download_size_bytes": 79893, "download_size_human": "78.0 KB", "product_url": "https://datanest-stores.com/python-developer/#product-oauth-auth-library", "preview_url": "https://datanest-stores.com/python-developer/preview-oauth-auth-library", "demo_url": "https://datanest-stores.com/python-developer/demo-oauth-auth-library/" }, { "product_id": "python-developer:python-microservices-kit", "slug": "python-microservices-kit", "store": "python-developer", "store_name": "Python Developer Pro", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "automation", "dev", "django", "fastapi", "flask", "microservices", "python", "testing" ], "title": "Python Microservices Kit", "short_description": "Microservice scaffolds with gRPC, message queues, service discovery, circuit breakers, and distributed tracing.", "detailed_description": "Production-ready microservices architecture with API gateway, service discovery, event-driven communication, and Docker orchestration. --- Includes: API Gateway with rate limiting, JWT auth, and request routing; User Service and Order Service as example bounded contexts; Shared library for events, service clients, health checks, and tracing; Docker Compose configs for dev and production; Integration tests covering cross-service workflows; Architecture guide explaining patterns and trade-offs.", "price_usd": 49, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "Docker & Docker Compose", "pip install fastapi uvicorn httpx pydantic" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "FastAPI", "Docker", "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "python-microservices-kit/\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 manifest.json\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 docker-compose.yml\n\u251c\u2500\u2500 services/\n\u2502 \u251c\u2500\u2500 api-gateway/\n\u2502 \u2502 \u251c\u2500\u2500 main.py # Gateway with routing & rate limiting\n\u2502 \u2502 \u2514\u2500\u2500 Dockerfile\n\u2502 \u251c\u2500\u2500 user-service/\n\u2502 \u2502 \u251c\u2500\u2500 main.py # User CRUD + auth endpoints\n\u2502 \u2502 \u251c\u2500\u2500 models.py # User domain models\n\u2502 \u2502 \u2514\u2500\u2500 Dockerfile\n\u2502 \u2514\u2500\u2500 order-service/\n\u2026", "docker-compose up --build" ], "included_files": [ "README.md", "configs/docker-compose.prod.yml", "docker-compose.yml", "guide/microservices-patterns.md", "guides/microservices-patterns.md", "services/api-gateway/Dockerfile", "services/api-gateway/main.py", "services/order-service/Dockerfile", "services/order-service/main.py", "services/order-service/models.py", "services/user-service/Dockerfile", "services/user-service/main.py", "services/user-service/models.py", "shared/events.py", "shared/health.py", "shared/service_client.py", "shared/tracing.py", "tests/test_integration.py" ], "file_count": 18, "download_size_bytes": 55395, "download_size_human": "54.1 KB", "product_url": "https://datanest-stores.com/python-developer/#product-python-microservices-kit", "preview_url": "https://datanest-stores.com/python-developer/preview-python-microservices-kit", "demo_url": "https://datanest-stores.com/python-developer/demo-python-microservices-kit/" }, { "product_id": "python-developer:python-automation-scripts", "slug": "python-automation-scripts", "store": "python-developer", "store_name": "Python Developer Pro", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "automation", "dev", "django", "fastapi", "flask", "microservices", "python", "testing" ], "title": "Python Automation Scripts", "short_description": "50+ automation scripts for file processing, email, PDF generation, spreadsheets, APIs, and system administration.", "detailed_description": "10 production-ready automation scripts for file management, data processing, API testing, log analysis, backups, and more. --- Includes: 10 standalone scripts \u2014 each solves a real DevOps / data task; Shared utilities for running scripts and sending notifications; YAML config for centralised settings; Tests for the most complex scripts; Guide with customisation tips and scheduling advice.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "pip install pyyaml requests jinja2", "Docker CLI", "Git CLI" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Docker", "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "pip install pyyaml requests jinja2", "python scripts/file_organizer.py --source ~/Downloads --dest ~/Sorted" ], "included_files": [ "README.md", "configs/scripts_config.yaml", "guide/automation-guide.md", "guides/automation-guide.md", "scripts/api_tester.py", "scripts/csv_processor.py", "scripts/db_backup.py", "scripts/docker_cleanup.py", "scripts/env_checker.py", "scripts/file_organizer.py", "scripts/git_stats.py", "scripts/log_analyzer.py", "scripts/report_generator.py", "scripts/ssl_checker.py", "tests/test_csv_processor.py", "tests/test_file_organizer.py", "utils/notifier.py", "utils/runner.py" ], "file_count": 18, "download_size_bytes": 64992, "download_size_human": "63.5 KB", "product_url": "https://datanest-stores.com/python-developer/#product-python-automation-scripts", "preview_url": "https://datanest-stores.com/python-developer/preview-python-automation-scripts", "demo_url": "https://datanest-stores.com/python-developer/demo-python-automation-scripts/" }, { "product_id": "python-developer:python-complete-bundle", "slug": "python-complete-bundle", "store": "python-developer", "store_name": "Python Developer Pro", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "automation", "bundle", "dev", "django", "fastapi", "flask", "microservices", "python", "testing" ], "title": "Python Complete Bundle", "short_description": "All 14 Python tools \u2014 $486 bought separately, yours for $159. Save $327 (67%): FastAPI scaffolds, utilities, packaging, and project templates.", "detailed_description": "Production-ready FastAPI boilerplate with async SQLAlchemy, JWT auth, and Docker \u2014 ship your API in hours, not weeks. ![Python 3.11+](https://www.python.org/downloads/) ![FastAPI](https://fastapi.tiangolo.com) ![License: MIT](LICENSE) --- Includes: Async-first architecture \u2014 SQLAlchemy 2; JWT authentication \u2014 Register, login, refresh tokens, role-based access control; User management \u2014 Full CRUD with pagination, soft-delete, and admin routes; Rate limiting \u2014 In-memory sliding window middleware (swap for Redis in production); Database migrations \u2014 Alembic with async support, auto-generate from models; Docker ready \u2014 Multi-stage Dockerfile, docker-compose with PostgreSQL & Redis.", "price_usd": 159, "currency": "usd", "is_bundle": true, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": 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Includes: Catalog Manager \u2014 Central CRUD coordinator with search, filtering, bulk operations, and JSON persistence; Bulk Importer \u2014 Import products from CSV or JSON with flexible column mapping and error reporting; Category Tree \u2014 Hierarchical category management with path computation, breadcrumbs, and tree traversal; Variant Manager \u2014 Generate product variants from option axes (Size x Color \u2192 SKUs) with price modifiers; SEO Generator \u2014 Auto-generate meta titles, descriptions, Open Graph tags, and JSON-LD structured data; Image Optimizer \u2014 Analyze product images, generate resize commands, and enforce naming conventions.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], 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"https://datanest-stores.com/retail-ecommerce/demo-product-catalog-manager/" }, { "product_id": "retail-ecommerce:pricing-engine-toolkit", "slug": "pricing-engine-toolkit", "store": "retail-ecommerce", "store_name": "Retail Automation Pro", "domain": "career", "category": "Career, Creator & Business", "tags": [ "analytics", "career", "e-commerce", "inventory management", "order fulfillment", "pricing engine", "retail automation" ], "title": "Pricing Engine Toolkit", "short_description": "Dynamic pricing rules, competitor price monitoring, margin calculator, discount scheduling, and A/B price testing framework.", "detailed_description": "A rules-based pricing engine with competitor monitoring, margin analysis, discount scheduling, and A/B price testing \u2014 all in pure Python with zero external dependencies.", "price_usd": 49, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "# Run the complete demo (all 5 modules)\ncd pricing-engine-toolkit/\npython scripts/run_pricing.py\n\n# Run the test suite\npython -m pytest tests/test_pricing.py -v", "from src.engine import PricingEngine\nfrom src.models import PricedProduct, PricingRule, RuleType\n\nengine = PricingEngine(global_min_margin=0.05)\n\n# Electronics get cost + 120% markup\nengine.add_rule(PricingRule(\n name=\"Electronics Markup\",\n rule_type=RuleType.PERCENTAGE_MARKUP,\n value=120.0,\n priority=50,\n conditions={\"category\": \"Electronics\"},\n))\n\n\u2026" ], "included_files": [ "README.md", "configs/pricing_config.yaml", "data/sample_competitor_prices.csv", "data/sample_products.csv", "data/sample_rules.json", "guide/01-pricing-strategy-foundations.md", "guide/02-dynamic-pricing-models.md", "guide/03-discount-optimization.md", "guide/04-margin-analysis.md", "guide/05-competitor-monitoring.md", "guides/pricing_guide.md", "scripts/run_pricing.py", "src/__init__.py", "src/ab_testing.py", "src/competitor.py", "src/engine.py", "src/margin.py", "src/models.py", "src/scheduler.py", "tests/test_pricing.py" ], "file_count": 20, "download_size_bytes": 108170, "download_size_human": "105.6 KB", "product_url": "https://datanest-stores.com/retail-ecommerce/#product-pricing-engine-toolkit", "preview_url": "https://datanest-stores.com/retail-ecommerce/preview-pricing-engine-toolkit", "demo_url": "https://datanest-stores.com/retail-ecommerce/demo-pricing-engine-toolkit/" }, { "product_id": "retail-ecommerce:order-fulfillment-pipeline", "slug": "order-fulfillment-pipeline", "store": "retail-ecommerce", "store_name": "Retail Automation Pro", "domain": "career", "category": "Career, Creator & Business", "tags": [ "analytics", "career", "e-commerce", "inventory management", "order fulfillment", "pricing engine", "retail automation" ], "title": "Order Fulfillment Pipeline", "short_description": "Order processing automation, shipping label generation, tracking notification system, and returns management workflow.", "detailed_description": "End-to-end order fulfillment system with state machine-driven lifecycle management, multi-carrier shipping, shipment tracking, returns processing, and fulfillment analytics \u2014 all in pure Python.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "cd order-fulfillment-pipeline/\npython scripts/run_fulfillment.py\npython -m pytest tests/test_fulfillment.py -v", "from src.pipeline import FulfillmentPipeline, PipelineConfig\nfrom src.models import Order, LineItem, Address\n\npipeline = FulfillmentPipeline(PipelineConfig(\n auto_confirm=True,\n free_shipping_threshold=75.0,\n))\n\norder = Order(\n customer_id=\"CUST-001\",\n items=[\n LineItem(sku=\"SKU-001\", name=\"Widget\", quantity=2, unit_price=29.99, weight_oz=8.0),\n ],\n shipping_address=Address(\n\u2026" ], "included_files": [ "README.md", "configs/fulfillment_config.yaml", "data/sample_orders.csv", "guide/01-overview.md", "guide/02-order-processing-automation.md", "guide/03-shipping-and-tracking-integration.md", "guides/fulfillment_guide.md", "scripts/run_fulfillment.py", "src/__init__.py", "src/carriers.py", "src/models.py", "src/pipeline.py", "src/returns.py", "src/shipping.py", "src/state_machine.py", "src/tracking.py", "tests/test_fulfillment.py" ], "file_count": 17, "download_size_bytes": 101116, "download_size_human": "98.7 KB", "product_url": "https://datanest-stores.com/retail-ecommerce/#product-order-fulfillment-pipeline", "preview_url": "https://datanest-stores.com/retail-ecommerce/preview-order-fulfillment-pipeline", "demo_url": "https://datanest-stores.com/retail-ecommerce/demo-order-fulfillment-pipeline/" }, { "product_id": "retail-ecommerce:ecommerce-analytics-dashboard", "slug": "ecommerce-analytics-dashboard", "store": "retail-ecommerce", "store_name": "Retail Automation Pro", "domain": "career", "category": "Career, Creator & Business", "tags": [ "analytics", "career", "e-commerce", "inventory management", "order fulfillment", "pricing engine", "retail automation" ], "title": "E-Commerce Analytics Dashboard", "short_description": "Sales analytics, customer cohort analysis, conversion funnel tracking, revenue forecasting, and KPI dashboards.", "detailed_description": "A complete Python analytics toolkit for e-commerce businesses. Covers every metric that matters: sales aggregation, customer cohort analysis, conversion funnel tracking, revenue forecasting, and KPI computation \u2014 all from raw transaction data. Includes: Sales Analytics Engine \u2014 daily/weekly/monthly revenue summaries, top products, category & channel breakdowns, period-over-period growth rates; Customer Cohort Analysis \u2014 retention matrices, cohort revenue curves, cumulative LTV by acquisition month; Conversion Funnel Tracker \u2014 multi-stage funnel modeling with drop-off rates, bottleneck detection, and per-segment breakdowns; Revenue Forecasting \u2014 moving average, weighted MA, linear trend, and seasonal decomposition \u2014 all pure Python, no numpy required; KPI Calculator \u2014 AOV, LTV, CAC, conversion rate, cart abandonment, repeat purchase rate, gross margin, and industry benchmark checks; Chart & Export Helpers \u2014 matplotlib bar/line/funnel charts (guarded import) plus CSV and JSON export.", "price_usd": 49, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No mandatory external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python", "SQL", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "# Run sales analysis on the sample data\npython -m src.sales_analytics data/sample_transactions.csv\n\n# Run cohort analysis\npython -m src.cohort_analysis data/sample_transactions.csv\n\n# Run the conversion funnel demo\npython -m src.funnel_tracker\n\n# Run revenue forecasting demo\npython -m src.revenue_forecast\n\n# Run all tests\npython -m pytest tests/ -v\n\u2026" ], "included_files": [ "README.md", "configs/dashboard_config.yaml", "data/sample_transactions.csv", "guide/01-overview.md", "guide/02-sales-analytics-and-kpis.md", "guide/03-cohort-analysis-and-funnel-tracking.md", "guides/analytics_guide.md", "sql/queries.sql", "src/__init__.py", "src/chart_export.py", "src/cohort_analysis.py", "src/funnel_tracker.py", "src/kpi_calculator.py", "src/revenue_forecast.py", "src/sales_analytics.py", "tests/test_analytics.py" ], "file_count": 16, "download_size_bytes": 90160, "download_size_human": "88.0 KB", "product_url": "https://datanest-stores.com/retail-ecommerce/#product-ecommerce-analytics-dashboard", "preview_url": "https://datanest-stores.com/retail-ecommerce/preview-ecommerce-analytics-dashboard", "demo_url": "https://datanest-stores.com/retail-ecommerce/demo-ecommerce-analytics-dashboard/" }, { "product_id": "retail-ecommerce:customer-segmentation-toolkit", "slug": "customer-segmentation-toolkit", "store": "retail-ecommerce", "store_name": "Retail Automation Pro", "domain": "career", "category": "Career, Creator & Business", "tags": [ "analytics", "career", "e-commerce", "inventory management", "order fulfillment", "pricing engine", "retail automation" ], "title": "Customer Segmentation Toolkit", "short_description": "RFM analysis, customer lifetime value calculation, churn prediction models, and personalized campaign targeting scripts.", "detailed_description": "A complete Python pipeline for customer segmentation: RFM scoring, lifetime value estimation, churn prediction, segment classification, and campaign targeting \u2014 all from raw transaction data. Includes: RFM Scoring Engine \u2014 Recency-Frequency-Monetary analysis with quintile scoring and named segment mapping (Champions, At Risk, Dormant, etc; CLV Calculator \u2014 Historical and predictive customer lifetime value with NPV discounting and tier assignment; Churn Prediction Model \u2014 Feature engineering pipeline + logistic regression / random forest classifier (scikit-learn, guarded); Segment Definitions \u2014 8 unified segments combining RFM, CLV, and churn data with recommended marketing actions for each; Campaign Targeting \u2014 Export audience lists for email platforms (Mailchimp/Klaviyo-ready CSV) and ad platforms (hashed emails for lookalike audiences), plus auto-generated campaign briefs; Sample Data \u2014 Customer and transaction CSVs ready to test the full pipeline.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No mandatory external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "# Run RFM analysis on sample data\npython -m src.rfm_engine data/sample_transactions.csv\n\n# Compute customer lifetime value\npython -m src.clv_calculator data/sample_transactions.csv\n\n# Run churn prediction\npython -m src.churn_predictor data/sample_transactions.csv\n\n# See segment definitions\npython -m src.segment_definitions\n\n# Run all tests\npython -m pytest tests/ -v", "from src.rfm_engine import RFMEngine\nfrom src.clv_calculator import CLVCalculator\n\n# RFM analysis\nengine = RFMEngine.from_csv(\"data/sample_transactions.csv\")\nengine.print_summary()\n\n# CLV estimation\ncalc = CLVCalculator.from_csv(\"data/sample_transactions.csv\")\ncalc.print_summary()" ], "included_files": [ "README.md", "configs/segmentation_config.yaml", "data/sample_customers.csv", "data/sample_transactions.csv", "guide/01-overview.md", "guide/02-rfm-analysis-for-customer-segmentation.md", "guide/03-customer-lifetime-value-and-churn-predic.md", "guides/segmentation_guide.md", "src/__init__.py", "src/campaign_targeting.py", "src/churn_predictor.py", "src/clv_calculator.py", "src/rfm_engine.py", "src/segment_definitions.py", "tests/test_segmentation.py" ], "file_count": 15, "download_size_bytes": 73724, "download_size_human": "72.0 KB", "product_url": "https://datanest-stores.com/retail-ecommerce/#product-customer-segmentation-toolkit", "preview_url": "https://datanest-stores.com/retail-ecommerce/preview-customer-segmentation-toolkit", "demo_url": "https://datanest-stores.com/retail-ecommerce/demo-customer-segmentation-toolkit/" }, { "product_id": "retail-ecommerce:shopping-cart-abandonment", "slug": "shopping-cart-abandonment", "store": "retail-ecommerce", "store_name": "Retail Automation Pro", "domain": "career", "category": "Career, Creator & Business", "tags": [ "analytics", "career", "e-commerce", "inventory management", "order fulfillment", "pricing engine", "retail automation" ], "title": "Cart Abandonment Recovery", "short_description": "Abandoned cart detection, email sequence automation, retargeting pixel setup, and recovery rate analytics dashboard.", "detailed_description": "A complete cart abandonment detection and recovery toolkit for e-commerce stores. Identifies abandoned carts from event data, triggers multi-step email sequences, generates retargeting pixel events for ad platforms, and measures recovery performance. Average e-commerce stores lose 70% of carts to abandonment. This system helps you recover 5-15% of that lost revenue through timed email sequences and retargeting. Includes: Cart Detection Engine \u2014 Identifies abandoned carts based on configurable time thresholds, categorizes by value tier and checkout stage reached, and prioritizes recovery targets; 3-Step Email Sequence \u2014 Pre-built HTML email templates (1-hour nudge, 24-hour social proof, 72-hour discount offer) with $variable substitution and send history tracking; Retargeting Pixel Events \u2014 Generates ready-to-use event payloads for Meta (Facebook) Pixel and Google Ads remarketing, plus JavaScript snippets for Dynamic Product Ads; Recovery Analytics \u2014 Measures recovery rates, revenue recaptured, email performance (open/click/conversion), and breakdown by cart value tier and checkout stage; Suppression Rules \u2014 Prevents duplicate sends, respects purchases and unsubscribes, enforces dai", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "from src.cart_detector import CartDetector\n\n# From CSV\ndetector = CartDetector.from_csv(\"data/sample_carts.csv\")\n\n# Or from a list of event dicts\ndetector = CartDetector(events, abandonment_threshold_minutes=60)\n\nabandoned = detector.detect_abandoned()\n# Returns: List[AbandonedCart] sorted by recovery priority\n\nsummary = detector.abandonment_summary()\n# Returns: AbandonmentSummary with rates, breakdowns, top targets", "from src.email_engine import EmailSequenceEngine\n\nengine = EmailSequenceEngine(template_dir=\".\")\nemails = engine.generate_emails(\n abandoned_carts=cart_dicts,\n send_history={\"CART-001\": {\"reminder_1h\"}}, # already sent\n purchased_cart_ids={\"CART-005\"}, # suppress\n)\n# Returns: List[EmailPayload] with rendered subject + HTML body" ], "included_files": [ "README.md", "configs/abandonment_config.yaml", "data/sample_carts.csv", "guide/01-overview.md", "guide/02-cart-abandonment-detection-and-triggers.md", "guide/03-recovery-analytics-and-optimization.md", "guides/setup_guide.md", "src/__init__.py", "src/cart_detector.py", "src/email_engine.py", "src/recovery_analytics.py", "src/retargeting_pixels.py", "templates/final_offer.html", "templates/reminder_1h.html", "templates/reminder_24h.html", "tests/test_abandonment.py" ], "file_count": 16, "download_size_bytes": 88912, "download_size_human": "86.8 KB", "product_url": "https://datanest-stores.com/retail-ecommerce/#product-shopping-cart-abandonment", "preview_url": "https://datanest-stores.com/retail-ecommerce/preview-shopping-cart-abandonment", "demo_url": "https://datanest-stores.com/retail-ecommerce/demo-shopping-cart-abandonment/" }, { "product_id": "retail-ecommerce:product-recommendation-engine", "slug": "product-recommendation-engine", "store": "retail-ecommerce", "store_name": "Retail Automation Pro", "domain": "career", "category": "Career, Creator & Business", "tags": [ "analytics", "career", "e-commerce", "inventory management", "order fulfillment", "pricing engine", "retail automation" ], "title": "Product Recommendation Engine", "short_description": "Collaborative filtering, content-based recommendations, trending products algorithm, and A/B testing integration.", "detailed_description": "A complete recommendation system for e-commerce stores. Implements collaborative filtering, content-based filtering, trending detection, and hybrid blending \u2014 all from scratch using Python's standard library. Includes A/B testing and evaluation metrics for measuring recommendation quality. Includes: Collaborative Filtering \u2014 User-based and item-based CF using cosine similarity; Content-Based Filtering \u2014 TF-IDF weighted feature vectors from product attributes (category, tags, brand, title words); Trending Detection \u2014 Three popularity signals; Hybrid Blender \u2014 Merges all algorithms into a single ranked list; A/B Testing Harness \u2014 Deterministic user-to-variant assignment, event tracking, and statistical significance testing with Z-test for conversion rate comparison; Evaluation Suite \u2014 Precision@K, Recall@K, NDCG@K, MAP@K, Hit Rate, Coverage, and multi-algorithm comparison.", "price_usd": 49, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "cf = CollaborativeFilter.from_csv(\"data/sample_interactions.csv\")\n\n# User-based: \"users like you also bought...