{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"anthropic-cookbook","slug":"anthropic-cookbook","name":"Anthropic Cookbook","type":"repo","url":"https://github.com/anthropics/anthropic-cookbook","page_url":"https://unfragile.ai/anthropic-cookbook","categories":["prompt-engineering","rag-knowledge","deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"anthropic-cookbook__cap_0","uri":"capability://code.generation.editing.executable.notebook.based.api.demonstration","name":"executable-notebook-based-api-demonstration","description":"Provides production-ready Jupyter notebooks (.ipynb files) that demonstrate Claude API capabilities through runnable code examples. Each notebook is structured as a self-contained, copy-paste-ready implementation pattern for specific features like tool use, RAG, or multimodal processing. The notebooks serve as both documentation and functional code templates that developers can immediately adapt to their own projects.","intents":["I need a working code example for implementing tool calling with Claude","Show me how to set up a RAG pipeline with vector databases","I want to see a production pattern for prompt caching before building it myself","How do I integrate Claude with my existing LLM agent framework?"],"best_for":["developers building Claude-based applications who learn by example","teams evaluating Claude capabilities before architectural decisions","engineers migrating from other LLM providers to Anthropic"],"limitations":["Notebooks require Jupyter environment or compatible notebook runner","Examples are point-in-time snapshots and may lag behind latest API changes","No interactive feedback loop — examples are static code, not adaptive tutorials","Requires manual adaptation for production use; not a framework or library"],"requires":["Python 3.9+","Jupyter or JupyterLab installed","Anthropic API key (ANTHROPIC_API_KEY environment variable)","Dependencies specified in pyproject.toml (anthropic SDK, optional integrations)"],"input_types":["Python code (notebook cells)","API credentials","Optional: external data files, vector database credentials"],"output_types":["Executable notebook output (text, structured data, API responses)","Copy-paste-ready code snippets","Integration patterns and architectural examples"],"categories":["code-generation-editing","documentation-as-code"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-cookbook__cap_1","uri":"capability://data.processing.analysis.machine.readable.capability.registry.system","name":"machine-readable-capability-registry-system","description":"Implements a YAML-based registry (registry.yaml) that catalogs all cookbook notebooks with structured metadata including category, tags, author, and description. This enables programmatic discovery, automated validation workflows, and machine-readable capability mapping without requiring manual documentation updates. The registry acts as a single source of truth for content organization and enables tooling to validate notebook compliance.","intents":["I need to programmatically discover which notebooks cover a specific Claude capability","Build tooling that validates all cookbook examples are still functional","Generate a capability matrix showing which features are documented","Automate the addition of new notebooks with consistent metadata"],"best_for":["platform teams building discovery systems for code examples","CI/CD pipelines that need to validate notebook quality at scale","documentation generators that need structured capability metadata","teams building internal LLM application catalogs"],"limitations":["Registry is manually maintained — requires human updates when notebooks change","YAML schema validation is basic; no complex constraint checking","No versioning system for tracking capability evolution across API releases","Registry discovery is read-only; no built-in search or filtering API"],"requires":["YAML parser (built into most languages)","Access to registry.yaml file in repository","Understanding of registry schema (documented in .claude/commands/add-registry.md)"],"input_types":["YAML configuration (registry.yaml)","Notebook metadata (title, description, category, tags)","Author information (authors.yaml)"],"output_types":["Structured capability catalog (JSON/YAML)","Validation reports (notebook compliance)","Discovery results (filtered by category/tag)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-cookbook__cap_10","uri":"capability://automation.workflow.automated.notebook.validation.and.quality.assurance","name":"automated-notebook-validation-and-quality-assurance","description":"Implements automated validation infrastructure (scripts/validate_notebooks.py) that ensures all cookbook notebooks remain functional and compliant with standards. Validation checks include notebook structure, API usage correctness, metadata consistency, and execution tests. Integrates with CI/CD pipeline to catch breaking changes and maintain quality across the cookbook collection.","intents":["How do I ensure all my example notebooks stay functional as APIs evolve?","I need to validate that notebooks follow consistent patterns and standards","Show me how to automate quality checks for a large collection of examples","What's