\"\nrecs = cf.recommend_for_user(\"U001\", n=10, method=\"user\")\n\n# Item-based: \"frequently bought together\"\nsimilar = cf.similar_items(\"P001\", n=5)\n\n# Diagnostics\ncf.print_stats() # sparsity, user/product counts", "rec = ContentBasedRecommender.from_csv(\"data/sample_products.csv\")\n\n# Product similarity (for \"You might also like\")\nsimilar = rec.similar_products(\"P001\", n=5)\n\n# User profile recommendations\nhistory = [{\"product_id\": \"P001\", \"score\": 5}, {\"product_id\": \"P004\", \"score\": 4}]\nrecs = rec.recommend_for_user(history, n=10)\n\n# Explain why two products are similar\nexp = rec.explain_similarity(\"P001\", \"P002\")" ], "included_files": [ "README.md", "configs/recommender_config.yaml", "data/sample_interactions.csv", "data/sample_products.csv", "guide/01-overview.md", "guide/02-recommendation-algorithms-explained.md", "guide/03-a-b-testing-recommendations.md", "guides/recommendation_guide.md", "src/__init__.py", "src/ab_testing.py", "src/collaborative_filter.py", "src/content_based.py", "src/evaluation.py", "src/hybrid_blender.py", "src/trending.py", "tests/test_recommender.py" ], "file_count": 16, "download_size_bytes": 98844, "download_size_human": "96.5 KB", "product_url": "https://datanest-stores.com/retail-ecommerce/#product-product-recommendation-engine", "preview_url": "https://datanest-stores.com/retail-ecommerce/preview-product-recommendation-engine", "demo_url": "https://datanest-stores.com/retail-ecommerce/demo-product-recommendation-engine/" }, { "product_id": "retail-ecommerce:multi-channel-sync", "slug": "multi-channel-sync", "store": "retail-ecommerce", "store_name": "Retail Automation Pro", "domain": "career", "category": "Career, Creator & Business", "tags": [ "analytics", "career", "e-commerce", "inventory management", "order fulfillment", "pricing engine", "retail automation" ], "title": "Multi-Channel Sync Toolkit", "short_description": "Inventory and order sync across Shopify, WooCommerce, Amazon, and eBay with conflict resolution and error handling.", "detailed_description": "Synchronize inventory levels, orders, and product data across Shopify, WooCommerce, Amazon, and eBay from a single Python codebase. Stop manually updating stock counts across four dashboards. This package gives you a unified sync engine with channel-specific adapters, automatic conflict resolution, and retry logic that handles the inevitable API failures. --- Includes: Channel adapter interface with concrete implementations for Shopify, WooCommerce, Amazon, and eBay; Sync engine that orchestrates bi-directional inventory and order synchronization; Conflict resolution with four strategies; Data normalization layer that maps each channel's quirky field names to a unified schema; Retry handler with exponential backoff, jitter, circuit breaker, and dead-letter logging; Unified data models for products, inventory, orders, and line items.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "cp configs/channels.yaml configs/channels_local.yaml\n# Edit channels_local.yaml with your API credentials", "from src.config import SyncConfig\nfrom src.sync_engine import SyncEngine\n\nconfig = SyncConfig.from_yaml(\"configs/channels_local.yaml\")\nengine = SyncEngine(config)\n\n# Pull inventory from all channels, reconcile, and push updates\nreport = engine.run_full_sync()\nprint(report.summary())" ], "included_files": [ "README.md", "configs/channels.yaml", "data/sample_catalog.json", "guide/01-overview.md", "guide/02-multi-channel-architecture.md", "guide/03-conflict-resolution-and-error-handling.md", "guides/multi-channel-guide.md", "src/__init__.py", "src/adapters/__init__.py", "src/adapters/amazon_adapter.py", "src/adapters/ebay_adapter.py", "src/adapters/shopify_adapter.py", "src/adapters/woocommerce_adapter.py", "src/channel_adapter.py", "src/config.py", "src/conflict_resolver.py", "src/models.py", "src/normalizer.py", "src/retry_handler.py", "src/sync_engine.py", "tests/test_sync.py" ], "file_count": 21, "download_size_bytes": 122120, "download_size_human": "119.3 KB", "product_url": "https://datanest-stores.com/retail-ecommerce/#product-multi-channel-sync", "preview_url": "https://datanest-stores.com/retail-ecommerce/preview-multi-channel-sync", "demo_url": "https://datanest-stores.com/retail-ecommerce/demo-multi-channel-sync/" }, { "product_id": "retail-ecommerce:retail-reporting-templates", "slug": "retail-reporting-templates", "store": "retail-ecommerce", "store_name": "Retail Automation Pro", "domain": "career", "category": "Career, Creator & Business", "tags": [ "analytics", "career", "e-commerce", "inventory management", "order fulfillment", "pricing engine", "retail automation" ], "title": "Retail Reporting Templates", "short_description": "50+ retail report templates: daily sales, inventory turnover, sell-through rates, vendor performance, and seasonal analysis.", "detailed_description": "52 ready-to-use SQL report templates covering every aspect of retail analytics. Each query is documented with its purpose, required parameters, expected output columns, and business context. Plug them into your database, adjust the table names if needed, and start generating insights immediately. Includes: Base retail database schema (sql/schema; Python report runner for executing any template against your database; Metrics definitions reference with formulas and interpretation guidance; CSV output templates showing expected report formats.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python", "SQL" ], "compatibility_notes": null, "preview_snippets": [ "psql -d your_database -f sql/schema.sql", "# Direct execution with psql\npsql -d your_database -v report_date=\"'2025-06-01'\" -f sql/sales/daily_sales_summary.sql\n\n# Or use the Python runner\npython src/report_runner.py --report sales/daily_sales_summary --date 2025-06-01" ], "included_files": [ "README.md", "docs/metrics_definitions.md", "guide/01-overview.md", "guide/02-daily-sales-and-performance-reports.md", "guide/03-inventory-and-vendor-analytics.md", "guides/reporting-guide.md", "reports/daily_sales_sample.csv", "reports/inventory_aging_sample.csv", "reports/margin_analysis_sample.csv", "sql/finance/basket_analysis.sql", "sql/finance/breakeven.sql", "sql/finance/cash_flow.sql", "sql/finance/cogs.sql", "sql/finance/gross_margin.sql", "sql/finance/operating_expense.sql", "sql/finance/price_elasticity.sql", "sql/finance/profit_by_category.sql", "sql/finance/refund_impact.sql", "sql/finance/revenue_by_store.sql", "sql/finance/shipping_cost.sql", "sql/finance/tax_collection.sql", "sql/inventory/abc_classification.sql", "sql/inventory/aging.sql", "sql/inventory/dead_stock.sql", "sql/inventory/reorder_point.sql", "sql/inventory/sell_through_rate.sql", "sql/inventory/shrinkage.sql", "sql/inventory/stock_to_sales.sql", "sql/inventory/turnover.sql", "sql/inventory/valuation.sql", "sql/inventory/warehouse_utilization.sql", "sql/sales/avg_order_value.sql", "sql/sales/by_category.sql", "sql/sales/by_channel.sql", "sql/sales/by_payment_method.sql", "sql/sales/customer_acquisition.sql", "sql/sales/daily_sales_summary.sql", "sql/sales/discount_impact.sql", "sql/sales/forecast_baseline.sql", "sql/sales/growth_comparison.sql", "sql/sales/hourly_breakdown.sql", "sql/sales/return_rate.sql", "sql/sales/top_products.sql", "sql/schema.sql", "sql/seasonal/demand_pattern.sql", "sql/seasonal/event_impact.sql", "sql/seasonal/holiday_performance.sql", "sql/seasonal/inventory_planning.sql", "sql/seasonal/monthly_trend.sql", "sql/seasonal/peak_hour.sql", "sql/seasonal/promo_calendar.sql", "sql/seasonal/weather_correlation.sql", "sql/seasonal/weekly_patterns.sql", "sql/seasonal/yoy_comparison.sql", "sql/vendor/cost_comparison.sql", "sql/vendor/fill_rate.sql", "sql/vendor/lead_time.sql", "sql/vendor/payment_aging.sql", "sql/vendor/performance_scorecard.sql", "sql/vendor/po_status.sql", "sql/vendor/quality_metrics.sql", "sql/vendor/return_rate.sql", "src/__init__.py", "src/report_runner.py" ], "file_count": 64, "download_size_bytes": 142599, "download_size_human": "139.3 KB", "product_url": "https://datanest-stores.com/retail-ecommerce/#product-retail-reporting-templates", "preview_url": "https://datanest-stores.com/retail-ecommerce/preview-retail-reporting-templates", "demo_url": "https://datanest-stores.com/retail-ecommerce/demo-retail-reporting-templates/" }, { "product_id": "retail-ecommerce:payment-reconciliation-toolkit", "slug": "payment-reconciliation-toolkit", "store": "retail-ecommerce", "store_name": "Retail Automation Pro", "domain": "career", "category": "Career, Creator & Business", "tags": [ "analytics", "career", "e-commerce", "inventory management", "order fulfillment", "pricing engine", "retail automation" ], "title": "Payment Reconciliation Toolkit", "short_description": "Multi-gateway payment reconciliation scripts, refund tracking, dispute management, and financial reporting automation.", "detailed_description": "Match gateway payouts to orders, track refunds, manage disputes, and generate financial reports -- all from Python scripts that parse real settlement file formats. Every payment gateway settles on its own schedule with its own file format. This toolkit parses settlement exports from Stripe and PayPal, matches them to your order records, flags discrepancies, tracks refunds through their lifecycle, and produces reconciliation reports ready for your accountant. --- Includes: Reconciliation engine that matches gateway settlement line items to internal order records using configurable matching strategies (exact, fuzzy amount, date-window); Gateway parsers for Stripe and PayPal settlement/payout file formats (CSV-based), with an abstract adapter for adding more gateways; Refund tracker that follows refunds from initiation through gateway confirmation, detecting stuck and partial refunds; Dispute & chargeback manager with lifecycle tracking, response deadline monitoring, and win/loss analytics; Financial report generator producing reconciliation summaries, aging reports, fee analysis, and daily settlement breakdowns; SQL schema for storing reconciliation data in any relational database.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python", "SQL" ], "compatibility_notes": null, "preview_snippets": [ "from src.adapters.stripe_parser import StripeParser\n\nparser = StripeParser()\nsettlements = parser.parse_file(\"data/stripe_settlement_sample.csv\")\n\nfor s in settlements[:5]:\n print(f\"{s.transaction_id}: {s.amount} {s.currency} ({s.transaction_type})\")", "from src.reconciliation_engine import ReconciliationEngine\n\nengine = ReconciliationEngine()\n\n# Load your internal orders\norders = engine.load_orders_from_csv(\"data/orders_sample.csv\")\n\n# Match settlement lines to orders\nreport = engine.reconcile(settlements, orders)\n\nprint(f\"Matched: {report.matched_count}\")\nprint(f\"Unmatched: {report.unmatched_settlement_count}\")\nprint(f\"Missing: {report.missing_payout_count}\")\nprint(f\"Discrepant: {len(report.discrepancies)}\")" ], "included_files": [ "README.md", "data/orders_sample.csv", "data/paypal_settlement_sample.csv", "data/stripe_settlement_sample.csv", "guide/01-overview.md", "guide/02-multi-gateway-reconciliation.md", "guide/03-refund-and-dispute-management.md", "guides/reconciliation-guide.md", "sql/schema.sql", "src/__init__.py", "src/adapters/__init__.py", "src/adapters/paypal_parser.py", "src/adapters/stripe_parser.py", "src/config.py", "src/dispute_manager.py", "src/financial_reporter.py", "src/gateway_adapter.py", "src/models.py", "src/reconciliation_engine.py", "src/refund_tracker.py", "tests/test_reconciliation.py" ], "file_count": 21, "download_size_bytes": 104278, "download_size_human": "101.8 KB", "product_url": "https://datanest-stores.com/retail-ecommerce/#product-payment-reconciliation-toolkit", "preview_url": "https://datanest-stores.com/retail-ecommerce/preview-payment-reconciliation-toolkit", "demo_url": "https://datanest-stores.com/retail-ecommerce/demo-payment-reconciliation-toolkit/" }, { "product_id": "retail-ecommerce:retail-complete-bundle", "slug": "retail-complete-bundle", "store": "retail-ecommerce", "store_name": "Retail Automation Pro", "domain": "career", "category": "Career, Creator & Business", "tags": [ "analytics", "bundle", "career", "e-commerce", "inventory management", "order fulfillment", "pricing engine", "retail automation" ], "title": "Retail Automation Bundle", "short_description": "All 11 retail tools \u2014 $449 bought separately, yours for $139. Save $310 (69%): e-commerce automation, inventory tools, and retail analytics.", "detailed_description": "Complete Python toolkit for real-time inventory tracking across multiple warehouses, with automated low-stock alerts, reorder point / EOQ calculations, and CSV/API synchronization. Includes: Stock Tracker \u2014 Central coordinator that maintains stock levels and records every movement with a full audit trail; Low-Stock Alerts \u2014 Configurable alert system with WARNING, CRITICAL, and STOCKOUT levels, cooldown periods, and pluggable handlers; Reorder Engine \u2014 Automated reorder-point and Economic Order Quantity (EOQ) calculations with purchase order generation; Multi-Warehouse Allocation \u2014 Three allocation strategies (priority, nearest, balanced) for deciding which warehouse fulfills each order; CSV Import/Export \u2014 Bulk data operations with flexible column mapping (handles different CSV formats from various systems); Sync Adapter Interface \u2014 Abstract adapter pattern for connecting to external systems (Shopify, WooCommerce, ERPs, etc.", "price_usd": 139, "currency": "usd", "is_bundle": true, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python", "SQL", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "# Run the complete demo\npython scripts/run_inventory.py", "python -m pytest tests/ -v\n# or:\npython -m unittest discover tests/" ], "included_files": [ "products/customer-segmentation-toolkit/README.md", "products/customer-segmentation-toolkit/source/README.md", "products/customer-segmentation-toolkit/source/configs/segmentation_config.yaml", "products/customer-segmentation-toolkit/source/data/sample_customers.csv", "products/customer-segmentation-toolkit/source/data/sample_transactions.csv", "products/customer-segmentation-toolkit/source/guide/01-overview.md", "products/customer-segmentation-toolkit/source/guide/02-rfm-analysis-for-customer-segmentation.md", "products/customer-segmentation-toolkit/source/guide/03-customer-lifetime-value-and-churn-predic.md", "products/customer-segmentation-toolkit/source/guides/segmentation_guide.md", "products/customer-segmentation-toolkit/source/src/__init__.py", "products/customer-segmentation-toolkit/source/src/campaign_targeting.py", "products/customer-segmentation-toolkit/source/src/churn_predictor.py", "products/customer-segmentation-toolkit/source/src/clv_calculator.py", "products/customer-segmentation-toolkit/source/src/rfm_engine.py", "products/customer-segmentation-toolkit/source/src/segment_definitions.py", "products/customer-segmentation-toolkit/source/tests/test_segmentation.py", "products/ecommerce-analytics-dashboard/README.md", "products/ecommerce-analytics-dashboard/source/README.md", "products/ecommerce-analytics-dashboard/source/configs/dashboard_config.yaml", "products/ecommerce-analytics-dashboard/source/data/sample_transactions.csv", "products/ecommerce-analytics-dashboard/source/guide/01-overview.md", "products/ecommerce-analytics-dashboard/source/guide/02-sales-analytics-and-kpis.md", "products/ecommerce-analytics-dashboard/source/guide/03-cohort-analysis-and-funnel-tracking.md", "products/ecommerce-analytics-dashboard/source/guides/analytics_guide.md", "products/ecommerce-analytics-dashboard/source/sql/queries.sql", "products/ecommerce-analytics-dashboard/source/src/__init__.py", "products/ecommerce-analytics-dashboard/source/src/chart_export.py", "products/ecommerce-analytics-dashboard/source/src/cohort_analysis.py", "products/ecommerce-analytics-dashboard/source/src/funnel_tracker.py", "products/ecommerce-analytics-dashboard/source/src/kpi_calculator.py", "products/ecommerce-analytics-dashboard/source/src/revenue_forecast.py", "products/ecommerce-analytics-dashboard/source/src/sales_analytics.py", "products/ecommerce-analytics-dashboard/source/tests/test_analytics.py", "products/inventory-management-system/README.md", "products/inventory-management-system/source/README.md", "products/inventory-management-system/source/configs/inventory_config.yaml", "products/inventory-management-system/source/data/sample_products.csv", "products/inventory-management-system/source/data/sample_stock.csv", "products/inventory-management-system/source/guide/01-overview.md", "products/inventory-management-system/source/guide/02-real-time-inventory-tracking.md", "products/inventory-management-system/source/guide/03-reorder-automation-and-multi-warehouse-s.md", "products/inventory-management-system/source/guides/setup_guide.md", "products/inventory-management-system/source/scripts/run_inventory.py", "products/inventory-management-system/source/sql/schema.sql", "products/inventory-management-system/source/src/__init__.py", "products/inventory-management-system/source/src/alerts.py", "products/inventory-management-system/source/src/csv_sync.py", "products/inventory-management-system/source/src/models.py", "products/inventory-management-system/source/src/reorder.py", "products/inventory-management-system/source/src/sync_adapter.py", "products/inventory-management-system/source/src/tracker.py", "products/inventory-management-system/source/src/warehouse.py", "products/inventory-management-system/source/tests/test_inventory.py", "products/multi-channel-sync/README.md", "products/multi-channel-sync/source/README.md", "products/multi-channel-sync/source/configs/channels.yaml", "products/multi-channel-sync/source/data/sample_catalog.json", "products/multi-channel-sync/source/guide/01-overview.md", "products/multi-channel-sync/source/guide/02-multi-channel-architecture.md", "products/multi-channel-sync/source/guide/03-conflict-resolution-and-error-handling.md", "products/multi-channel-sync/source/guides/multi-channel-guide.md", "products/multi-channel-sync/source/src/__init__.py", "products/multi-channel-sync/source/src/adapters/__init__.py", "products/multi-channel-sync/source/src/adapters/amazon_adapter.py", "products/multi-channel-sync/source/src/adapters/ebay_adapter.py", "products/multi-channel-sync/source/src/adapters/shopify_adapter.py", "products/multi-channel-sync/source/src/adapters/woocommerce_adapter.py", "products/multi-channel-sync/source/src/channel_adapter.py", "products/multi-channel-sync/source/src/config.py", "products/multi-channel-sync/source/src/conflict_resolver.py", "products/multi-channel-sync/source/src/models.py", "products/multi-channel-sync/source/src/normalizer.py", "products/multi-channel-sync/source/src/retry_handler.py", "products/multi-channel-sync/source/src/sync_engine.py", "products/multi-channel-sync/source/tests/test_sync.py", "products/order-fulfillment-pipeline/README.md", "products/order-fulfillment-pipeline/source/README.md", "products/order-fulfillment-pipeline/source/configs/fulfillment_config.yaml", "products/order-fulfillment-pipeline/source/data/sample_orders.csv", "products/order-fulfillment-pipeline/source/guide/01-overview.md" ], "file_count": 80, "download_size_bytes": 1120972, "download_size_human": "1.1 MB", "product_url": "https://datanest-stores.com/retail-ecommerce/#product-retail-complete-bundle", "preview_url": null, "demo_url": null }, { "product_id": "saas-starter:admin-panel", "slug": "admin-panel", "store": "saas-starter", "store_name": "SaaS Starter", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "admin dashboard", "billing", "dev", "multi-tenant", "saas", "subscription", "user management" ], "title": "Admin Panel", "short_description": "Admin panel generator with CRUD operations, user management, system settings, and audit logs.", "detailed_description": "A Python admin panel generator for SaaS applications. Provides automatic CRUD operations, user management, system settings, role-based access control, and a comprehensive audit log \u2014 all built on Python's standard library with an HTML-rendering admin interface. Includes: CRUD generator \u2014 Register any data model and get full create/read/update/delete endpoints and HTML forms; User management \u2014 List, create, disable, reset passwords, assign roles; Role-based access \u2014 Admin, editor, viewer roles with per-resource permission checks; System settings \u2014 Key-value settings store with typed values and change tracking; Audit log \u2014 Every admin action recorded with actor, action, resource, and timestamp; Search & pagination \u2014 Built-in search across any registered model with paginated results.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "python src/main.py --init-demo", "# List all users\ncurl http://localhost:8007/api/users\n\n# Create a user\ncurl -X POST http://localhost:8007/api/users \\\n -H \"Content-Type: application/json\" \\\n -d '{\"username\": \"newuser\", \"email\": \"newuser@docs.example.com\", \"role\": \"editor\"}'\n\n# Get system settings\ncurl http://localhost:8007/api/settings\n\n# Update a setting\ncurl -X PUT http://localhost:8007/api/settings/site_name \\\n -H \"Content-Type: application/json\" \\\n\u2026" ], "included_files": [ "README.md", "examples/basic_example.py", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_project-structure.md", "src/main.py" ], "file_count": 6, "download_size_bytes": 47453, "download_size_human": "46.3 KB", "product_url": "https://datanest-stores.com/saas-starter/#product-admin-panel", "preview_url": "https://datanest-stores.com/saas-starter/preview-admin-panel", "demo_url": "https://datanest-stores.com/saas-starter/demo-admin-panel/" }, { "product_id": "saas-starter:api-rate-controller", "slug": "api-rate-controller", "store": "saas-starter", "store_name": "SaaS Starter", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "admin dashboard", "billing", "dev", "multi-tenant", "saas", "subscription", "user management" ], "title": "API Rate Controller", "short_description": "API rate limiting with per-tenant and per-plan rate limits using sliding windows.", "detailed_description": "A Python API rate limiting system for SaaS applications. Provides per-tenant and per-plan rate limiting with sliding window counters, quota management, throttling, overage handling, and real-time limit headers \u2014 all built on Python's standard library. Includes: Per-tenant limits \u2014 Different rate limits for each tenant based on their plan; Sliding window \u2014 Accurate rate limiting using sliding window counters (not just fixed windows); Multiple windows \u2014 Enforce per-second, per-minute, per-hour, and per-day limits simultaneously; Plan-based tiers \u2014 Define rate limit tiers (free, starter, pro, enterprise) with different limits; Quota management \u2014 Monthly/daily quotas separate from burst rate limits; Throttling \u2014 Gradual slowdown near limits instead of hard cutoff (optional).", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "python src/main.py --init-demo", "# Check rate limit status for a tenant\ncurl http://localhost:8009/api/limits/tenant_acme\n\n# Simulate an API request (consumes 1 unit)\ncurl -X POST http://localhost:8009/api/check \\\n -H \"Content-Type: application/json\" \\\n -d '{\"tenant_id\": \"tenant_acme\", \"endpoint\": \"/api/data\", \"cost\": 1}'\n\n# Get quota status\ncurl http://localhost:8009/api/quota/tenant_acme\n\n# View rate limit analytics\ncurl http://localhost:8009/api/analytics/tenant_acme\n\n\u2026" ], "included_files": [ "README.md", "examples/basic_example.py", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_rate-limit-headers.md", "guide/04_license.md", "src/main.py" ], "file_count": 7, "download_size_bytes": 44633, "download_size_human": "43.6 KB", "product_url": "https://datanest-stores.com/saas-starter/#product-api-rate-controller", "preview_url": "https://datanest-stores.com/saas-starter/preview-api-rate-controller", "demo_url": "https://datanest-stores.com/saas-starter/demo-api-rate-controller/" }, { "product_id": "saas-starter:billing-integration", "slug": "billing-integration", "store": "saas-starter", "store_name": "SaaS Starter", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "admin dashboard", "billing", "dev", "multi-tenant", "saas", "subscription", "user management" ], "title": "Billing Integration", "short_description": "Python billing system for SaaS with subscription plans, usage metering, and invoicing.", "detailed_description": "A complete Python billing system for SaaS applications. Handles subscription plans, customer management, usage metering, invoice generation, and webhook processing \u2014 all built on Python's standard library. Includes: Subscription plans \u2014 Free, flat-rate, and metered billing with monthly/yearly intervals; Customer management \u2014 Create customers, link to your internal user/tenant IDs; Usage metering \u2014 Record API calls, storage, compute with idempotency protection; Invoice generation \u2014 Automatic line items for base plan + metered usage; Webhook processing \u2014 Verify HMAC-SHA256 signatures, dispatch to typed handlers; Trial support \u2014 Configurable trial periods per subscription.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Start the billing server with demo data\npython src/main.py --init-demo\n\n# Start on a custom port\npython src/main.py --port 8001", "billing-integration/\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 src/\n\u2502 \u2514\u2500\u2500 main.py # Complete billing engine + HTTP API\n\u2514\u2500\u2500 examples/\n \u2514\u2500\u2500 basic_example.py # Full billing lifecycle demo" ], "included_files": [ "README.md", "examples/basic_example.py", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_configuration.md", "guide/04_project-structure.md", "src/main.py" ], "file_count": 7, "download_size_bytes": 45439, "download_size_human": "44.4 KB", "product_url": "https://datanest-stores.com/saas-starter/#product-billing-integration", "preview_url": "https://datanest-stores.com/saas-starter/preview-billing-integration", "demo_url": "https://datanest-stores.com/saas-starter/demo-billing-integration/" }, { "product_id": "saas-starter:dashboard-builder", "slug": "dashboard-builder", "store": "saas-starter", "store_name": "SaaS Starter", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "admin dashboard", "billing", "dev", "multi-tenant", "saas", "subscription", "user management" ], "title": "Dashboard Builder", "short_description": "HTML dashboard template generator with widgets, SVG charts, data tables, and filters.", "detailed_description": "A Python dashboard template generator that creates self-contained HTML dashboards with widgets, SVG charts, data tables, filters, and CSV/JSON export. No JavaScript libraries, no external dependencies \u2014 just Python's standard library. Includes: Metric cards \u2014 KPI widgets with values, change indicators, and icons; SVG charts \u2014 Bar, line, and pie/donut charts rendered as inline SVG (no Chart; Data tables \u2014 Sortable columns with currency/percent/date formatting; CSV/JSON export \u2014 One-click data export from any table; Filters \u2014 Select dropdowns, date ranges, and search inputs; Dark theme \u2014 Professional dark UI with configurable brand color.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Generate a demo SaaS metrics dashboard\npython src/main.py --demo\n\n# Generate from a JSON config\npython src/main.py --json examples/config.json\n\n# Specify output directory\npython src/main.py --demo --output ./my-dashboards", "from main import DashboardConfig, MetricCard, ChartData, DataTable, DashboardGenerator\n\nconfig = DashboardConfig(\n title=\"My Dashboard\",\n metrics=[\n MetricCard(title=\"Revenue\", value=\"$12,345\", change=8.5, icon=\"$\"),\n ],\n charts=[\n ChartData(\n title=\"Monthly Revenue\",\n chart_type=\"bar\",\n labels=[\"Jan\", \"Feb\", \"Mar\"],\n datasets=[{\"label\": \"MRR\", \"values\": [10000, 11500, 12345], \"color\": \"#4F46E5\"}],\n ),\n\u2026" ], "included_files": [ "README.md", "examples/basic_example.py", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_widget-types.md", "guide/04_license.md", "src/main.py" ], "file_count": 7, "download_size_bytes": 44306, "download_size_human": "43.3 KB", "product_url": "https://datanest-stores.com/saas-starter/#product-dashboard-builder", "preview_url": "https://datanest-stores.com/saas-starter/preview-dashboard-builder", "demo_url": "https://datanest-stores.com/saas-starter/demo-dashboard-builder/" }, { "product_id": "saas-starter:feature-flags", "slug": "feature-flags", "store": "saas-starter", "store_name": "SaaS Starter", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "admin dashboard", "billing", "dev", "multi-tenant", "saas", "subscription", "user management" ], "title": "Feature Flags", "short_description": "Feature flag system with boolean flags, percentage rollouts, user targeting, and A/B tests.", "detailed_description": "A Python feature flag system for SaaS applications. Supports boolean flags, percentage rollouts, user targeting rules, A/B testing with deterministic variant assignment, kill switches, and scheduled activations \u2014 all built on Python's standard library. Includes: Boolean flags \u2014 Simple on/off toggles for any feature; Percentage rollouts \u2014 Gradual rollout via deterministic hashing (consistent per user); User targeting \u2014 Rules matching on user attributes (plan, email, role, etc; A/B testing \u2014 Multiple variants with consistent assignment per user; Kill switch \u2014 Emergency one-call disable for any flag; Scheduled flags \u2014 Auto-enable/disable at specified times.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Start with demo flags\npython src/main.py --init-demo\n\n# Custom port\npython src/main.py --port 8003", "# List all flags\ncurl http://localhost:8003/api/flags\n\n# Evaluate a flag for a user\ncurl -X POST http://localhost:8003/api/flags/new_dashboard/evaluate \\\n -H \"Content-Type: application/json\" \\\n -d '{\"context\": {\"user_id\": \"user_123\", \"plan\": \"pro\", \"email\": \"user@docs.example.com\"}}'\n\n# Kill switch\ncurl -X POST http://localhost:8003/api/flags/new_dashboard/kill\n\n# Revive\ncurl -X POST http://localhost:8003/api/flags/new_dashboard/revive" ], "included_files": [ "README.md", "examples/basic_example.py", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_project-structure.md", "src/main.py" ], "file_count": 6, "download_size_bytes": 38213, "download_size_human": "37.3 KB", "product_url": "https://datanest-stores.com/saas-starter/#product-feature-flags", "preview_url": "https://datanest-stores.com/saas-starter/preview-feature-flags", "demo_url": "https://datanest-stores.com/saas-starter/demo-feature-flags/" }, { "product_id": "saas-starter:notification-system", "slug": "notification-system", "store": "saas-starter", "store_name": "SaaS Starter", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "admin dashboard", "billing", "dev", "multi-tenant", "saas", "subscription", "user management" ], "title": "Notification System", "short_description": "Multi-channel notification engine for email, webhook, in-app, and SMS with template system.", "detailed_description": "A Python notification engine for SaaS applications. Supports email, webhook, in-app, and SMS channels with a template system, scheduling, per-user preferences, delivery tracking, and retry logic \u2014 all built on Python's standard library. Includes: Multi-channel delivery \u2014 Email, webhook, in-app, and SMS (via pluggable adapters); Template engine \u2014 Reusable notification templates with variable interpolation; Scheduling \u2014 Send immediately or schedule for a future time; User preferences \u2014 Per-user channel opt-in/opt-out with global defaults; Delivery tracking \u2014 Track sent, delivered, failed, and pending status per notification; Retry logic \u2014 Automatic retries with exponential backoff for failed deliveries.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "python src/main.py --init-demo", "notification-system/\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 src/\n\u2502 \u2514\u2500\u2500 main.py # Notification engine, templates, channels, scheduling, HTTP API\n\u2514\u2500\u2500 examples/\n \u2514\u2500\u2500 basic_example.py # Send notifications, manage templates, check delivery demo" ], "included_files": [ "README.md", "examples/basic_example.py", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_project-structure.md", "src/main.py" ], "file_count": 6, "download_size_bytes": 44808, "download_size_human": "43.8 KB", "product_url": "https://datanest-stores.com/saas-starter/#product-notification-system", "preview_url": "https://datanest-stores.com/saas-starter/preview-notification-system", "demo_url": "https://datanest-stores.com/saas-starter/demo-notification-system/" }, { "product_id": "saas-starter:onboarding-flow", "slug": "onboarding-flow", "store": "saas-starter", "store_name": "SaaS Starter", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "admin dashboard", "billing", "dev", "multi-tenant", "saas", "subscription", "user management" ], "title": "Onboarding Flow", "short_description": "Onboarding wizard with progressive profiling, conditional setup steps, and completion tracking.", "detailed_description": "A Python onboarding wizard for SaaS applications. Guides new users through setup steps with progressive profiling, conditional step logic, completion tracking, and automated welcome/completion emails \u2014 all built on Python's standard library. Includes: Multi-step flows \u2014 Define ordered onboarding steps (form, choice, info, action types); Progressive profiling \u2014 Collect user data across multiple steps without overwhelming them; Conditional steps \u2014 Show/hide steps based on user attributes (role, plan, etc; Skip support \u2014 Optional steps can be skipped; required steps must be completed; Completion tracking \u2014 Percentage-based progress tracking per user; Welcome emails \u2014 HTML email templates with step overview and CTA buttons.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Start with demo flow\npython src/main.py --init-demo\n\n# Custom port\npython src/main.py --port 8002", "# List available flows\ncurl http://localhost:8002/api/flows\n\n# Start onboarding for a user\ncurl -X POST http://localhost:8002/api/flows//start \\\n -H \"Content-Type: application/json\" \\\n -d '{\"user_id\": \"user_123\", \"context\": {\"role\": \"admin\"}}'\n\n# Complete a step\ncurl -X POST http://localhost:8002/api/flows//complete-step \\\n -H \"Content-Type: application/json\" \\\n -d '{\"user_id\": \"user_123\", \"step_id\": \"profile\", \"data\": {\"company\": \"Acme Corp\"}}'\n\n# Check progress\n\u2026" ], "included_files": [ "README.md", "examples/basic_example.py", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_project-structure.md", "src/main.py" ], "file_count": 6, "download_size_bytes": 42610, "download_size_human": "41.6 KB", "product_url": "https://datanest-stores.com/saas-starter/#product-onboarding-flow", "preview_url": "https://datanest-stores.com/saas-starter/preview-onboarding-flow", "demo_url": "https://datanest-stores.com/saas-starter/demo-onboarding-flow/" }, { "product_id": "saas-starter:saas-boilerplate", "slug": "saas-boilerplate", "store": "saas-starter", "store_name": "SaaS Starter", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "admin dashboard", "billing", "dev", "multi-tenant", "saas", "subscription", "user management" ], "title": "SaaS Boilerplate", "short_description": "SaaS Boilerplate with user management, RBAC, settings, and multi-tenancy in pure Python.", "detailed_description": "A complete Python SaaS application boilerplate with user management, role-based access control, settings, and multi-tenancy scaffolding. Your starting point for any SaaS product. Includes: User management \u2014 Registration, authentication, password hashing (PBKDF2-HMAC-SHA256); Role-based access control \u2014 Admin, member, and viewer roles with permission checks; Multi-tenant scaffolding \u2014 Tenant context, per-tenant data isolation; Application settings \u2014 JSON-persisted settings with defaults and validation; Session management \u2014 Token-based sessions with expiration and rotation; Audit logging \u2014 Structured log of all user and system operations.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Start the server with demo data\npython src/main.py --init-demo\n\n# Start on a custom port\npython src/main.py --port 9000\n\n# Start clean (no demo data)\npython src/main.py", "{\n \"host\": \"0.0.0.0\",\n \"port\": 8000,\n \"secret_key\": \"YOUR_SECRET_KEY_HERE\",\n \"session_ttl_hours\": 24,\n \"max_users_per_tenant\": 50,\n \"data_dir\": \"./data\"\n}" ], "included_files": [ "README.md", "examples/basic_example.py", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_configuration.md", "guide/04_license.md", "src/main.py" ], "file_count": 7, "download_size_bytes": 42800, "download_size_human": "41.8 KB", "product_url": "https://datanest-stores.com/saas-starter/#product-saas-boilerplate", "preview_url": "https://datanest-stores.com/saas-starter/preview-saas-boilerplate", "demo_url": "https://datanest-stores.com/saas-starter/demo-saas-boilerplate/" }, { "product_id": "saas-starter:saas-starter-pro-bundle", "slug": "saas-starter-pro-bundle", "store": "saas-starter", "store_name": "SaaS Starter", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "admin dashboard", "billing", "bundle", "dev", "multi-tenant", "saas", "subscription", "user management" ], "title": "SaaS Starter \u2014 Pro Bundle", "short_description": "All 10 SaaS tools \u2014 $240 bought separately, yours for $59. Save $181 (75%): boilerplate, billing, dashboards, feature flags, and multi-tenancy.", "detailed_description": "All 10 SaaS tools \u2014 $240 bought separately, yours for $59. Save $181 (75%): boilerplate, billing, dashboards, feature flags, and multi-tenancy.", "price_usd": 59, "currency": "usd", "is_bundle": true, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md" ], "file_count": 1, "download_size_bytes": 282134, "download_size_human": "275.5 KB", "product_url": "https://datanest-stores.com/saas-starter/#product-saas-starter-pro-bundle", "preview_url": "https://datanest-stores.com/saas-starter/preview-saas-starter-pro-bundle", "demo_url": null }, { "product_id": "saas-starter:tenant-manager", "slug": "tenant-manager", "store": "saas-starter", "store_name": "SaaS Starter", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "admin dashboard", "billing", "dev", "multi-tenant", "saas", "subscription", "user management" ], "title": "Tenant Manager", "short_description": "Multi-tenant management system with thread-local isolation, per-tenant config, and provisioning.", "detailed_description": "A complete Python multi-tenant management system. Handles tenant isolation via thread-local context, per-tenant configuration, data partitioning, plan-based feature gating, member management, and admin cross-tenant operations \u2014 all built on Python's standard library. Includes: Tenant isolation \u2014 Thread-local context ensures requests never leak data between tenants; Context manager \u2014 with tenantscope(\"tenantid\"); Per-tenant config \u2014 Feature flags, user limits, storage limits, theming per tenant; Plan-based limits \u2014 Free/Starter/Pro/Enterprise tiers with auto-applied config; Data partitioning \u2014 All CRUD operations automatically scoped to current tenant; Member management \u2014 Add/remove users, role-based access (owner/admin/member/viewer).", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "python src/main.py --init-demo", "# List tenants\ncurl http://localhost:8004/api/tenants\n\n# Create a tenant\ncurl -X POST http://localhost:8004/api/tenants \\\n -H \"Content-Type: application/json\" \\\n -d '{\"name\": \"My Company\", \"slug\": \"my-company\", \"owner_id\": \"user_1\", \"plan\": \"pro\"}'\n\n# Add a member\ncurl -X POST http://localhost:8004/api/tenants//members \\\n -H \"Content-Type: application/json\" \\\n -d '{\"user_id\": \"user_2\", \"role\": \"admin\"}'\n\n# Platform stats\n\u2026" ], "included_files": [ "README.md", "examples/basic_example.py", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_project-structure.md", "src/main.py" ], "file_count": 6, "download_size_bytes": 38572, "download_size_human": "37.7 KB", "product_url": "https://datanest-stores.com/saas-starter/#product-tenant-manager", "preview_url": "https://datanest-stores.com/saas-starter/preview-tenant-manager", "demo_url": "https://datanest-stores.com/saas-starter/demo-tenant-manager/" }, { "product_id": "saas-starter:usage-metering", "slug": "usage-metering", "store": "saas-starter", "store_name": "SaaS Starter", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "admin dashboard", "billing", "dev", "multi-tenant", "saas", "subscription", "user management" ], "title": "Usage Metering", "short_description": "Usage tracking for SaaS with API calls, storage, compute minutes, and custom metrics.", "detailed_description": "A Python usage tracking system for SaaS applications. Tracks API calls, storage, compute minutes, and custom metrics with quota enforcement, billing period aggregation, idempotency protection, and billing integration hooks \u2014 all built on Python's standard library. Includes: Event recording \u2014 Track any metric (API calls, storage, compute, custom); Quota enforcement \u2014 Per-plan limits with configurable overage policies; Idempotency \u2014 Dedup protection prevents double-counting on retries; Usage summaries \u2014 Aggregate by tenant, metric, and billing period; Billing reports \u2014 Generate billing-ready reports with overage calculations; Top users \u2014 Identify heaviest users within a tenant.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "python src/main.py --init-demo", "# Record usage\ncurl -X POST http://localhost:8005/api/events \\\n -H \"Content-Type: application/json\" \\\n -d '{\"tenant_id\": \"tenant_acme\", \"metric\": \"api_calls\", \"quantity\": 1, \"user_id\": \"user_1\"}'\n\n# Check usage\ncurl http://localhost:8005/api/usage/tenant_acme/api_calls\n\n# Check quota\ncurl http://localhost:8005/api/quota/tenant_acme/api_calls\n\n# Generate billing report\ncurl -X POST http://localhost:8005/api/billing-report \\\n -H \"Content-Type: application/json\" \\\n\u2026" ], "included_files": [ "README.md", "examples/basic_example.py", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_project-structure.md", "src/main.py" ], "file_count": 6, "download_size_bytes": 37842, "download_size_human": "37.0 KB", "product_url": "https://datanest-stores.com/saas-starter/#product-usage-metering", "preview_url": "https://datanest-stores.com/saas-starter/preview-usage-metering", "demo_url": "https://datanest-stores.com/saas-starter/demo-usage-metering/" }, { "product_id": "security-engineer:cve-triage-playbook", "slug": "cve-triage-playbook", "store": "security-engineer", "store_name": "Security Engineer Pro", "domain": "security", "category": "Security & Compliance", "tags": [ "compliance", "cve", "dependencies", "gdpr", "incident response", "owasp", "penetration testing", "security", "soc 2", "triage", "vulnerability" ], "title": "CVE Triage Playbook", "short_description": "A decision framework + executable scorer to turn a wall of dependency-scanner CVE alerts into a ranked, defensible P0-P3 action list based on your deployment's real risk.", "detailed_description": "A decision framework for turning a wall of dependency-scanner alerts into a ranked, defensible action list. When a scanner (Dependabot, Trivy, Snyk, npm audit, pip-audit) reports 60 \"critical\" CVEs, most are noise for your app \u2014 this playbook tells you which ones actually matter, in what order, and why. Includes: TRIAGE-FRAMEWORK; SEVERITY-RUBRIC; examples/triage-log; examples/triage.