the pattern for detecting breaking changes in notebook examples?"],"best_for":["teams maintaining large collections of example code","organizations needing to ensure documentation stays current","CI/CD pipelines that validate code examples as part of release process"],"limitations":["Validation is static analysis only; doesn't catch runtime errors in all cases","Requires API credentials to run full validation; can't validate in all CI environments","Validation rules are cookbook-specific; not generalizable to other projects","No automatic fixing; validation only reports issues, requires manual remediation","Validation latency grows with notebook count; large collections require optimization"],"requires":["Python 3.9+","nbformat library for notebook parsing","Anthropic API key for execution validation","CI/CD system (GitHub Actions, etc.) for automation"],"input_types":["Jupyter notebooks (.ipynb files)","Registry metadata (registry.yaml)","Validation rules and standards"],"output_types":["Validation reports (pass/fail per notebook)","Error logs and failure details","Compliance metrics"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-cookbook__cap_11","uri":"capability://automation.workflow.contributor.workflow.and.registry.management","name":"contributor-workflow-and-registry-management","description":"Provides structured contribution guidelines and tooling for adding new notebooks to the cookbook. Includes Claude Code slash commands (.claude/commands/add-registry.md) that semi-automate registry entry creation, GitHub pull request templates that enforce metadata requirements, and contributor documentation (CONTRIBUTING.md). Enables consistent, high-quality contributions without manual registry editing.","intents":["I want to contribute a new example notebook to the cookbook","How do I add my notebook to the registry without manual YAML editing?","Show me the standards and patterns I should follow for new examples","What metadata is required for a cookbook contribution?"],"best_for":["community contributors submitting new cookbook examples","Anthropic team members adding official examples","teams maintaining internal example repositories with similar workflows"],"limitations":["Slash commands require Claude Code integration; not available in standard GitHub UI","PR templates are GitHub-specific; don't apply to other version control systems","Contribution guidelines are text-based; no automated enforcement of code style","Registry updates are manual even with slash commands; requires human review","No built-in conflict resolution for simultaneous contributions"],"requires":["GitHub account with fork/PR permissions","Jupyter notebook with working example code","Understanding of registry.yaml schema","Claude Code integration (for slash command automation)"],"input_types":["Jupyter notebook (.ipynb file)","Notebook metadata (title, description, category, tags)","Author information"],"output_types":["Registry entry (YAML)","Pull request with metadata validation","Contribution checklist"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-cookbook__cap_12","uri":"capability://memory.knowledge.advanced.rag.with.llamaindex.integration","name":"advanced-rag-with-llamaindex-integration","description":"Demonstrates advanced RAG patterns using LlamaIndex as an abstraction layer over vector databases and retrieval strategies. Notebooks show how to implement hybrid search (combining keyword and semantic search), multi-hop retrieval (chaining multiple retrieval steps), reranking, and query expansion. Covers integration with multiple vector databases (Pinecone, Weaviate, Chroma) without rewriting core logic.","intents":["How do I implement advanced retrieval strategies beyond simple semantic search?","Show me how to use LlamaIndex to abstract over different vector databases","I need to implement multi-hop retrieval for complex question answering","What's the pattern for reranking retrieved documents to improve quality?"],"best_for":["teams building sophisticated knowledge-base systems","applications requiring multi-hop reasoning over documents","organizations evaluating different vector databases without rewriting code"],"limitations":["LlamaIndex abstraction adds complexity; direct vector DB integration may be simpler","Advanced retrieval strategies increase latency; no guidance on latency budgets","Reranking requires additional API calls; cost increases with retrieval complexity","Multi-hop retrieval can amplify retrieval errors; no built-in error recovery","LlamaIndex version compatibility issues; examples may lag behind latest releases"],"requires":["LlamaIndex library (llama-index>=0.9.0)","Vector database (Pinecone, Weaviate, Chroma, or compatible)","Embedding model API key","Reranking model (optional, for advanced patterns)","Python 3.9+"],"input_types":["Documents (text, PDF, markdown)","User queries","Vector database credentials","Reranking model configuration (optional)"],"output_types":["Retrieved document chunks (ranked)","Multi-hop retrieval results","Reranked results","Claude response with augmented context"],"categories":["memory-knowledge","search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-cookbook__cap_13","uri":"capability://image.visual.voice.and.audio.processing.with.multimodal.input","name":"voice-and-audio-processing-with-multimodal-input","description":"Provides