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": "2026-08-02", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [ "None to read/apply. triage.py needs Python 3.8+" ], "supported_versions": [ "Python 3.8+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md", "SEVERITY-RUBRIC.md", "TRIAGE-FRAMEWORK.md", "examples/triage-log.md", "examples/triage.py", "guide/01-overview.md" ], "file_count": 6, "download_size_bytes": 7783, "download_size_human": "7.6 KB", "product_url": "https://datanest-stores.com/security-engineer/#product-cve-triage-playbook", "preview_url": "https://datanest-stores.com/security-engineer/preview-cve-triage-playbook", "demo_url": null }, { "product_id": "security-engineer:iam-least-privilege-library", "slug": "iam-least-privilege-library", "store": "security-engineer", "store_name": "Security Engineer Pro", "domain": "security", "category": "Security & Compliance", "tags": [ "aws", "azure", "compliance", "gcp", "gdpr", "iam", "incident response", "least-privilege", "owasp", "penetration testing", "policy", "security", "soc 2" ], "title": "IAM Least-Privilege Policy Library", "short_description": "Copy-pasteable least-privilege IAM policies for the roles teams actually create (CI deployer, S3 app, Lambda, auditor, secrets consumer) across AWS/GCP/Azure, plus a review checklist.", "detailed_description": "A starter library of tight, copy-pasteable IAM policies for the roles teams actually create \u2014 instead of the : policy someone pasted at 2am that never got fixed. Covers AWS (JSON policies) with GCP and Azure equivalents, plus a review checklist for tightening what you already have. Includes: policies/aws/ \u2014 least-privilege JSON policies for common roles; ci-deployer; s3-app-readwrite; lambda-execution; readonly-auditor; secrets-consumer.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": "2026-08-02", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [], "supported_versions": [], "supported_technologies": [ "AWS", "YAML" ], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "PRINCIPLES.md", "README.md", "REVIEW-CHECKLIST.md", "guide/01-overview.md", "policies/aws/ci-deployer.json", "policies/aws/lambda-execution.json", "policies/aws/readonly-auditor.json", "policies/aws/s3-app-readwrite.json", "policies/aws/secrets-consumer.json", "policies/azure/custom-roles.json", "policies/gcp/custom-roles.yaml" ], "file_count": 11, "download_size_bytes": 8737, "download_size_human": "8.5 KB", "product_url": "https://datanest-stores.com/security-engineer/#product-iam-least-privilege-library", "preview_url": "https://datanest-stores.com/security-engineer/preview-iam-least-privilege-library", "demo_url": null }, { "product_id": "security-engineer:secrets-rotation-runbook", "slug": "secrets-rotation-runbook", "store": "security-engineer", "store_name": "Security Engineer Pro", "domain": "security", "category": "Security & Compliance", "tags": [ "compliance", "credentials", "gdpr", "incident response", "owasp", "penetration testing", "rotation", "runbook", "secrets", "security", "soc 2", "vault" ], "title": "Secrets Rotation Runbook", "short_description": "Zero-downtime rotation runbooks for database passwords, cloud access keys, API keys, JWT signing keys, TLS certs, and OAuth client secrets using the two-secret overlap pattern.", "detailed_description": "Step-by-step runbooks for rotating the secrets that actually leak \u2014 database passwords, API keys, cloud access keys, JWT signing keys, TLS certs, and OAuth client secrets \u2014 without downtime. Each runbook uses the two-secret (dual-key) pattern so you never have a window where the old secret is dead but the new one isn't live yet.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": "2026-08-02", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [], "supported_versions": [], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md", "guide/01-overview.md", "runbooks/api-key.md", "runbooks/cloud-access-key.md", "runbooks/database-password.md", "runbooks/jwt-signing-key.md", "runbooks/oauth-client-secret.md", "runbooks/tls-certificate.md" ], "file_count": 8, "download_size_bytes": 6167, "download_size_human": "6.0 KB", "product_url": "https://datanest-stores.com/security-engineer/#product-secrets-rotation-runbook", "preview_url": "https://datanest-stores.com/security-engineer/preview-secrets-rotation-runbook", "demo_url": null }, { "product_id": "security-engineer:soc2-controls-implementation-map", "slug": "soc2-controls-implementation-map", "store": "security-engineer", "store_name": "Security Engineer Pro", "domain": "security", "category": "Security & Compliance", "tags": [ "audit", "compliance", "controls", "gdpr", "incident response", "owasp", "penetration testing", "reference", "security", "soc 2", "soc2" ], "title": "SOC 2 Control Implementation Map", "short_description": "Maps SOC 2 Trust Services Criteria (CC6/CC7/CC8/A1) to concrete engineering controls and the exact evidence auditors accept. The translation layer from abstract criteria to what to build.", "detailed_description": "The missing translation layer between SOC 2's abstract Trust Services Criteria and the concrete engineering controls an auditor will accept as evidence. When someone (or an agent) asks \"what do we actually build to satisfy CC6.1?\", this maps each common criterion to specific, implementable controls and the evidence that proves them. Not legal advice and not a substitute for your auditor. This is an engineering reference to accelerate readiness and reduce back-and-forth, based on the controls auditors commonly accept. Includes: controls/access-control; controls/change-management; controls/monitoring; controls/availability; EVIDENCE-INDEX.", "price_usd": 49, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": "2026-08-02", "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [], "supported_versions": [], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "EVIDENCE-INDEX.md", "README.md", "controls/access-control.md", "controls/availability.md", "controls/change-management.md", "controls/monitoring.md", "guide/01-overview.md" ], "file_count": 7, "download_size_bytes": 6126, "download_size_human": "6.0 KB", "product_url": "https://datanest-stores.com/security-engineer/#product-soc2-controls-implementation-map", "preview_url": "https://datanest-stores.com/security-engineer/preview-soc2-controls-implementation-map", "demo_url": null }, { "product_id": "security-engineer:owasp-security-checklist", "slug": "owasp-security-checklist", "store": "security-engineer", "store_name": "Security Engineer Pro", "domain": "security", "category": "Security & Compliance", "tags": [ "compliance", "gdpr", "incident response", "owasp", "penetration testing", "security", "soc 2" ], "title": "OWASP Security Checklist", "short_description": "Complete OWASP Top 10 implementation guide with code examples, testing scripts, and remediation patterns.", "detailed_description": "A complete, hands-on reference for the OWASP Top 10:2021. 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This file\n+-- LICENSE # MIT License\n+-- index.html # Interactive reader (free preview)\n+-- free-sample.zip # Free sample with Chapter 1\n+-- guide/\n +-- 01-*.md through 05-*.md" ], "included_files": [ "README.md", "guide/01-soc-automation-architecture.md", "guide/02-alert-ingestion-and-normalization.md", "guide/03-playbook-engine-and-automated-triage.md", "guide/04-enrichment,-case-management,-escalation.md", "guide/05-metrics,-reporting,-and-continuous-impro.md" ], "file_count": 6, "download_size_bytes": 45026, "download_size_human": "44.0 KB", "product_url": "https://datanest-stores.com/security-engineer/#product-soc-automation-framework", "preview_url": "https://datanest-stores.com/security-engineer/preview-soc-automation-framework", "demo_url": "https://datanest-stores.com/security-engineer/demo-soc-automation-framework/" }, { "product_id": "security-engineer:threat-intel-platform", "slug": "threat-intel-platform", "store": "security-engineer", "store_name": "Security Engineer Pro", "domain": "security", "category": "Security & Compliance", "tags": [ "compliance", "gdpr", "incident response", "owasp", "penetration testing", "security", "soc 2" ], "title": "Threat Intelligence Platform", "short_description": "Aggregate, analyze, and act on threat intelligence: feed ingestion, IOC normalization, scoring, and integration with detection tooling.", "detailed_description": "Aggregate, analyze, and act on threat intelligence: feed ingestion, IOC normalization, scoring, and integration with detection tooling.", "price_usd": 49, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "No specialized software required for reading the guides", "Code examples include setup instructions within each chapter", "Python 3.8+ recommended for running example scripts" ], "supported_versions": [ "Python 3.8+" ], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [ "threat-intel-platform/\n+-- README.md # 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This file\n+-- LICENSE # MIT License\n+-- index.html # Interactive reader (free preview)\n+-- free-sample.zip # Free sample with Chapter 1\n+-- guide/\n +-- 01-*.md through 05-*.md" ], "included_files": [ "README.md", "guide/01-vm-lifecycle-and-architecture.md", "guide/02-scan-integration-and-normalization.md", "guide/03-risk-based-prioritization.md", "guide/04-remediation-workflow-automation.md", "guide/05-verification,-reporting,-and-metrics.md" ], "file_count": 6, "download_size_bytes": 45180, "download_size_human": "44.1 KB", "product_url": "https://datanest-stores.com/security-engineer/#product-vulnerability-management-pipeline", "preview_url": "https://datanest-stores.com/security-engineer/preview-vulnerability-management-pipeline", "demo_url": "https://datanest-stores.com/security-engineer/demo-vulnerability-management-pipeline/" }, { "product_id": "security-engineer:zero-trust-implementation", "slug": "zero-trust-implementation", "store": "security-engineer", "store_name": "Security Engineer Pro", "domain": "security", "category": "Security & Compliance", "tags": [ "compliance", "gdpr", "incident response", "owasp", "penetration testing", "security", "soc 2" ], "title": "Zero Trust Implementation Guide", "short_description": "Implement zero trust with NIST 800-207 and the CISA maturity model: identity, device, network, and workload controls with a phased rollout plan.", "detailed_description": "Implement Zero Trust with NIST 800-207 and CISA Maturity Model Practical guide to implementing Zero Trust architecture following NIST standards. ---", "price_usd": 49, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "No specialized software required for reading the guides", "Code examples include setup instructions within each chapter", "Python 3.8+ recommended for running example scripts" ], "supported_versions": [ "Python 3.8+" ], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [ "zero-trust-implementation/\n+-- README.md # This file\n+-- LICENSE # MIT License\n+-- index.html # Interactive reader (free preview)\n+-- free-sample.zip # Free sample with Chapter 1\n+-- guide/\n +-- 01-*.md through 05-*.md" ], "included_files": [ "README.md", "guide/01-zero-trust-principles-and-foundations.md", "guide/02-identity-and-device-trust-pillars.md", "guide/03-network-and-environment-segmentation.md", "guide/04-application-and-workload-protection.md", "guide/05-data-security-and-continuous-monitoring.md" ], "file_count": 6, "download_size_bytes": 45197, "download_size_human": "44.1 KB", "product_url": "https://datanest-stores.com/security-engineer/#product-zero-trust-implementation", "preview_url": "https://datanest-stores.com/security-engineer/preview-zero-trust-implementation", "demo_url": "https://datanest-stores.com/security-engineer/demo-zero-trust-implementation/" }, { "product_id": "security-engineer:incident-response-playbook", "slug": "incident-response-playbook", "store": "security-engineer", "store_name": "Security Engineer Pro", "domain": "security", "category": "Security & Compliance", "tags": [ "compliance", "gdpr", "incident response", "owasp", "penetration testing", "security", "soc 2" ], "title": "Incident Response Playbook", "short_description": "A ready-to-run IR kit for small and mid-size teams: a clear operating model, five attack-specific 3-a.m. runbooks, severity classification, on-call escalation, tabletop exercises, and printable communication templates. Built to be usable under pressure.", "detailed_description": "A ready-to-run incident response kit for small and mid-size teams: a clear operating model, five attack-specific runbooks you can actually follow at 3 a.m., and the templates that turn a stressful breach into a consistent, defensible response. Built to be usable under pressure \u2014 short, specific, and printable \u2014 not a 90-page binder nobody opens. The goal of incident response isn't to never have incidents. It's to respond consistently and fast when you do, limit the harm, and learn so it doesn't recur. A mediocre plan you actually run beats a perfect plan nobody knows. ---", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [], "supported_versions": [], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md", "docs/incident-response-overview.md", "docs/on-call-escalation.md", "docs/severity-classification.md", "docs/tabletop-exercise-guide.md", "examples/worked-incident-timeline.md", "guide/01-ir-operating-model.md", "guide/02-severity-and-escalation.md", "guide/03-playbook-walkthroughs.md", "guide/04-communication-and-evidence.md", "guide/05-tabletop-and-pir.md", "playbooks/account-compromise.md", "playbooks/data-breach.md", "playbooks/ddos.md", "playbooks/phishing-bec.md", "playbooks/ransomware.md", "templates/communication-templates.md", "templates/escalation-roster.csv", "templates/forensic-evidence-checklist.md", "templates/incident-log.csv", "templates/post-incident-review.md" ], "file_count": 21, "download_size_bytes": 113354, "download_size_human": "110.7 KB", "product_url": "https://datanest-stores.com/security-engineer/#product-incident-response-playbook", "preview_url": "https://datanest-stores.com/security-engineer/preview-incident-response-playbook", "demo_url": "https://datanest-stores.com/security-engineer/demo-incident-response-playbook/" }, { "product_id": "security-engineer:security-complete-bundle", "slug": "security-complete-bundle", "store": "security-engineer", "store_name": "Security Engineer Pro", "domain": "security", "category": "Security & Compliance", "tags": [ "bundle", "compliance", "gdpr", "incident response", "owasp", "penetration testing", "security", "soc 2" ], "title": "Security Engineer Bundle", "short_description": "All 16 security tools \u2014 $664 bought separately, yours for $119. Save $545 (82%): audit scripts, vulnerability scanning, and compliance templates.", "detailed_description": "A complete, hands-on reference for the OWASP Top 10:2021. Every category gets a plain-English explanation, paired vulnerable-vs-fixed code in multiple languages, concrete test procedures, a remediation checklist, and links to authoritative secure-coding references. Ten of the example pairs are runnable, and a test suite proves the fixes actually hold. Built for engineers who have to ship secure code and prove it \u2014 not just read about it. 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"products/api-security-framework/source/src/node/jwt_verify.js", "products/api-security-framework/source/src/node/rate_limiter.js", "products/api-security-framework/source/src/node/security_headers.js", "products/api-security-framework/source/src/python/input_validation.py", "products/api-security-framework/source/src/python/jwt_auth.py", "products/api-security-framework/source/src/python/rate_limiter.py", "products/api-security-framework/source/src/python/secrets_loader.py", "products/api-security-framework/source/src/python/security_headers.py", "products/gdpr-privacy-toolkit/README.md", "products/gdpr-privacy-toolkit/source/README.md", "products/gdpr-privacy-toolkit/source/docs/breach-notification-guide.md", "products/gdpr-privacy-toolkit/source/docs/data-retention-guide.md", "products/gdpr-privacy-toolkit/source/docs/dpia-framework.md", "products/gdpr-privacy-toolkit/source/docs/dsar-procedure.md", "products/gdpr-privacy-toolkit/source/docs/gdpr-overview.md", 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"products/network-security-toolkit/source/README.md", "products/network-security-toolkit/source/docs/network-hardening-guide.md", "products/network-security-toolkit/source/examples/suricata-alert-sample.txt", "products/network-security-toolkit/source/firewall/iptables-rules.sh", "products/network-security-toolkit/source/firewall/nftables.conf", "products/network-security-toolkit/source/firewall/segmentation-zones.md", "products/network-security-toolkit/source/guide/01-network-security-fundamentals.md" ], "file_count": 80, "download_size_bytes": 1027227, "download_size_human": "1003.2 KB", "product_url": "https://datanest-stores.com/security-engineer/#product-security-complete-bundle", "preview_url": null, "demo_url": null }, { "product_id": "security-kit:audit-script-kit", "slug": "audit-script-kit", "store": "security-kit", "store_name": "Security Kit", "domain": "security", "category": "Security & Compliance", "tags": [ "cors", "csp", "penetration testing", "security", "security audit", "sql injection", "vulnerability scanner", "xss" ], "title": "Audit Script Kit", "short_description": "Security audit scripts for Linux: file permissions, port enumeration, and process monitoring.", "detailed_description": "Collection of security audit scripts for Linux servers: file permission checks, open port enumeration, process monitoring, cron job auditing, and user account review. Run on any server in seconds. Includes: File permission auditing \u2014 detects world-readable, world-writable, and SUID/SGID files; Open port enumeration \u2014 lists all listening ports with associated processes; Process monitoring \u2014 flags suspicious or unexpected running processes; Cron job auditing \u2014 reviews cron entries across all users for anomalies; User account review \u2014 privilege escalation detection, inactive accounts, passwordless users; Sensitive path detection \u2014 checks permissions on /etc/shadow, SSH keys, config files.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies", "Linux/macOS" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Run all audit checks\npython src/audit_script_kit.py --all\n\n# Check file permissions on a specific path\npython src/audit_script_kit.py --check permissions --path /etc\n\n# Enumerate open ports\npython src/audit_script_kit.py --check ports\n\n# Check processes and save results\npython src/audit_script_kit.py --check processes --output audit.json", "{\n \"audit_modules\": {\n \"file_permissions\": {\n \"enabled\": true,\n \"paths\": [\"/etc\", \"/var/log\", \"/home\"]\n },\n \"open_ports\": { \"enabled\": true },\n \"processes\": { \"enabled\": true },\n \"cron_jobs\": { \"enabled\": true },\n \"user_accounts\": { \"enabled\": true }\n }\n}" ], "included_files": [ "README.md", "examples/audit_config.json", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_configuration-reference.md", "guide/04_license.md", "src/audit_script_kit.py" ], "file_count": 7, "download_size_bytes": 38917, "download_size_human": "38.0 KB", "product_url": "https://datanest-stores.com/security-kit/#product-audit-script-kit", "preview_url": "https://datanest-stores.com/security-kit/preview-audit-script-kit", "demo_url": "https://datanest-stores.com/security-kit/demo-audit-script-kit/" }, { "product_id": "security-kit:compliance-checker", "slug": "compliance-checker", "store": "security-kit", "store_name": "Security Kit", "domain": "security", "category": "Security & Compliance", "tags": [ "cors", "csp", "penetration testing", "security", "security audit", "sql injection", "vulnerability scanner", "xss" ], "title": "Compliance Checker", "short_description": "Validate apps against OWASP Top 10, CIS benchmarks, and security header best practices.", "detailed_description": "Validate web applications against OWASP Top 10, CIS benchmark guidelines, and security header best practices. Get a compliance score and remediation steps in minutes. Includes: OWASP Top 10 (2021) \u2014 automated checks for the most critical web security risks; CIS benchmark guidelines \u2014 server configuration hardening checks; Security header validation \u2014 HSTS, CSP, X-Frame-Options, X-Content-Type-Options, Referrer-Policy, Permissions-Policy; Compliance scoring \u2014 letter-grade rating (A through F) based on pass/fail ratio; Remediation recommendations \u2014 actionable fix for each failed check; Multiple frameworks \u2014 run OWASP, CIS, or both with --all.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Quick compliance check with all frameworks\npython src/compliance_checker.py --target api.docs.example.com\n\n# OWASP Top 10 only\npython src/compliance_checker.py --target docs.example.com --framework owasp\n\n# Full check with JSON output\npython src/compliance_checker.py --target docs.example.com --all --output report.json", "{\n \"target\": \"https://app.docs.example.com\",\n \"checks\": {\n \"owasp_top_10\": true,\n \"cis_benchmarks\": true,\n \"security_headers\": true\n },\n \"fail_threshold\": 70,\n \"output_format\": \"json\"\n}" ], "included_files": [ "README.md", "examples/compliance_config.json", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_configuration-reference.md", "guide/04_license.md", "src/compliance_checker.py" ], "file_count": 7, "download_size_bytes": 39094, "download_size_human": "38.2 KB", "product_url": "https://datanest-stores.com/security-kit/#product-compliance-checker", "preview_url": "https://datanest-stores.com/security-kit/preview-compliance-checker", "demo_url": "https://datanest-stores.com/security-kit/demo-compliance-checker/" }, { "product_id": "security-kit:csp-builder", "slug": "csp-builder", "store": "security-kit", "store_name": "Security Kit", "domain": "security", "category": "Security & Compliance", "tags": [ "cors", "csp", "penetration testing", "security", "security audit", "sql injection", "vulnerability scanner", "xss" ], "title": "CSP Builder", "short_description": "Build Content Security Policy headers from presets, test against live URLs, and generate violation reports.", "detailed_description": "Build Content Security Policy headers from presets or config files. Test policies against live URLs and generate violation report configurations. Stop XSS before it starts. Includes: Directive generator \u2014 covers all standard CSP directives (default-src, script-src, style-src, etc; Preset policies \u2014 strict, moderate, and permissive presets for quick starts; Policy tester \u2014 validate a URL's existing CSP headers and identify gaps; Violation reporting \u2014 configure report-uri and report-to directives; Nonce generation \u2014 create cryptographic nonces for inline scripts and styles; Multiple output formats \u2014 HTTP header string or HTML tag.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Generate a strict CSP header\npython src/csp_builder.py --preset strict\n\n# Build from a config file\npython src/csp_builder.py --config examples/csp_config.json --output csp_header.txt\n\n# Test a live URL's CSP\npython src/csp_builder.py --test --url https://docs.example.com", "{\n \"directives\": {\n \"default-src\": [\"'none'\"],\n \"script-src\": [\"'self'\", \"https://cdn.docs.example.com\"],\n \"style-src\": [\"'self'\", \"'unsafe-inline'\"],\n \"img-src\": [\"'self'\", \"data:\"],\n \"connect-src\": [\"'self'\", \"https://api.docs.example.com\"],\n \"font-src\": [\"'self'\"],\n \"frame-ancestors\": [\"'none'\"]\n },\n \"report_uri\": \"https://report.docs.example.com/csp\"\n}" ], "included_files": [ "README.md", "examples/csp_config.json", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_configuration-reference.md", "guide/04_license.md", "src/csp_builder.py" ], "file_count": 7, "download_size_bytes": 36834, "download_size_human": "36.0 KB", "product_url": "https://datanest-stores.com/security-kit/#product-csp-builder", "preview_url": "https://datanest-stores.com/security-kit/preview-csp-builder", "demo_url": "https://datanest-stores.com/security-kit/demo-csp-builder/" }, { "product_id": "security-kit:dependency-auditor", "slug": "dependency-auditor", "store": "security-kit", "store_name": "Security Kit", "domain": "security", "category": "Security & Compliance", "tags": [ "cors", "csp", "penetration testing", "security", "security audit", "sql injection", "vulnerability scanner", "xss" ], "title": "Dependency Auditor", "short_description": "Offline dependency audit engine for Python requirements: version checks, risk policies, severity ranking. Ships sample CVE data; plug in an OSV/NVD feed for live scanning.", "detailed_description": "Audit Python requirements files for outdated versions, risky packages and policy violations \u2014 offline, stdlib-only, CI-friendly. Read this first \u2014 the bundled CVE data is a SAMPLE, not a live feed. The included CVEDATABASE uses deliberately non-real placeholder CVE ids (CVE-2024-XXXXX) so the auditor runs out of the box with zero setup and zero network. It will not tell you whether your dependencies are actually vulnerable. What you get here is the auditing engine \u2014 parser, version matching, severity ranking, policy gating, reporting \u2014 into which you plug a real feed with --cve-db (export from OSV, NVD or pip-audit). Includes: Pluggable CVE source \u2014 ships with an offline sample dataset; point --cve-db at an OSV/NVD export for real scanning; Requirements parsing \u2014 reads requirements; Version comparison \u2014 semantic version matching with range-aware CVE lookups; Severity ratings \u2014 findings ranked critical / high / medium / low; Upgrade recommendations \u2014 suggests safe target versions for vulnerable packages; Strict mode \u2014 exit code 1 on any vulnerability found (CI/CD friendly).", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Audit your requirements file\npython src/dependency_auditor.py --file requirements.txt\n\n# Strict mode \u2014 fail on any vulnerability\npython src/dependency_auditor.py --file requirements.txt --strict\n\n# Audit a Pipfile with JSON output\npython src/dependency_auditor.py --file Pipfile --output report.json", "django==4.2.5\nflask==2.3.2\nrequests==2.28.0\npyyaml==5.4" ], "included_files": [ "README.md", "examples/sample_requirements.txt", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_sample-input.md", "guide/04_faq.md", "src/dependency_auditor.py" ], "file_count": 7, "download_size_bytes": 38126, "download_size_human": "37.2 KB", "product_url": "https://datanest-stores.com/security-kit/#product-dependency-auditor", "preview_url": "https://datanest-stores.com/security-kit/preview-dependency-auditor", "demo_url": "https://datanest-stores.com/security-kit/demo-dependency-auditor/" }, { "product_id": "security-kit:encryption-toolkit", "slug": "encryption-toolkit", "store": "security-kit", "store_name": "Security Kit", "domain": "security", "category": "Security & Compliance", "tags": [ "cors", "csp", "penetration testing", "security", "security audit", "sql injection", "vulnerability scanner", "xss" ], "title": "Encryption Toolkit", "short_description": "AES-CBC encryption, RSA key generation, secure hashing, and HMAC using only Python stdlib.", "detailed_description": "A comprehensive encryption library using only Python stdlib: AES-CBC encryption, RSA key generation, secure hashing, HMAC, file encryption, and a simple password vault. Includes: AES-CBC encryption \u2014 file and string encryption with PBKDF2 key derivation; RSA key generation \u2014 2048 and 4096-bit key pair generation; Secure hashing \u2014 SHA-256, SHA-512, and SHA-3 digests; HMAC authentication \u2014 message authentication codes for data integrity; Password vault \u2014 encrypted key-value store protected by a master password; File encryption \u2014 encrypt/decrypt any file with integrity verification.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md", "examples/sample_secret.txt", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_configuration-reference.md", "guide/04_license.md", "src/encryption_toolkit.py" ], "file_count": 7, "download_size_bytes": 39486, "download_size_human": "38.6 KB", "product_url": "https://datanest-stores.com/security-kit/#product-encryption-toolkit", "preview_url": "https://datanest-stores.com/security-kit/preview-encryption-toolkit", "demo_url": "https://datanest-stores.com/security-kit/demo-encryption-toolkit/" }, { "product_id": "security-kit:incident-response-kit", "slug": "incident-response-kit", "store": "security-kit", "store_name": "Security Kit", "domain": "security", "category": "Security & Compliance", "tags": [ "cors", "csp", "penetration testing", "security", "security audit", "sql injection", "vulnerability scanner", "xss" ], "title": "Incident Response Kit", "short_description": "Incident response toolkit with playbook generator, communication templates, and timeline builder.", "detailed_description": "A structured incident response toolkit: playbook generator, communication templates, timeline builder, and severity classifier. Be ready when things go wrong. Includes: Playbook generator \u2014 step-by-step response plans for data breaches, DDoS, ransomware, phishing, and more; Communication templates \u2014 pre-written drafts for stakeholders, customers, and technical teams; Timeline builder \u2014 build chronological incident timelines from event JSON; Severity classifier \u2014 classify incidents as P1-P4 with SLA targets and escalation rules; Role assignments \u2014 suggested team roles for each incident type (IC, comms lead, technical lead); Response workflows \u2014 checklists for containment, eradication, recovery, and lessons learned.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Generate a data breach response playbook\npython src/incident_response_kit.py --mode playbook --type data-breach\n\n# Build a timeline from events\npython src/incident_response_kit.py --mode timeline --events examples/sample_events.json\n\n# Generate a stakeholder communication template\npython src/incident_response_kit.py --mode template --type stakeholder\n\n# Classify an incident by description\npython src/incident_response_kit.py --mode classify --description \"Database accessible without auth\"", "{\n \"incident_id\": \"INC-2026-042\",\n \"incident_type\": \"data-breach\",\n \"severity\": \"P1\",\n \"events\": [\n {\n \"timestamp\": \"2026-03-14T08:00:00Z\",\n \"description\": \"Anomalous database query volume detected\",\n \"actor\": \"monitoring-system\"\n },\n {\n \"timestamp\": \"2026-03-14T08:15:00Z\",\n \"description\": \"SOC analyst confirms unauthorized access\",\n \"actor\": \"security-team\"\n\u2026" ], "included_files": [ "README.md", "examples/sample_events.json", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_configuration-reference.md", "guide/04_license.md", "src/incident_response_kit.py" ], "file_count": 7, "download_size_bytes": 44181, "download_size_human": "43.1 KB", "product_url": "https://datanest-stores.com/security-kit/#product-incident-response-kit", "preview_url": "https://datanest-stores.com/security-kit/preview-incident-response-kit", "demo_url": "https://datanest-stores.com/security-kit/demo-incident-response-kit/" }, { "product_id": "security-kit:pentest-helper", "slug": "pentest-helper", "store": "security-kit", "store_name": "Security Kit", "domain": "security", "category": "Security & Compliance", "tags": [ "cors", "csp", "penetration testing", "security", "security audit", "sql injection", "vulnerability scanner", "xss" ], "title": "Pentest Helper", "short_description": "Penetration testing toolkit for testing your own apps with request fuzzing and injection detection.", "detailed_description": "A lightweight penetration testing toolkit for testing YOUR OWN applications. Request fuzzing, SQL injection detection, and XSS scanning in a single Python script. Disclaimer: Only use this tool against systems you own or have explicit written authorization to test. Unauthorized penetration testing is illegal in most jurisdictions. Includes: Request fuzzer \u2014 sends common payload patterns to discover unexpected behavior; SQL injection tester \u2014 error-based and blind injection pattern detection; XSS scanner \u2014 reflected XSS detection with encoding bypass checks; Multiple test modes \u2014 run fuzz, sqli, xss, or all in one pass; Configurable endpoints \u2014 test multiple URLs and HTTP methods from a config file; Rate limiting \u2014 built-in delays to avoid overwhelming targets.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Fuzz an endpoint\npython src/pentest_helper.py --target https://app.docs.example.com --mode fuzz\n\n# Test for SQL injection\npython src/pentest_helper.py --target https://docs.example.com --mode sqli\n\n# Test for XSS\npython src/pentest_helper.py --target https://docs.example.com --mode xss\n\n# Run all tests with JSON output\npython src/pentest_helper.py --target https://docs.example.com --mode all --output report.json", "{\n \"target\": \"https://app.docs.example.com\",\n \"modes\": [\"sqli\", \"xss\", \"fuzz\"],\n \"endpoints\": [\n {\"url\": \"https://app.docs.example.com/search?q=test\", \"method\": \"GET\"},\n {\"url\": \"https://app.docs.example.com/login\", \"method\": \"POST\"}\n ],\n \"rate_limit_ms\": 500,\n \"timeout_seconds\": 10\n}" ], "included_files": [ "README.md", "examples/pentest_config.json", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_configuration-reference.md", "guide/04_license.md", "src/pentest_helper.py" ], "file_count": 7, "download_size_bytes": 41202, "download_size_human": "40.2 KB", "product_url": "https://datanest-stores.com/security-kit/#product-pentest-helper", "preview_url": "https://datanest-stores.com/security-kit/preview-pentest-helper", "demo_url": "https://datanest-stores.com/security-kit/demo-pentest-helper/" }, { "product_id": "security-kit:secret-rotator", "slug": "secret-rotator", "store": "security-kit", "store_name": "Security Kit", "domain": "security", "category": "Security & Compliance", "tags": [ "cors", "csp", "penetration testing", "security", "security audit", "sql injection", "vulnerability scanner", "xss" ], "title": "Secret Rotator", "short_description": "Manage secret rotation for API keys, passwords, and tokens with configurable policies.", "detailed_description": "Manage secret rotation for API keys, passwords, and tokens with configurable policies, provider hooks, and automatic rollback on failure. Never let credentials go stale. Includes: Automated rotation \u2014 rotate secrets based on configurable max-age policies (default; Provider hooks \u2014 built-in support for Stripe, generic API keys, and password generation; Rollback on failure \u2014 automatic backup and restore if rotation fails; Age checking \u2014 scan all secrets and flag stale credentials with --check-age; Secure backups \u2014 HMAC-verified backup before every rotation; Cryptographic generation \u2014 secrets generated via Python's secrets module (CSPRNG).", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Rotate all secrets that exceed their max age\npython src/secret_rotator.py --config secrets.json --rotate\n\n# Check which secrets are stale\npython src/secret_rotator.py --config secrets.json --check-age\n\n# Roll back a specific secret to its previous value\npython src/secret_rotator.py --config secrets.json --rollback api-key-prod", "{\n \"secrets\": [\n {\n \"name\": \"STRIPE_API_KEY\",\n \"provider\": \"stripe\",\n \"max_age_days\": 90,\n \"current_value\": \"sk-EXAMPLE...\",\n \"last_rotated\": \"2026-01-15T00:00:00Z\"\n },\n {\n \"name\": \"DB_PASSWORD\",\n \"provider\": \"password\",\n \"max_age_days\": 60,\n \"length\": 32,\n\u2026" ], "included_files": [ "README.md", "examples/rotation_config.json", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_configuration-reference.md", "guide/04_license.md", "src/secret_rotator.py" ], "file_count": 7, "download_size_bytes": 37906, "download_size_human": "37.0 KB", "product_url": "https://datanest-stores.com/security-kit/#product-secret-rotator", "preview_url": "https://datanest-stores.com/security-kit/preview-secret-rotator", "demo_url": "https://datanest-stores.com/security-kit/demo-secret-rotator/" }, { "product_id": "security-kit:security-kit-pro-bundle", "slug": "security-kit-pro-bundle", "store": "security-kit", "store_name": "Security Kit", "domain": "security", "category": "Security & Compliance", "tags": [ "bundle", "cors", "csp", "penetration testing", "security", "security audit", "sql injection", "vulnerability scanner", "xss" ], "title": "Security Kit Pro Bundle", "short_description": "All 10 security tools \u2014 $230 bought separately, yours for $59. Save $171 (74%): vulnerability scanning, encryption, compliance, and pentesting.", "detailed_description": "All 10 security tools \u2014 $230 bought separately, yours for $59. Save $171 (74%): vulnerability scanning, encryption, compliance, and pentesting.", "price_usd": 59, "currency": "usd", "is_bundle": true, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [ "security-kit-pro-bundle/\n\u251c\u2500\u2500 vulnerability-scanner/\n\u2502 \u251c\u2500\u2500 README.md\n\u2502 \u251c\u2500\u2500 src/vulnerability_scanner.py\n\u2502 \u251c\u2500\u2500 examples/scan_config.json\n\u2502 \u2514\u2500\u2500 LICENSE\n\u251c\u2500\u2500 audit-script-kit/\n\u2502 \u251c\u2500\u2500 README.md\n\u2502 \u251c\u2500\u2500 src/audit_script_kit.py\n\u2502 \u251c\u2500\u2500 examples/audit_config.json\n\u2502 \u2514\u2500\u2500 LICENSE\n\u251c\u2500\u2500 compliance-checker/\n\u2502 \u251c\u2500\u2500 README.md\n\u2502 \u251c\u2500\u2500 src/compliance_checker.py\n\u2026" ], "included_files": [ "README.md" ], "file_count": 1, "download_size_bytes": 266732, "download_size_human": "260.5 KB", "product_url": "https://datanest-stores.com/security-kit/#product-security-kit-pro-bundle", "preview_url": "https://datanest-stores.com/security-kit/preview-security-kit-pro-bundle", "demo_url": null }, { "product_id": "security-kit:security-report-generator", "slug": "security-report-generator", "store": "security-kit", "store_name": "Security Kit", "domain": "security", "category": "Security & Compliance", "tags": [ "cors", "csp", "penetration testing", "security", "security audit", "sql injection", "vulnerability scanner", "xss" ], "title": "Security Report Generator", "short_description": "Convert scan results into professional HTML reports with severity ratings and executive summaries.", "detailed_description": "Convert security scan results (JSON) into professional HTML reports with severity ratings, executive summaries, finding details, and remediation steps. Self-contained HTML that works offline. Includes: Self-contained HTML \u2014 single-file output with embedded CSS, works in any browser offline; Executive summary \u2014 overall risk score with finding count by severity; Severity ratings \u2014 findings categorized as critical, high, medium, low, or informational; Remediation steps \u2014 actionable fix for each finding; Demo mode \u2014 generate a sample report instantly to see the output format; Configurable branding \u2014 custom title, subtitle, and accent color.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Generate a report from scan results\npython src/security_report_generator.py --input scan_results.json\n\n# Custom title and output path\npython src/security_report_generator.py --input scan_results.json --title \"Q1 Audit\" --output report.html\n\n# Summary-only report\npython src/security_report_generator.py --input scan_results.json --format summary\n\n# Generate a demo report\npython src/security_report_generator.py --demo", "{\n \"target\": \"api.docs.example.com\",\n \"scan_time\": \"2026-03-14T10:30:00Z\",\n \"scanner\": \"SecurityKit Vulnerability Scanner v1.0\",\n \"open_ports\": [22, 80, 443, 8080],\n \"findings\": [\n {\n \"severity\": \"high\",\n \"category\": \"SSL/TLS\",\n \"title\": \"Expired SSL certificate\",\n \"description\": \"The SSL certificate expired 14 days ago.\",\n \"remediation\": \"Renew the SSL certificate and configure auto-renewal.\"\n }\n ]\n\u2026" ], "included_files": [ "README.md", "examples/sample_scan_results.json", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_input-format.md", "guide/04_faq.md", "src/security_report_generator.py" ], "file_count": 7, "download_size_bytes": 40731, "download_size_human": "39.8 KB", "product_url": "https://datanest-stores.com/security-kit/#product-security-report-generator", "preview_url": "https://datanest-stores.com/security-kit/preview-security-report-generator", "demo_url": "https://datanest-stores.com/security-kit/demo-security-report-generator/" }, { "product_id": "security-kit:vulnerability-scanner", "slug": "vulnerability-scanner", "store": "security-kit", "store_name": "Security Kit", "domain": "security", "category": "Security & Compliance", "tags": [ "cors", "csp", "penetration testing", "security", "security audit", "sql injection", "vulnerability scanner", "xss" ], "title": "Vulnerability Scanner", "short_description": "Scan hosts for open ports, insecure HTTP headers, SSL issues, and known CVEs.", "detailed_description": "Scan any host for open ports, insecure HTTP headers, SSL/TLS certificate issues, and known CVE patterns. Generates structured reports in JSON or console output. Includes: TCP port scanning \u2014 configurable port ranges with timeout control; SSL/TLS validation \u2014 certificate expiry, chain verification, hostname matching; HTTP security headers \u2014 audits HSTS, X-Frame-Options, CSP, X-Content-Type-Options, and more; CVE pattern detection \u2014 flags common server misconfigurations and known vulnerability signatures; Multiple output formats \u2014 JSON for automation, console for quick checks; Full scan mode \u2014 runs all checks in a single pass with --full.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "No external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Quick scan with default ports\npython src/vulnerability_scanner.py --target api.docs.example.com\n\n# Scan specific ports\npython src/vulnerability_scanner.py --target 192.168.1.1 --ports 80,443,8080\n\n# Full scan with JSON output\npython src/vulnerability_scanner.py --target docs.example.com --full --output report.json", "{\n \"target\": \"api.docs.example.com\",\n \"ports\": [22, 80, 443, 8080, 8443],\n \"full_scan\": true,\n \"timeout_seconds\": 5,\n \"output_format\": \"json\"\n}" ], "included_files": [ "README.md", "examples/scan_config.json", "guide/01_features.md", "guide/02_quick-start.md", "guide/03_configuration-reference.md", "guide/04_license.md", "src/vulnerability_scanner.py" ], "file_count": 7, "download_size_bytes": 41376, "download_size_human": "40.4 KB", "product_url": "https://datanest-stores.com/security-kit/#product-vulnerability-scanner", "preview_url": "https://datanest-stores.com/security-kit/preview-vulnerability-scanner", "demo_url": "https://datanest-stores.com/security-kit/demo-vulnerability-scanner/" }, { "product_id": "seo-toolkit:backlink-checker", "slug": "backlink-checker", "store": "seo-toolkit", "store_name": "SEO Toolkit", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "backlinks", "dev", "keywords", "meta tags", "page speed", "seo", "serp", "sitemap", "structured data" ], "title": "Backlink Checker", "short_description": "Backlink analysis tool with domain authority, anchor text distribution, and link quality scoring.", "detailed_description": "SEO Toolkit by CodeVault Check backlink status codes, detect broken links, analyze anchor text distribution, and export comprehensive link health reports \u2014 all from the command line with zero dependencies. Includes: Link Health Check; Redirect Chain Detection; Anchor Text Analysis; Link Type Breakdown; Domain Diversity; Health Grading.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Copy the script to your project\ncp src/backlink_checker.py /your/project/\n\n# Or run it directly from this directory\npython src/backlink_checker.py --help", "source_url,target_url,anchor_text,link_type\nhttps://blog.docs.example.com/article,https://yoursite.com/page,click here,dofollow" ], "included_files": [ "README.md", "examples/backlinks.csv", "examples/backlinks.json", "guide/01_features.md", "guide/02_input-format.md", "guide/03_where-to-get-your-backlink-data.md", "src/backlink_checker.py" ], "file_count": 7, "download_size_bytes": 41309, "download_size_human": "40.3 KB", "product_url": "https://datanest-stores.com/seo-toolkit/#product-backlink-checker", "preview_url": "https://datanest-stores.com/seo-toolkit/preview-backlink-checker", "demo_url": "https://datanest-stores.com/seo-toolkit/demo-backlink-checker/" }, { "product_id": "seo-toolkit:canonical-url-planner", "slug": "canonical-url-planner", "store": "seo-toolkit", "store_name": "SEO Toolkit", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "backlinks", "dev", "keywords", "meta tags", "page speed", "seo", "serp", "sitemap", "structured data" ], "title": "Canonical URL Planner", "short_description": "Canonical URL planning and audit tool for preventing duplicate content issues.", "detailed_description": "Part of the SEO Toolkit by CodeVault Plan and validate canonical URLs across your entire site. Detect chains, loops, duplicate content clusters, missing canonicals, protocol mismatches, and cross-domain references \u2014 all from a single CSV or JSON audit. Includes: Load page/canonical pairs from CSV or JSON; Detect canonical chains (A \u2192 B \u2192 C) and recommend direct canonicals; Detect canonical loops (A \u2192 B \u2192 A); Find missing canonical tags; Flag protocol mismatches (HTTP page with HTTPS canonical); Identify cross-domain canonicals.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Validate canonical URLs from CSV\npython src/canonical_url_planner.py --input examples/pages.csv\n\n# Validate from JSON\npython src/canonical_url_planner.py --input examples/pages.json\n\n# JSON output for CI/CD\npython src/canonical_url_planner.py --input examples/pages.csv --format json\n\n# Strict mode\npython src/canonical_url_planner.py --input examples/pages.csv --strict\n\n# Write report to file\npython src/canonical_url_planner.py --input examples/pages.csv --output report.txt", "page_url,canonical_url\nhttps://www.docs.example.com/,https://www.docs.example.com/\nhttps://www.docs.example.com/old-page,https://www.docs.example.com/new-page\nhttps://www.docs.example.com/no-canonical," ], "included_files": [ "README.md", "examples/pages.csv", "examples/pages.json", "guide/01_features.md", "guide/02_input-formats.md", "guide/03_license.md", "src/canonical_url_planner.py" ], "file_count": 7, "download_size_bytes": 35898, "download_size_human": "35.1 KB", "product_url": "https://datanest-stores.com/seo-toolkit/#product-canonical-url-planner", "preview_url": "https://datanest-stores.com/seo-toolkit/preview-canonical-url-planner", "demo_url": "https://datanest-stores.com/seo-toolkit/demo-canonical-url-planner/" }, { "product_id": "seo-toolkit:keyword-density-checker", "slug": "keyword-density-checker", "store": "seo-toolkit", "store_name": "SEO Toolkit", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "backlinks", "dev", "keywords", "meta tags", "page speed", "seo", "serp", "sitemap", "structured data" ], "title": "Keyword Density Checker", "short_description": "Analyze keyword density, frequency, and distribution across web page content.", "detailed_description": "Part of the SEO Toolkit by CodeVault Analyze text or HTML content for keyword density, n-gram frequency, readability scores, and content optimization signals. Includes: Keyword density calculation with status labels (optimal, low, high, stuffed); N-gram analysis; Flesch-Kincaid readability scoring (Reading Ease + Grade Level); HTML text extraction (strips scripts, styles, tags); Stop word filtering for cleaner n-gram results; Vocabulary richness metric.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Analyze a text file\npython src/keyword_density_checker.py --input examples/sample_article.txt\n\n# Check specific keyword densities\npython src/keyword_density_checker.py --input article.txt --keywords \"seo,keyword density,python\"\n\n# Analyze HTML content\npython src/keyword_density_checker.py --input page.html --keywords \"seo tools\"\n\n# JSON output\npython src/keyword_density_checker.py --input article.txt --format json --output report.json" ], "included_files": [ "README.md", "examples/sample_article.txt", "guide/01_features.md", "guide/02_density-status-labels.md", "guide/03_license.md", "src/keyword_density_checker.py" ], "file_count": 6, "download_size_bytes": 36828, "download_size_human": "36.0 KB", "product_url": "https://datanest-stores.com/seo-toolkit/#product-keyword-density-checker", "preview_url": "https://datanest-stores.com/seo-toolkit/preview-keyword-density-checker", "demo_url": "https://datanest-stores.com/seo-toolkit/demo-keyword-density-checker/" }, { "product_id": "seo-toolkit:meta-tag-generator", "slug": "meta-tag-generator", "store": "seo-toolkit", "store_name": "SEO Toolkit", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "backlinks", "dev", "keywords", "meta tags", "page speed", "seo", "serp", "sitemap", "structured data" ], "title": "Meta Tag Generator", "short_description": "Generate SEO-optimized meta tags including title, description, OG tags, and Twitter cards.", "detailed_description": "Part of the SEO Toolkit by CodeVault Generate comprehensive HTML meta tags, Open Graph tags, and Twitter Card tags from a simple JSON config file. Never hand-write meta tags again. Includes: Generates all essential meta tags from a single config; Full Open Graph support (og; Twitter Card tags (summary, summarylargeimage); Validates your config and warns about common SEO mistakes; Title length check (Google truncates at ~60 chars); Description length check (max 160 chars).", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md", "examples/site_config.json", "guide/01_features.md", "guide/02_configuration-reference.md", "guide/03_cli-flags.md", "src/meta_tag_generator.py" ], "file_count": 6, "download_size_bytes": 37525, "download_size_human": "36.6 KB", "product_url": "https://datanest-stores.com/seo-toolkit/#product-meta-tag-generator", "preview_url": "https://datanest-stores.com/seo-toolkit/preview-meta-tag-generator", "demo_url": "https://datanest-stores.com/seo-toolkit/demo-meta-tag-generator/" }, { "product_id": "seo-toolkit:page-speed-auditor", "slug": "page-speed-auditor", "store": "seo-toolkit", "store_name": "SEO Toolkit", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "backlinks", "dev", "keywords", "meta tags", "page speed", "seo", "serp", "sitemap", "structured data" ], "title": "Page Speed Auditor", "short_description": "Page speed audit with Core Web Vitals, load time analysis, and optimization recommendations.", "detailed_description": "SEO Toolkit by CodeVault Audit page performance by analyzing HTML structure, resource sizes, render-blocking patterns, and DOM complexity \u2014 all from the command line with zero dependencies. Includes: HTML Size Analysis; Render-Blocking Detection; Image Audit; DOM Complexity; Inline Resource Tracking; Best Practices Check.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Copy the script to your project\ncp src/page_speed_auditor.py /your/project/\n\n# Or run it directly from this directory\npython src/page_speed_auditor.py --help", "# Audit a live URL\npython src/page_speed_auditor.py --url https://docs.example.com\n\n# Audit a local HTML file\npython src/page_speed_auditor.py --file examples/sample_page.html\n\n# JSON output for CI/CD integration\npython src/page_speed_auditor.py --url https://docs.example.com --format json\n\n# Batch audit multiple URLs\necho \"https://docs.example.com\nhttps://docs.example.com/about\nhttps://docs.example.com/contact\" > urls.txt\npython src/page_speed_auditor.py --input urls.txt\n\u2026" ], "included_files": [ "README.md", "examples/performance_budget.json", "examples/sample_page.html", "guide/01_features.md", "guide/02_performance-budget.md", "guide/03_exit-codes.md", "guide/04_faq.md", "src/page_speed_auditor.py" ], "file_count": 8, "download_size_bytes": 49078, "download_size_human": "47.9 KB", "product_url": "https://datanest-stores.com/seo-toolkit/#product-page-speed-auditor", "preview_url": "https://datanest-stores.com/seo-toolkit/preview-page-speed-auditor", "demo_url": "https://datanest-stores.com/seo-toolkit/demo-page-speed-auditor/" }, { "product_id": "seo-toolkit:redirect-mapper", "slug": "redirect-mapper", "store": "seo-toolkit", "store_name": "SEO Toolkit", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "backlinks", "dev", "keywords", "meta tags", "page speed", "seo", "serp", "sitemap", "structured data" ], "title": "Redirect Mapper", "short_description": "URL redirect mapping tool with chain detection, status code validation, and SEO impact analysis.", "detailed_description": "Part of the SEO Toolkit by CodeVault Validate your redirect maps before deploying them. Detect loops, chains, status code mistakes, protocol mismatches, and self-referencing redirects \u2014 all the things that tank rankings during site migrations. Includes: Load redirect rules from CSV or JSON files; Detect redirect loops (A \u2192 B \u2192 A) before they hit production; Find redirect chains (A \u2192 B \u2192 C \u2192 D) and warn about depth; Validate HTTP status codes (301 vs 302 vs 307/308); Catch protocol downgrades (HTTPS \u2192 HTTP); Flag trailing slash inconsistencies.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Validate a CSV redirect map\npython src/redirect_mapper.py --input examples/redirects.csv\n\n# Validate a JSON redirect map\npython src/redirect_mapper.py --input examples/redirects.json\n\n# Get JSON output for CI/CD\npython src/redirect_mapper.py --input examples/redirects.csv --format json\n\n# Strict mode \u2014 fail on any warning\npython src/redirect_mapper.py --input examples/redirects.csv --strict\n\n# Custom max chain depth\npython src/redirect_mapper.py --input examples/redirects.csv --max-depth 5\n\u2026", "old_url,new_url,status_code\nhttps://www.docs.example.com/old-page,https://www.docs.example.com/new-page,301\nhttps://www.docs.example.com/temp-page,https://www.docs.example.com/other,302" ], "included_files": [ "README.md", "examples/redirects.csv", "examples/redirects.json", "guide/01_features.md", "guide/02_input-formats.md", "guide/03_license.md", "src/redirect_mapper.py" ], "file_count": 7, "download_size_bytes": 36786, "download_size_human": "35.9 KB", "product_url": "https://datanest-stores.com/seo-toolkit/#product-redirect-mapper", "preview_url": "https://datanest-stores.com/seo-toolkit/preview-redirect-mapper", "demo_url": "https://datanest-stores.com/seo-toolkit/demo-redirect-mapper/" }, { "product_id": "seo-toolkit:robots-txt-generator", "slug": "robots-txt-generator", "store": "seo-toolkit", "store_name": "SEO Toolkit", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "backlinks", "dev", "keywords", "meta tags", "page speed", "seo", "serp", "sitemap", "structured data" ], "title": "Robots.txt Generator", "short_description": "Robots.txt generator with rule validation, sitemap links, and crawler directive management.", "detailed_description": "Part of the SEO Toolkit by CodeVault Generate production-ready robots.txt files from JSON configs or built-in presets. Includes rules for AI crawlers, search engines, and SEO tools. Includes: 4 built-in presets; Block AI crawlers (GPTBot, CCBot, anthropic-ai, Google-Extended); Multiple user-agent blocks with Allow/Disallow rules; Crawl-delay support; Sitemap references (from config or CLI flags); Generation timestamp.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md", "examples/robots_config.json", "guide/01_features.md", "guide/02_presets.md", "guide/03_cli-flags.md", "src/robots_txt_generator.py" ], "file_count": 6, "download_size_bytes": 32457, "download_size_human": "31.7 KB", "product_url": "https://datanest-stores.com/seo-toolkit/#product-robots-txt-generator", "preview_url": "https://datanest-stores.com/seo-toolkit/preview-robots-txt-generator", "demo_url": "https://datanest-stores.com/seo-toolkit/demo-robots-txt-generator/" }, { "product_id": "seo-toolkit:seo-toolkit-pro-bundle", "slug": "seo-toolkit-pro-bundle", "store": "seo-toolkit", "store_name": "SEO Toolkit", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "backlinks", "bundle", "dev", "keywords", "meta tags", "page speed", "seo", "serp", "sitemap", "structured data" ], "title": "SEO Toolkit Pro Bundle", "short_description": "All 10 SEO tools \u2014 $230 bought separately, yours for $59. Save $171 (74%): meta tags, sitemaps, SERP analysis, structured data, and speed audits.", "detailed_description": "SEO Toolkit by CodeVault The complete SEO command-line arsenal \u2014 all 10 tools at a massive discount. Everything you need to audit, optimize, and monitor your site's technical SEO from the terminal.", "price_usd": 59, "currency": "usd", "is_bundle": true, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [], "supported_versions": [], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [ "seo-toolkit-pro-bundle/\n\u251c\u2500\u2500 meta-tag-generator/\n\u251c\u2500\u2500 sitemap-builder/\n\u251c\u2500\u2500 robots-txt-generator/\n\u251c\u2500\u2500 serp-analyzer/\n\u251c\u2500\u2500 keyword-density-checker/\n\u251c\u2500\u2500 structured-data-validator/\n\u251c\u2500\u2500 redirect-mapper/\n\u251c\u2500\u2500 canonical-url-planner/\n\u251c\u2500\u2500 backlink-checker/\n\u2514\u2500\u2500 page-speed-auditor/", "# GitHub Actions example\nsteps:\n - name: SEO Audit\n run: |\n python page-speed-auditor/src/page_speed_auditor.py --url ${{ env.SITE_URL }} --format json --output speed.json\n python structured-data-validator/src/structured_data_validator.py --input schema.json --format json --output schema-report.json\n python canonical-url-planner/src/canonical_url_planner.py --input pages.csv --strict" ], "included_files": [ "README.md" ], "file_count": 1, "download_size_bytes": 263771, "download_size_human": "257.6 KB", "product_url": "https://datanest-stores.com/seo-toolkit/#product-seo-toolkit-pro-bundle", "preview_url": "https://datanest-stores.com/seo-toolkit/preview-seo-toolkit-pro-bundle", "demo_url": null }, { "product_id": "seo-toolkit:serp-analyzer", "slug": "serp-analyzer", "store": "seo-toolkit", "store_name": "SEO Toolkit", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "backlinks", "dev", "keywords", "meta tags", "page speed", "seo", "serp", "sitemap", "structured data" ], "title": "SERP Analyzer", "short_description": "SERP analysis tool for keyword rankings, snippet previews, and competitor position tracking.", "detailed_description": "Part of the SEO Toolkit by CodeVault Analyze saved search engine results pages (SERPs) to extract featured snippets, People Also Ask questions, ranking signals, and content gap opportunities. Includes: Extracts organic results with titles, URLs, and descriptions; Detects 9 SERP features; People Also Ask question extraction; Title pattern analysis (length, common words, year usage); Content gap identification with actionable recommendations; Keyword presence checking across result titles.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# Analyze a saved SERP page\npython src/serp_analyzer.py --input examples/sample_serp.html\n\n# With query context and keyword tracking\npython src/serp_analyzer.py --input serp.html --query \"python seo tools\" --keywords \"python,seo,tools\"\n\n# JSON output for programmatic use\npython src/serp_analyzer.py --input serp.html --format json --output report.json" ], "included_files": [ "README.md", "examples/sample_serp.html", "guide/01_features.md", "guide/02_how-to-save-a-serp-page.md", "guide/03_cli-flags.md", "src/serp_analyzer.py" ], "file_count": 6, "download_size_bytes": 38323, "download_size_human": "37.4 KB", "product_url": "https://datanest-stores.com/seo-toolkit/#product-serp-analyzer", "preview_url": "https://datanest-stores.com/seo-toolkit/preview-serp-analyzer", "demo_url": "https://datanest-stores.com/seo-toolkit/demo-serp-analyzer/" }, { "product_id": "seo-toolkit:sitemap-builder", "slug": "sitemap-builder", "store": "seo-toolkit", "store_name": "SEO Toolkit", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "backlinks", "dev", "keywords", "meta tags", "page speed", "seo", "serp", "sitemap", "structured data" ], "title": "Sitemap Builder", "short_description": "XML sitemap generator with automatic discovery, priority assignment, and changefreq configuration.", "detailed_description": "Part of the SEO Toolkit by CodeVault Generate standards-compliant sitemap.xml files from URL lists or JSON configs. Supports priority rules, change frequencies, exclusion patterns, and sitemap indexes. Includes: Generates valid sitemap; Configurable priority rules (e; Configurable changefreq rules (e; URL exclusion patterns (skip admin pages, staging URLs, etc; Accepts URLs from a text file, JSON config, or stdin; Pretty-printed or compact XML output.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [ "# From a URL list file\npython src/sitemap_builder.py --urls urls.txt --output sitemap.xml\n\n# From a JSON config (with rules and priorities)\npython src/sitemap_builder.py --config examples/sitemap_config.json --output sitemap.xml\n\n# Pipe URLs from another command\ncurl -s https://api.docs.example.com/urls | python src/sitemap_builder.py --output sitemap.xml\n\n# With stats\npython src/sitemap_builder.py --config examples/sitemap_config.json --output sitemap.xml --stats" ], "included_files": [ "README.md", "examples/sitemap_config.json", "guide/01_features.md", "guide/02_configuration-reference.md", "guide/03_license.md", "src/sitemap_builder.py" ], "file_count": 6, "download_size_bytes": 35754, "download_size_human": "34.9 KB", "product_url": "https://datanest-stores.com/seo-toolkit/#product-sitemap-builder", "preview_url": "https://datanest-stores.com/seo-toolkit/preview-sitemap-builder", "demo_url": "https://datanest-stores.com/seo-toolkit/demo-sitemap-builder/" }, { "product_id": "seo-toolkit:structured-data-validator", "slug": "structured-data-validator", "store": "seo-toolkit", "store_name": "SEO Toolkit", "domain": "dev", "category": "Dev & Web Craft", "tags": [ "backlinks", "dev", "keywords", "meta tags", "page speed", "seo", "serp", "sitemap", "structured data" ], "title": "Structured Data Validator", "short_description": "Validate JSON-LD, Microdata, and RDFa structured data against Schema.org standards.", "detailed_description": "Part of the SEO Toolkit by CodeVault Validate JSON-LD structured data against Schema.org type specs \u2014 locally, offline, and CI/CD-ready. Catches missing required fields, type mismatches, invalid contexts, and common markup errors that break Google rich results. Includes: Validates JSON-LD files (; Checks 16+ Schema; Validates required fields per type (e; Checks recommended fields and suggests additions for richer results; Validates field values; Recursively validates nested objects (publisher, author, etc.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python" ], "supported_versions": [], "supported_technologies": [ "Python" ], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md", "examples/sample_jsonld.json", "examples/sample_page.html", "guide/01_features.md", "guide/02_schema-types-supported.md", "guide/03_license.md", "src/structured_data_validator.py" ], "file_count": 7, "download_size_bytes": 40842, "download_size_human": "39.9 KB", "product_url": "https://datanest-stores.com/seo-toolkit/#product-structured-data-validator", "preview_url": "https://datanest-stores.com/seo-toolkit/preview-structured-data-validator", "demo_url": "https://datanest-stores.com/seo-toolkit/demo-structured-data-validator/" }, { "product_id": "spreadsheet-tools:financial-dashboard", "slug": "financial-dashboard", "store": "spreadsheet-tools", "store_name": "Spreadsheet Tools Pro", "domain": "data", "category": "Data & Analytics", "tags": [ "apps script", "budget", "data", "excel", "financial dashboard", "google sheets", "kpi tracker", "spreadsheets" ], "title": "Small Business Financial Dashboard", "short_description": "P&L statement, cash flow tracker, balance sheet, and financial forecasting with automated charts and KPI indicators.", "detailed_description": "A comprehensive financial workbook for small businesses that brings together your P&L statement, cash flow tracker, balance sheet, and financial forecasting into a single, interconnected spreadsheet. Built for Google Sheets and Microsoft Excel.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [], "supported_versions": [], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [ "financial-dashboard/\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 sheets/\n\u2502 \u251c\u2500\u2500 01-pnl.csv\n\u2502 \u251c\u2500\u2500 02-cash-flow.csv\n\u2502 \u251c\u2500\u2500 03-balance-sheet.csv\n\u2502 \u251c\u2500\u2500 04-revenue-breakdown.csv\n\u2502 \u251c\u2500\u2500 05-expense-tracker.csv\n\u2502 \u251c\u2500\u2500 06-kpi-dashboard.csv\n\u2502 \u2514\u2500\u2500 07-forecast.csv\n\u251c\u2500\u2500 formulas/\n\u2502 \u2514\u2500\u2500 FORMULAS.md\n\u251c\u2500\u2500 docs/\n\u2026" ], "included_files": [ "README.md", "docs/CUSTOMIZATION.md", "docs/GETTING-STARTED.md", "docs/IMPORT-GUIDE.md", "financial-dashboard.xlsx", "formulas/FORMULAS.md", "guide/financial-ratios-explained.md", "guide/reading-your-pnl.md", "guides/financial-ratios-explained.md", "guides/reading-your-pnl.md", "sample.xlsx", "sheets/01-pnl.csv", "sheets/02-cash-flow.csv", "sheets/03-balance-sheet.csv", "sheets/04-revenue-breakdown.csv", "sheets/05-expense-tracker.csv", "sheets/06-kpi-dashboard.csv", "sheets/07-forecast.csv" ], "file_count": 18, "download_size_bytes": 103184, "download_size_human": "100.8 KB", "product_url": "https://datanest-stores.com/spreadsheet-tools/#product-financial-dashboard", "preview_url": "https://datanest-stores.com/spreadsheet-tools/preview-financial-dashboard", "demo_url": "https://datanest-stores.com/spreadsheet-tools/demo-financial-dashboard/" }, { "product_id": "spreadsheet-tools:marketing-roi-tracker", "slug": "marketing-roi-tracker", "store": "spreadsheet-tools", "store_name": "Spreadsheet Tools Pro", "domain": "data", "category": "Data & Analytics", "tags": [ "apps script", "budget", "data", "excel", "financial dashboard", "google sheets", "kpi tracker", "spreadsheets" ], "title": "Marketing Campaign ROI Tracker", "short_description": "Track ad spend, conversions, cost per acquisition, and ROI across multiple channels with automated reporting.", "detailed_description": "A comprehensive spreadsheet system for tracking advertising spend, conversions, cost per acquisition, and return on investment across multiple marketing channels. Built for marketing managers, growth teams, and agency professionals who need clear visibility into campaign performance without expensive analytics tools.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [], "supported_versions": [], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md", "docs/CUSTOMIZATION.md", "docs/GETTING-STARTED.md", "docs/IMPORT-GUIDE.md", "formulas/FORMULAS.md", "guide/attribution-models.md", "guide/calculating-cac-and-roas.md", "guides/attribution-models.md", "guides/calculating-cac-and-roas.md", "marketing-roi-tracker.xlsx", "sample.xlsx", "sheets/01-campaign-overview.csv", "sheets/02-channel-performance.csv", "sheets/03-monthly-spend.csv", "sheets/04-conversion-tracking.csv", "sheets/05-roi-summary.csv", "sheets/06-dashboard-data.csv" ], "file_count": 17, "download_size_bytes": 97818, "download_size_human": "95.5 KB", "product_url": "https://datanest-stores.com/spreadsheet-tools/#product-marketing-roi-tracker", "preview_url": "https://datanest-stores.com/spreadsheet-tools/preview-marketing-roi-tracker", "demo_url": "https://datanest-stores.com/spreadsheet-tools/demo-marketing-roi-tracker/" }, { "product_id": "spreadsheet-tools:project-management-sheet", "slug": "project-management-sheet", "store": "spreadsheet-tools", "store_name": "Spreadsheet Tools Pro", "domain": "data", "category": "Data & Analytics", "tags": [ "apps script", "budget", "data", "excel", "financial dashboard", "google sheets", "kpi tracker", "spreadsheets" ], "title": "Project Management Spreadsheet", "short_description": "Gantt chart, resource allocation, budget tracker, and milestone tracking with automated status dashboards.", "detailed_description": "A spreadsheet-based project tracker with a built-in Gantt chart, resource plan, budget tracker, milestone log, status dashboard, and risk register \u2014 all driven by one task list, all in plain Google Sheets or Excel. No add-ons, no macros, no project-management SaaS subscription. This workbook is built around a worked example: a \"Customer Portal Redesign\" project, 18 tasks across five phases (Discovery \u2192 Design \u2192 Development \u2192 QA & Testing \u2192 Launch), tracked as of a real status date (2025-03-17). Every rollup reconciles \u2014 the Gantt bars, the per-person allocation, the budget variance, the milestone slip, and the dashboard KPIs are all computed from the single task list, so you can trace any number back to the row it came from. Replace the sample tasks with your own and the whole workbook recalculates. Includes: formulas/FORMULAS; guides/building-a-gantt-in-sheets; guides/resource-leveling; docs/ \u2014 getting-started, CSV import, and customization guides.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [], "supported_versions": [], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md", "docs/CUSTOMIZATION.md", "docs/GETTING-STARTED.md", "docs/IMPORT-GUIDE.md", "formulas/FORMULAS.md", "guide/building-a-gantt-in-sheets.md", "guide/resource-leveling.md", "guides/building-a-gantt-in-sheets.md", "guides/resource-leveling.md", "project-management-sheet.xlsx", "sample.xlsx", "sheets/01-task-list.csv", "sheets/02-gantt-chart.csv", "sheets/03-resource-allocation.csv", "sheets/04-budget-tracker.csv", "sheets/05-milestone-log.csv", "sheets/06-status-dashboard.csv", "sheets/07-team-roster.csv", "sheets/08-risk-register.csv" ], "file_count": 19, "download_size_bytes": 126356, "download_size_human": "123.4 KB", "product_url": "https://datanest-stores.com/spreadsheet-tools/#product-project-management-sheet", "preview_url": "https://datanest-stores.com/spreadsheet-tools/preview-project-management-sheet", "demo_url": "https://datanest-stores.com/spreadsheet-tools/demo-project-management-sheet/" }, { "product_id": "spreadsheet-tools:hr-hiring-tracker", "slug": "hr-hiring-tracker", "store": "spreadsheet-tools", "store_name": "Spreadsheet Tools Pro", "domain": "data", "category": "Data & Analytics", "tags": [ "apps script", "budget", "data", "excel", "financial dashboard", "google sheets", "kpi tracker", "spreadsheets" ], "title": "HR & Hiring Pipeline Tracker", "short_description": "Applicant tracking system with pipeline stages, interview scoring, offer tracking, and hiring metrics dashboard.", "detailed_description": "A comprehensive applicant tracking system built entirely in Google Sheets / Excel. Track candidates from first application through offer acceptance, score interviews consistently, and monitor hiring metrics \u2014 all without expensive ATS software.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [], "supported_versions": [], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "README.md", "docs/CUSTOMIZATION.md", "docs/GETTING-STARTED.md", "docs/IMPORT-GUIDE.md", "formulas/FORMULAS.md", "guide/structured-interview-scoring.md", "guides/structured-interview-scoring.md", "hr-hiring-tracker.xlsx", "sample.xlsx", "sheets/applicants.csv", "sheets/hiring-metrics.csv", "sheets/interview-scorecards.csv", "sheets/job-requisitions.csv", "sheets/offers.csv", "sheets/pipeline-stages.csv" ], "file_count": 15, "download_size_bytes": 93065, "download_size_human": "90.9 KB", "product_url": "https://datanest-stores.com/spreadsheet-tools/#product-hr-hiring-tracker", "preview_url": "https://datanest-stores.com/spreadsheet-tools/preview-hr-hiring-tracker", "demo_url": "https://datanest-stores.com/spreadsheet-tools/demo-hr-hiring-tracker/" }, { "product_id": "spreadsheet-tools:sales-pipeline-crm", "slug": "sales-pipeline-crm", "store": "spreadsheet-tools", "store_name": "Spreadsheet Tools Pro", "domain": "data", "category": "Data & Analytics", "tags": [ "apps script", "budget", "data", "excel", "financial dashboard", "google sheets", "kpi tracker", "spreadsheets" ], "title": "Sales Pipeline & CRM", "short_description": "Lead tracking, deal stages, revenue forecasting, and sales performance dashboard with automated pipeline views.", "detailed_description": "A complete spreadsheet-based CRM and sales pipeline management system. 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"products/data-cleaning-templates/source/sample.xlsx", "products/data-cleaning-templates/source/sheets/01-dirty-data.csv", "products/data-cleaning-templates/source/sheets/02-cleaned-data.csv", "products/data-cleaning-templates/source/sheets/03-deduplication.csv", "products/data-cleaning-templates/source/sheets/04-validation-rules.csv", "products/data-cleaning-templates/source/sheets/05-normalization-map.csv", "products/data-cleaning-templates/source/sheets/06-pivot-source.csv", "products/data-cleaning-templates/source/sheets/07-pivot-summary.csv", "products/data-cleaning-templates/source/sheets/08-statistical-summary.csv", "products/financial-dashboard/README.md", "products/financial-dashboard/source/README.md", "products/financial-dashboard/source/docs/CUSTOMIZATION.md", "products/financial-dashboard/source/docs/GETTING-STARTED.md", "products/financial-dashboard/source/docs/IMPORT-GUIDE.md", "products/financial-dashboard/source/financial-dashboard.xlsx", "products/financial-dashboard/source/formulas/FORMULAS.md", "products/financial-dashboard/source/guide/financial-ratios-explained.md", "products/financial-dashboard/source/guide/reading-your-pnl.md", "products/financial-dashboard/source/guides/financial-ratios-explained.md", "products/financial-dashboard/source/guides/reading-your-pnl.md", "products/financial-dashboard/source/sample.xlsx", "products/financial-dashboard/source/sheets/01-pnl.csv", "products/financial-dashboard/source/sheets/02-cash-flow.csv", "products/financial-dashboard/source/sheets/03-balance-sheet.csv" ], "file_count": 80, "download_size_bytes": 1134807, "download_size_human": "1.1 MB", "product_url": "https://datanest-stores.com/spreadsheet-tools/#product-spreadsheet-complete-bundle", "preview_url": null, "demo_url": null }, { "product_id": "sre-platform:runbook-template-library", "slug": "runbook-template-library", "store": "sre-platform", "store_name": "SRE Platform Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "capacity planning", "chaos engineering", "cloud", "on-call", "postmortem", "runbooks", "sli/slo", "sre" ], "title": "Runbook Template Library", "short_description": "50+ operational runbooks for common incidents: database issues, memory leaks, traffic spikes, deployment failures.", "detailed_description": "A comprehensive library of 54 operational runbooks for Site Reliability Engineering teams, covering the most common infrastructure incidents and operational procedures. --- Includes: 54 detailed runbooks organized into 7 categories (database, compute, network, deploy, storage, observability, security); Blank template (RUNBOOKTEMPLATE; Writing guide (WRITINGGUIDE; Complete index (INDEX.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [], "supported_versions": [], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [], "included_files": [ "INDEX.md", "README.md", "RUNBOOK_TEMPLATE.md", "WRITING_GUIDE.md", "compute/container-crash.md", "compute/cpu-saturation.md", "compute/disk-io-saturation.md", "compute/memory-leak.md", "compute/oom-kills.md", "compute/restart-loops.md", "compute/thread-pool-exhaustion.md", "compute/zombie-processes.md", "database/backup-failure.md", "database/connection-pool-exhaustion.md", "database/deadlock-detection.md", "database/disk-full.md", "database/failover-procedure.md", "database/index-corruption.md", "database/replication-lag.md", "database/schema-migration-failure.md", "database/slow-queries.md", "deploy/blue-green-switch-failure.md", "deploy/canary-abort.md", "deploy/config-drift.md", "deploy/dependency-failure.md", "deploy/failed-rollout.md", "deploy/helm-release-stuck.md", "deploy/migration-failure.md", "deploy/rollback-procedure.md", "guide/01_runbook-structure.md", "guide/02_writing-guide.md", "guide/03_runbook-index.md", "network/connection-timeout.md", "network/dns-failure.md", "network/firewall-misconfiguration.md", "network/load-balancer-issues.md", "network/packet-loss.md", "network/service-mesh-failure.md", "network/tls-cert-expiry.md", "network/traffic-spike.md", "observability/alerting-storm.md", "observability/cardinality-explosion.md", "observability/dashboard-data-gap.md", "observability/log-ingestion-failure.md", "observability/metrics-pipeline-lag.md", "observability/monitoring-blind-spot.md", "observability/trace-collection-failure.md", "security/api-key-leak.md", "security/cert-rotation.md", "security/ddos-mitigation.md", "security/privilege-escalation.md", "security/secret-rotation.md", "security/suspected-breach.md", "security/unauthorized-access.md", "storage/data-corruption.md", "storage/disk-full.md", "storage/nfs-mount-stale.md", "storage/object-storage-latency.md", "storage/quota-exceeded.md", "storage/snapshot-failure.md", "storage/volume-detach-failure.md" ], "file_count": 61, "download_size_bytes": 265425, "download_size_human": "259.2 KB", "product_url": "https://datanest-stores.com/sre-platform/#product-runbook-template-library", "preview_url": "https://datanest-stores.com/sre-platform/preview-runbook-template-library", "demo_url": "https://datanest-stores.com/sre-platform/demo-runbook-template-library/" }, { "product_id": "sre-platform:chaos-engineering-toolkit", "slug": "chaos-engineering-toolkit", "store": "sre-platform", "store_name": "SRE Platform Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "capacity planning", "chaos engineering", "cloud", "on-call", "postmortem", "runbooks", "sli/slo", "sre" ], "title": "Chaos Engineering Toolkit", "short_description": "Chaos experiment designs, Litmus/Gremlin configs, failure injection scripts, and game day planning templates.", "detailed_description": "A comprehensive toolkit for designing, executing, and managing chaos engineering experiments. Includes Python failure injection libraries (stdlib only), experiment configurations, game day templates, and a maturity model guide. Includes: Failure Injection Engine \u2014 Decorator-based latency/error/resource injection with safety controls; Steady-State Hypothesis Checker \u2014 Define and evaluate system health before, during, and after experiments; Blast Radius Control \u2014 Progressive expansion with consistent hashing, automatic abort, and kill switch; Experiment Catalog \u2014 Pre-built YAML experiment definitions for common failure scenarios; Game Day Templates \u2014 Structured planning documents for team chaos exercises; Maturity Model Guide \u2014 Five-level progression framework for building a chaos program.", "price_usd": 49, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "Standard library only \u2014 no external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "from src.failure_injector import FailureInjector, InjectionConfig, FailureType\n\ninjector = FailureInjector()\nconfig = InjectionConfig(\n failure_type=FailureType.LATENCY,\n probability=0.3, # Affect 30% of calls\n latency_ms=500, # Add 500ms delay\n duration_seconds=300, # Run for 5 minutes\n blast_radius_pct=30.0, # Safety: never exceed 30%\n)\ninjector.activate(config)\n\n@injector.inject_latency(config)\ndef call_user_service(user_id: str) -> dict:\n\u2026", "from src.blast_radius import create_progressive_experiment\n\nlimiter = create_progressive_experiment(\n initial_pct=5.0, # Start with 5% of traffic\n max_pct=30.0, # Never exceed 30%\n step_pct=5.0, # Increase by 5% each step\n step_interval_seconds=300, # Expand every 5 minutes\n abort_at_error_rate=0.05, # Abort if errors exceed 5%\n)\n\nlimiter.activate()\n\n# For each incoming request:\nif limiter.should_affect(request_id=request.id):\n\u2026" ], "included_files": [ "README.md", "experiments/dependency_failure.yaml", "experiments/latency_injection.yaml", "experiments/pod_failure.yaml", "guide/01-chaos-engineering-foundations.md", "guide/02-experiment-design.md", "guide/03-steady-state-hypothesis.md", "guide/04-game-day-planning.md", "guide/05-chaos-maturity-model.md", "guides/chaos_maturity_model.md", "src/__init__.py", "src/blast_radius.py", "src/failure_injector.py", "src/steady_state.py", "templates/experiment_proposal.md", "templates/gameday_plan.md", "tests/test_chaos.py" ], "file_count": 17, "download_size_bytes": 82826, "download_size_human": "80.9 KB", "product_url": "https://datanest-stores.com/sre-platform/#product-chaos-engineering-toolkit", "preview_url": "https://datanest-stores.com/sre-platform/preview-chaos-engineering-toolkit", "demo_url": "https://datanest-stores.com/sre-platform/demo-chaos-engineering-toolkit/" }, { "product_id": "sre-platform:on-call-management-kit", "slug": "on-call-management-kit", "store": "sre-platform", "store_name": "SRE Platform Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "capacity planning", "chaos engineering", "cloud", "on-call", "postmortem", "runbooks", "sli/slo", "sre" ], "title": "On-Call Management Kit", "short_description": "PagerDuty/OpsGenie configs, escalation policies, rotation schedules, and incident communication templates.", "detailed_description": "A complete on-call operations toolkit: configuration-as-code for PagerDuty and OpsGenie, Python rotation generators, incident communication templates, and guides for building a sustainable on-call program. Includes: PagerDuty configuration-as-code \u2014 Escalation policies, service definitions, and alert routing rules in YAML ready for Terraform/Pulumi import or API-driven provisioning; OpsGenie configuration-as-code \u2014 Equivalent configs for OpsGenie including schedules, routing rules, and escalation policies; Python rotation tools (stdlib only) \u2014 Generate rotation calendars, validate escalation coverage gaps, and export schedules to iCal format; Incident communication templates \u2014 Pre-written markdown templates for status page updates, stakeholder communications, and severity-specific incident comms; On-call handoff checklist \u2014 Structured handoff procedure to reduce context loss between rotations; Healthy on-call program guide \u2014 Comprehensive guide covering toil budgets, alert hygiene, compensation models, and burnout prevention.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "from src.rotation_generator import RotationGenerator, RotationConfig\n\nconfig = RotationConfig(\n team_members=[\"alice\", \"bob\", \"carol\", \"dave\"],\n rotation_type=\"weekly\",\n start_date=\"2025-01-06\",\n weeks_to_generate=12,\n handoff_day=\"monday\",\n handoff_hour=9,\n)\n\ngenerator = RotationGenerator(config)\nschedule = generator.generate()\n\n\u2026", "from src.escalation_validator import EscalationValidator\n\nvalidator = EscalationValidator()\nvalidator.load_from_yaml(\"configs/pagerduty/escalation_policies.yaml\")\n\nreport = validator.validate()\nif report.has_gaps:\n for gap in report.gaps:\n print(f\"COVERAGE GAP: {gap.start} to {gap.end} \u2014 no responder assigned\")\nelse:\n print(\"All escalation policies have complete 24/7 coverage\")" ], "included_files": [ "README.md", "configs/opsgenie/escalation_policies.yaml", "configs/opsgenie/routing_rules.yaml", "configs/opsgenie/schedules.yaml", "configs/pagerduty/alert_routing.yaml", "configs/pagerduty/escalation_policies.yaml", "configs/pagerduty/services.yaml", "guide/01-building-an-on-call-program.md", "guide/02-configuration-as-code.md", "guide/03-rotation-generation-and-coverage.md", "guide/04-incident-communication-templates.md", "guide/05-alert-hygiene-and-burnout-prevention.md", "guides/alert_hygiene.md", "guides/healthy_oncall_program.md", "src/escalation_validator.py", "src/rotation_generator.py", "src/schedule_exporter.py", "templates/incident_comms_sev1.md", "templates/incident_comms_sev2.md", "templates/incident_comms_sev3.md", "templates/on_call_handoff_checklist.md", "templates/stakeholder_comms.md", "templates/status_page_update.md" ], "file_count": 23, "download_size_bytes": 136478, "download_size_human": "133.3 KB", "product_url": "https://datanest-stores.com/sre-platform/#product-on-call-management-kit", "preview_url": "https://datanest-stores.com/sre-platform/preview-on-call-management-kit", "demo_url": "https://datanest-stores.com/sre-platform/demo-on-call-management-kit/" }, { "product_id": "sre-platform:capacity-planning-toolkit", "slug": "capacity-planning-toolkit", "store": "sre-platform", "store_name": "SRE Platform Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "capacity planning", "chaos engineering", "cloud", "on-call", "postmortem", "runbooks", "sli/slo", "sre" ], "title": "Capacity Planning Toolkit", "short_description": "Load forecasting models, resource utilization tracking, scaling decision frameworks, and capacity review templates.", "detailed_description": "A Python toolkit for infrastructure load forecasting, resource utilization tracking, scaling decisions, and cost modeling. All implementations use Python standard library only \u2014 no numpy, pandas, or scipy required. Includes: Load Forecasting \u2014 Linear regression, seasonal decomposition, double exponential smoothing, all implemented from scratch; Resource Utilization Tracking \u2014 Fleet-wide CPU/memory/disk monitoring with anomaly detection via z-score analysis; Scaling Advisor \u2014 Headroom-based scaling recommendations with tier-specific policies and cost-aware decisions; Cost Modeling \u2014 Unit economics computation, scaling cost projections, and optimization opportunity identification; Auto-Method Selection \u2014 Forecaster automatically picks the best algorithm based on data characteristics.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "Standard library only \u2014 zero external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "from src.forecaster import Forecaster, TimeSeriesPoint\nfrom datetime import datetime, timedelta\n\n# Load your historical utilization data\ndata = [\n TimeSeriesPoint(timestamp=datetime(2024, 1, 1) + timedelta(days=i), value=cpu_pct)\n for i, cpu_pct in enumerate(your_daily_cpu_data)\n]\n\nforecaster = Forecaster()\nresult = forecaster.auto_forecast(data, horizon_days=90)\n\nprint(f\"Method selected: {result.method}\")\nprint(f\"Model fit (R\u00b2): {result.r_squared:.3f}\")\n\u2026", "from src.utilization import UtilizationTracker, ResourceMetric, ResourceType\nfrom datetime import datetime\n\ntracker = UtilizationTracker(waste_threshold=15.0, critical_threshold=85.0)\n\ntracker.record(ResourceMetric(\n resource_id=\"web-server-001\",\n resource_type=ResourceType.CPU,\n timestamp=datetime.utcnow(),\n utilization_pct=72.5,\n capacity_total=8.0, # 8 CPU cores\n capacity_used=5.8,\n metadata={\"cluster\": \"production\", \"tier\": \"web\"},\n))\n\u2026" ], "included_files": [ "README.md", "configs/thresholds.yaml", "examples/sample_utilization.csv", "guide/01-capacity-planning-fundamentals.md", "guide/02-load-forecasting-methods.md", "guide/03-resource-utilization-tracking.md", "guide/04-scaling-decisions-and-cost-modeling.md", "guide/05-capacity-review-process.md", "guides/capacity_review_guide.md", "src/__init__.py", "src/cost_model.py", "src/forecaster.py", "src/scaling_advisor.py", "src/utilization.py", "tests/test_forecaster.py" ], "file_count": 15, "download_size_bytes": 93697, "download_size_human": "91.5 KB", "product_url": "https://datanest-stores.com/sre-platform/#product-capacity-planning-toolkit", "preview_url": "https://datanest-stores.com/sre-platform/preview-capacity-planning-toolkit", "demo_url": "https://datanest-stores.com/sre-platform/demo-capacity-planning-toolkit/" }, { "product_id": "sre-platform:platform-developer-portal", "slug": "platform-developer-portal", "store": "sre-platform", "store_name": "SRE Platform Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "capacity planning", "chaos engineering", "cloud", "on-call", "postmortem", "runbooks", "sli/slo", "sre" ], "title": "Platform Developer Portal", "short_description": "Internal developer portal with service catalog, API docs, onboarding guides, and self-service infrastructure templates.", "detailed_description": "A comprehensive internal developer portal (IDP) starter kit inspired by Backstage. Includes service catalog definitions, scaffolding templates, API documentation, golden-path onboarding guides, self-service infrastructure modules, and a Python-based catalog validator. Includes: Service Catalog (Backstage-compatible YAML) \u2014 catalog-info; Software Templates (Scaffolder YAML) \u2014 Ready-to-use scaffolder templates for creating new services, APIs, and frontend applications following your golden path; API Documentation (OpenAPI 3; Golden Path Onboarding Guides \u2014 Markdown guides for new developers; Self-Service Infrastructure (HCL/Terraform) \u2014 Reusable Terraform modules for common infrastructure patterns (ECS services, RDS databases, S3 buckets); TechDocs Structure \u2014 MkDocs-ready documentation structure for publishing technical documentation through your portal.", "price_usd": 49, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "Python 3.10+", "Terraform 1.5+", "MkDocs 1.5+" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Terraform", "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "platform-developer-portal/\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 catalog/\n\u2502 \u251c\u2500\u2500 catalog-info-api-gateway.yaml\n\u2502 \u251c\u2500\u2500 catalog-info-user-service.yaml\n\u2502 \u251c\u2500\u2500 catalog-info-payment-service.yaml\n\u2502 \u2514\u2500\u2500 catalog-info-notification-service.yaml\n\u251c\u2500\u2500 templates/\n\u2502 \u251c\u2500\u2500 new-backend-service.yaml\n\u2502 \u251c\u2500\u2500 new-api-service.yaml\n\u2502 \u2514\u2500\u2500 new-frontend-app.yaml\n\u251c\u2500\u2500 docs/\n\u2502 \u251c\u2500\u2500 onboarding/\n\u2026", "from src.catalog_validator import CatalogValidator\n\nvalidator = CatalogValidator()\nvalidator.load_directory(\"catalog/\")\n\nreport = validator.validate()\nprint(report.summary())\n# Outputs: validation results for all catalog-info.yaml files" ], "included_files": [ "README.md", "catalog/catalog-info-api-gateway.yaml", "catalog/catalog-info-notification-service.yaml", "catalog/catalog-info-payment-service.yaml", "catalog/catalog-info-user-service.yaml", "docs/api/payment-service-openapi.yaml", "docs/api/user-service-openapi.yaml", "docs/onboarding/getting-started.md", "docs/onboarding/golden-path.md", "docs/onboarding/service-ownership.md", "docs/techdocs/docs/architecture.md", "docs/techdocs/docs/index.md", "docs/techdocs/mkdocs.yml", "guide/01-idp-fundamentals.md", "guide/02-service-catalog-design.md", "guide/03-software-templates-and-golden-paths.md", "guide/04-api-documentation-and-techdocs.md", "guide/05-self-service-infrastructure.md", "infra/modules/ecs-service/README.md", "infra/modules/ecs-service/main.tf", "infra/modules/ecs-service/variables.tf", "infra/modules/rds-postgres/README.md", "infra/modules/rds-postgres/main.tf", "infra/modules/rds-postgres/variables.tf", "infra/modules/s3-bucket/README.md", "infra/modules/s3-bucket/main.tf", "infra/modules/s3-bucket/variables.tf", "src/catalog_linter.py", "src/catalog_validator.py", "templates/new-api-service.yaml", "templates/new-backend-service.yaml", "templates/new-frontend-app.yaml" ], "file_count": 32, "download_size_bytes": 116519, "download_size_human": "113.8 KB", "product_url": "https://datanest-stores.com/sre-platform/#product-platform-developer-portal", "preview_url": "https://datanest-stores.com/sre-platform/preview-platform-developer-portal", "demo_url": "https://datanest-stores.com/sre-platform/demo-platform-developer-portal/" }, { "product_id": "sre-platform:postmortem-framework", "slug": "postmortem-framework", "store": "sre-platform", "store_name": "SRE Platform Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "capacity planning", "chaos engineering", "cloud", "on-call", "postmortem", "runbooks", "sli/slo", "sre" ], "title": "Postmortem Framework", "short_description": "Blameless postmortem templates, root cause analysis methods, action item tracking, and trend analysis dashboards.", "detailed_description": "A complete system for running blameless postmortems, tracking action items, and measuring incident response effectiveness. 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This file\n+-- LICENSE # MIT License\n+-- index.html # Interactive reader (free preview)\n+-- free-sample.zip # Free sample with Chapter 1\n+-- guide/\n +-- 01-*.md through 05-*.md" ], "included_files": [ "README.md", "guide/01-capacity-planning-fundamentals.md", "guide/02-demand-forecasting-and-modeling.md", "guide/03-cost-optimization-and-scaling-strategies.md", "guide/04-capacity-review-process-and-governance.md", "guide/05-tools,-automation,-and-advanced-topics.md" ], "file_count": 6, "download_size_bytes": 45130, "download_size_human": "44.1 KB", "product_url": "https://datanest-stores.com/sre-platform/#product-capacity-planning-guide", "preview_url": "https://datanest-stores.com/sre-platform/preview-capacity-planning-guide", "demo_url": "https://datanest-stores.com/sre-platform/demo-capacity-planning-guide/" }, { "product_id": "sre-platform:error-budget-policy", "slug": "error-budget-policy", "store": "sre-platform", "store_name": "SRE Platform Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "capacity planning", "chaos engineering", "cloud", "on-call", "postmortem", "runbooks", "sli/slo", "sre" ], "title": "Error Budget Policy", "short_description": "Define and enforce error budget policies across the org: burn-rate alerting, freeze triggers, and stakeholder reporting templates.", "detailed_description": "Define and Enforce Error Budget Policies Across the Org Complete framework for error budget policies connecting reliability to velocity. ---", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "No specialized software required for reading the guides", "Code examples include setup instructions within each chapter", "Python 3.8+ recommended for running example scripts" ], "supported_versions": [ "Python 3.8+" ], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [ "error-budget-policy/\n+-- README.md # This file\n+-- LICENSE # MIT License\n+-- index.html # Interactive reader (free preview)\n+-- free-sample.zip # Free sample with Chapter 1\n+-- guide/\n +-- 01-*.md through 05-*.md" ], "included_files": [ "README.md", "guide/01-error-budget-fundamentals.md", "guide/02-budget-consumption-tracking.md", "guide/03-policy-enforcement-gates.md", "guide/04-governance-and-exceptions.md", "guide/05-maturity-model-and-continuous-improvemen.md" ], "file_count": 6, "download_size_bytes": 44717, "download_size_human": "43.7 KB", "product_url": "https://datanest-stores.com/sre-platform/#product-error-budget-policy", "preview_url": "https://datanest-stores.com/sre-platform/preview-error-budget-policy", "demo_url": "https://datanest-stores.com/sre-platform/demo-error-budget-policy/" }, { "product_id": "sre-platform:incident-response-runbook", "slug": "incident-response-runbook", "store": "sre-platform", "store_name": "SRE Platform Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "capacity planning", "chaos engineering", "cloud", "on-call", "postmortem", "runbooks", "sli/slo", "sre" ], "title": "Incident Response Runbook", "short_description": "Structured incident-response runbooks for production systems: severity matrices, on-call procedures, comms templates, and escalation paths.", "detailed_description": "Structured incident-response runbooks for production systems: severity matrices, on-call procedures, comms templates, and escalation paths.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "No specialized software required for reading the guides", "Code examples include setup instructions within each chapter", "Python 3.8+ recommended for running example scripts" ], "supported_versions": [ "Python 3.8+" ], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [ "incident-response-runbook/\n+-- README.md # This file\n+-- LICENSE # MIT License\n+-- index.html # Interactive reader (free preview)\n+-- free-sample.zip # Free sample with Chapter 1\n+-- guide/\n +-- 01-*.md through 05-*.md" ], "included_files": [ "README.md", "guide/01-incident-response-process-and-standards.md", "guide/02-service-degradation-runbooks.md", "guide/03-complete-outage-and-data-loss-runbooks.md", "guide/04-dependency-and-infrastructure-failure-ru.md", "guide/05-security,-capacity,-and-complex-incident.md" ], "file_count": 6, "download_size_bytes": 45157, "download_size_human": "44.1 KB", "product_url": "https://datanest-stores.com/sre-platform/#product-incident-response-runbook", "preview_url": "https://datanest-stores.com/sre-platform/preview-incident-response-runbook", "demo_url": "https://datanest-stores.com/sre-platform/demo-incident-response-runbook/" }, { "product_id": "sre-platform:on-call-scheduler", "slug": "on-call-scheduler", "store": "sre-platform", "store_name": "SRE Platform Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "capacity planning", "chaos engineering", "cloud", "on-call", "postmortem", "runbooks", "sli/slo", "sre" ], "title": "On-Call Scheduler", "short_description": "Build fair, sustainable on-call schedules with automation: rotation generation, coverage balancing, handoff checklists, and fatigue safeguards.", "detailed_description": "Build fair, sustainable on-call schedules with automation: rotation generation, coverage balancing, handoff checklists, and fatigue safeguards.", "price_usd": 29, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "No specialized software required for reading the guides", "Code examples include setup instructions within each chapter", "Python 3.8+ recommended for running example scripts" ], "supported_versions": [ "Python 3.8+" ], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [ "on-call-scheduler/\n+-- README.md # This file\n+-- LICENSE # MIT License\n+-- index.html # Interactive reader (free preview)\n+-- free-sample.zip # Free sample with Chapter 1\n+-- guide/\n +-- 01-*.md through 05-*.md" ], "included_files": [ "README.md", "guide/01-on-call-design-principles.md", "guide/02-rotation-algorithms-and-fairness.md", "guide/03-escalation-policies-and-override-handlin.md", "guide/04-pagerduty,-opsgenie,-and-grafana-integra.md", "guide/05-wellness,-burnout-prevention,-and-improv.md" ], "file_count": 6, "download_size_bytes": 44951, "download_size_human": "43.9 KB", "product_url": "https://datanest-stores.com/sre-platform/#product-on-call-scheduler", "preview_url": "https://datanest-stores.com/sre-platform/preview-on-call-scheduler", "demo_url": "https://datanest-stores.com/sre-platform/demo-on-call-scheduler/" }, { "product_id": "sre-platform:sli-metrics-definitions", "slug": "sli-metrics-definitions", "store": "sre-platform", "store_name": "SRE Platform Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "capacity planning", "chaos engineering", "cloud", "on-call", "postmortem", "runbooks", "sli/slo", "sre" ], "title": "SLI Metrics Definitions", "short_description": "50+ ready-to-use SLI definitions for every layer of the stack \u2014 availability, latency, throughput, correctness \u2014 with measurement queries.", "detailed_description": "50+ ready-to-use SLI definitions for every layer of the stack \u2014 availability, latency, throughput, correctness \u2014 with measurement queries.", "price_usd": 19, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "No specialized software required for reading the guides", "Code examples include setup instructions within each chapter", "Python 3.8+ recommended for running example scripts" ], "supported_versions": [ "Python 3.8+" ], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [ "sli-metrics-definitions/\n+-- README.md # This file\n+-- LICENSE # MIT License\n+-- index.html # Interactive reader (free preview)\n+-- free-sample.zip # Free sample with Chapter 1\n+-- guide/\n +-- 01-*.md through 05-*.md" ], "included_files": [ "README.md", "guide/01-sli-taxonomy-and-measurement.md", "guide/02-frontend-and-api-layer-slis.md", "guide/03-database-and-storage-layer-slis.md", "guide/04-infrastructure-and-pipeline-slis.md", "guide/05-business-process-and-composite-slis.md" ], "file_count": 6, "download_size_bytes": 45039, "download_size_human": "44.0 KB", "product_url": "https://datanest-stores.com/sre-platform/#product-sli-metrics-definitions", "preview_url": "https://datanest-stores.com/sre-platform/preview-sli-metrics-definitions", "demo_url": "https://datanest-stores.com/sre-platform/demo-sli-metrics-definitions/" }, { "product_id": "sre-platform:slo-framework", "slug": "slo-framework", "store": "sre-platform", "store_name": "SRE Platform Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "capacity planning", "chaos engineering", "cloud", "on-call", "postmortem", "runbooks", "sli/slo", "sre" ], "title": "SLO Framework", "short_description": "Design, implement, and manage SLOs across your platform: SLI selection, target setting, error budgets, alerting rules, and executive dashboards.", "detailed_description": "Design, implement, and manage SLOs across your platform: SLI selection, target setting, error budgets, alerting rules, and executive dashboards.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": "~30 min", "prerequisites": [ "No specialized software required for reading the guides", "Code examples include setup instructions within each chapter", "Python 3.8+ recommended for running example scripts" ], "supported_versions": [ "Python 3.8+" ], "supported_technologies": [], "compatibility_notes": null, "preview_snippets": [ "slo-framework/\n+-- README.md # This file\n+-- LICENSE # MIT License\n+-- index.html # Interactive reader (free preview)\n+-- free-sample.zip # Free sample with Chapter 1\n+-- guide/\n +-- 01-*.md through 05-*.md" ], "included_files": [ "README.md", "guide/01-slo-fundamentals-and-design-patterns.md", "guide/02-stakeholder-alignment.md", "guide/03-slo-implementation-and-tooling.md", "guide/04-error-budget-policies-and-governance.md", "guide/05-operational-reviews-and-continuous-impro.md" ], "file_count": 6, "download_size_bytes": 44646, "download_size_human": "43.6 KB", "product_url": "https://datanest-stores.com/sre-platform/#product-slo-framework", "preview_url": "https://datanest-stores.com/sre-platform/preview-slo-framework", "demo_url": "https://datanest-stores.com/sre-platform/demo-slo-framework/" }, { "product_id": "sre-platform:sli-slo-framework", "slug": "sli-slo-framework", "store": "sre-platform", "store_name": "SRE Platform Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "capacity planning", "chaos engineering", "cloud", "on-call", "postmortem", "runbooks", "sli/slo", "sre" ], "title": "SLI/SLO Framework", "short_description": "A Python toolkit implementing Google's multi-window multi-burn-rate methodology: SLI computation, SLO evaluation with rolling windows, real-time error-budget tracking, auto-generated Prometheus/Grafana/Alertmanager rules, a Grafana dashboard, an SLO catalog, and executive reporting.", "detailed_description": "A comprehensive Python toolkit for defining, measuring, and tracking Service Level Indicators (SLIs) and Service Level Objectives (SLOs). Implements Google's multi-window multi-burn-rate alerting methodology with error budget management. Includes: SLI Computation \u2014 Calculate availability, latency, throughput, and quality indicators from raw metric data; SLO Evaluation \u2014 Define targets with rolling compliance windows and automated status determination; Error Budget Tracking \u2014 Real-time budget consumption monitoring with velocity-based exhaustion prediction; Multi-Window Burn-Rate Alerting \u2014 Generate Prometheus and Grafana alert rules following Google SRE best practices; Executive Reporting \u2014 Markdown, JSON, and Slack-formatted compliance reports with trend analysis; Alert Rule Generation \u2014 Auto-generate Prometheus rules, Grafana provisioning JSON, and Alertmanager routing.", "price_usd": 39, "currency": "usd", "is_bundle": false, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [ "Python 3.10+", "Standard library only \u2014 no external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "from src.sli_metrics import SLIDefinition, SLIType\nfrom src.slo_calculator import SLODefinition\n\n# Define what to measure\nsli = SLIDefinition(\n name=\"api-availability\",\n sli_type=SLIType.AVAILABILITY,\n description=\"Fraction of requests that return non-5xx status\",\n good_event_filter=\"status_code < 500\",\n valid_event_filter=\"all API requests\",\n)\n\n# Define the target\nslo = SLODefinition(\n\u2026", "from src.sli_metrics import SLIComputer, MetricDataPoint\nfrom datetime import datetime, timedelta\n\ncomputer = SLIComputer()\nnow = datetime.utcnow()\n\n# Your metric data (from Prometheus, Datadog, CloudWatch, etc.)\ndata_points = [\n MetricDataPoint(timestamp=now - timedelta(minutes=i), value=1,\n labels={\"status_code\": \"200\" if i % 1000 != 0 else \"500\"})\n for i in range(10000)\n]\n\nresult = computer.compute_availability(\n\u2026" ], "included_files": [ "README.md", "configs/grafana_dashboard.json", "configs/slo_catalog.yaml", "examples/full_pipeline_demo.py", "guides/error_budget_policy.md", "src/__init__.py", "src/alert_rules.py", "src/burn_rate.py", "src/error_budget.py", "src/reporting.py", "src/sli_metrics.py", "src/slo_calculator.py", "tests/test_sli_slo.py" ], "file_count": 13, "download_size_bytes": 114316, "download_size_human": "111.6 KB", "product_url": "https://datanest-stores.com/sre-platform/#product-sli-slo-framework", "preview_url": "https://datanest-stores.com/sre-platform/preview-sli-slo-framework", "demo_url": "https://datanest-stores.com/sre-platform/demo-sli-slo-framework/" }, { "product_id": "sre-platform:sre-complete-bundle", "slug": "sre-complete-bundle", "store": "sre-platform", "store_name": "SRE Platform Pro", "domain": "cloud", "category": "Cloud, DevOps & SRE", "tags": [ "bundle", "capacity planning", "chaos engineering", "cloud", "on-call", "postmortem", "runbooks", "sli/slo", "sre" ], "title": "SRE Platform Bundle", "short_description": "All 12 SRE tools \u2014 $378 bought separately, yours for $89. Save $289 (76%): SLOs, runbooks, incident response, and chaos drills.", "detailed_description": "A comprehensive Python toolkit for defining, measuring, and tracking Service Level Indicators (SLIs) and Service Level Objectives (SLOs). Implements Google's multi-window multi-burn-rate alerting methodology with error budget management. Includes: SLI Computation \u2014 Calculate availability, latency, throughput, and quality indicators from raw metric data; SLO Evaluation \u2014 Define targets with rolling compliance windows and automated status determination; Error Budget Tracking \u2014 Real-time budget consumption monitoring with velocity-based exhaustion prediction; Multi-Window Burn-Rate Alerting \u2014 Generate Prometheus and Grafana alert rules following Google SRE best practices; Executive Reporting \u2014 Markdown, JSON, and Slack-formatted compliance reports with trend analysis; Alert Rule Generation \u2014 Auto-generate Prometheus rules, Grafana provisioning JSON, and Alertmanager routing.", "price_usd": 89, "currency": "usd", "is_bundle": true, "published": true, "license": "MIT licensed", "version": "1.0.0", "changelog_at": null, "last_verified_at": "2026-08-22", "verification_status": "verified", "agent_sellable": true, "install_time": null, "prerequisites": [ "Python 3.10+", "Standard library only \u2014 no external dependencies" ], "supported_versions": [ "Python 3.10+" ], "supported_technologies": [ "Python", "Terraform", "YAML" ], "compatibility_notes": null, "preview_snippets": [ "from src.sli_metrics import SLIDefinition, SLIType\nfrom src.slo_calculator import SLODefinition\n\n# Define what to measure\nsli = SLIDefinition(\n name=\"api-availability\",\n sli_type=SLIType.AVAILABILITY,\n description=\"Fraction of requests that return non-5xx status\",\n good_event_filter=\"status_code < 500\",\n valid_event_filter=\"all API requests\",\n)\n\n# Define the target\nslo = SLODefinition(\n\u2026", "from src.sli_metrics import SLIComputer, MetricDataPoint\nfrom datetime import datetime, timedelta\n\ncomputer = SLIComputer()\nnow = datetime.utcnow()\n\n# Your metric data (from Prometheus, Datadog, CloudWatch, etc.)\ndata_points = [\n MetricDataPoint(timestamp=now - timedelta(minutes=i), value=1,\n labels={\"status_code\": \"200\" if i % 1000 != 0 else \"500\"})\n for i in range(10000)\n]\n\nresult = computer.compute_availability(\n\u2026" ], "included_files": [ "products/capacity-planning-toolkit/README.md", "products/capacity-planning-toolkit/source/README.md", "products/capacity-planning-toolkit/source/configs/thresholds.yaml", "products/capacity-planning-toolkit/source/examples/sample_utilization.csv", "products/capacity-planning-toolkit/source/guide/01-capacity-planning-fundamentals.md", "products/capacity-planning-toolkit/source/guide/02-load-forecasting-methods.md", "products/capacity-planning-toolkit/source/guide/03-resource-utilization-tracking.md", "products/capacity-planning-toolkit/source/guide/04-scaling-decisions-and-cost-modeling.md", "products/capacity-planning-toolkit/source/guide/05-capacity-review-process.md", "products/capacity-planning-toolkit/source/guides/capacity_review_guide.md", "products/capacity-planning-toolkit/source/src/__init__.py", "products/capacity-planning-toolkit/source/src/cost_model.py", 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