examples for processing audio and voice input with Claude, including audio transcription, voice analysis, and audio-to-text workflows. Notebooks demonstrate how to encode audio files, send them to Claude, and extract structured information from audio content. Covers use cases like meeting transcription, voice command processing, and audio content analysis.","intents":["How do I send audio files to Claude for analysis?","Show me how to transcribe and analyze voice recordings","I need to extract structured data from audio content","What's the pattern for processing multiple audio files in batch?"],"best_for":["applications requiring audio transcription and analysis","voice-enabled interfaces that need semantic understanding","teams processing meeting recordings or voice notes"],"limitations":["Audio processing requires pre-transcription or encoding; no native audio input","Audio quality affects analysis accuracy; no guidance on audio preprocessing","Large audio files require chunking; no built-in audio segmentation","Batch audio processing requires sequential API calls; no bulk audio API","Audio analysis is text-based after transcription; loses audio-specific features"],"requires":["Anthropic Python SDK (anthropic>=0.7.0)","Audio files in supported formats (WAV, MP3, etc.)","Audio encoding/transcription tool (external)","Claude 3 Sonnet or later"],"input_types":["Audio files (WAV, MP3, etc.)","Transcribed audio text","Audio metadata (duration, format)"],"output_types":["Transcription results","Audio analysis and interpretation","Extracted structured data from audio"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-cookbook__cap_2","uri":"capability://tool.use.integration.tool.use.pattern.implementation.with.function.schemas","name":"tool-use-pattern-implementation-with-function-schemas","description":"Provides executable examples demonstrating Claude's tool-calling capability through function schema definitions, parameter binding, and multi-turn interaction patterns. Notebooks show how to define tool schemas (JSON Schema format), handle tool calls in API responses, execute tools, and feed results back to Claude for iterative problem-solving. Covers both simple single-tool scenarios and complex multi-tool orchestration patterns.","intents":["Show me how to define function schemas for Claude to call my APIs","I need to implement a multi-turn agent that uses tools to solve problems","How do I handle tool execution errors and feed them back to Claude?","What's the pattern for orchestrating multiple tools in sequence?"],"best_for":["developers building agentic systems that need Claude to invoke external functions","teams implementing tool-augmented LLM applications","engineers designing API-first architectures where Claude acts as an orchestrator"],"limitations":["Examples show synchronous tool execution; async patterns require custom implementation","No built-in error recovery or retry logic — developers must implement resilience","Tool schema validation is client-side only; no server-side enforcement","Examples assume stateless tool calls; stateful tool interactions require custom context management"],"requires":["Anthropic Python SDK (anthropic>=0.7.0)","Understanding of JSON Schema for function definitions","API endpoints or callable functions to expose as tools","Python 3.9+"],"input_types":["Tool schema definitions (JSON Schema)","User queries or prompts","Tool execution results (structured data or text)"],"output_types":["Tool call requests (function name + parameters)","Final Claude response after tool execution","Execution logs and interaction traces"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-cookbook__cap_3","uri":"capability://memory.knowledge.retrieval.augmented.generation.pipeline.templates","name":"retrieval-augmented-generation-pipeline-templates","description":"Provides end-to-end RAG implementation patterns including document ingestion, vector embedding, semantic search, and context injection into Claude prompts. Notebooks demonstrate integration with vector databases (Pinecone, Weaviate, etc.) via LlamaIndex abstraction layer, showing how to build retrieval systems that augment Claude's knowledge with external documents. Covers both basic RAG (simple retrieval + prompt injection) and advanced patterns (hybrid search, reranking, multi-hop retrieval).","intents":["I need to build a system where Claude answers questions about my proprietary documents","Show me how to integrate Claude with a vector database for semantic search","How do I implement RAG with LlamaIndex and multiple document sources?","What's the pattern for handling large document collections efficiently?"],"best_for":["teams building knowledge-base Q&A systems augmented with Claude","enterprises implementing document-aware LLM applications","developers migrating from keyword search to semantic search with LLMs"],"limitations":["Vector database setup and maintenance is out-of-scope; examples assume pre-configured databases","Embedding quality depends on external embedding models; no guidance on model selection","No built-in handling of document staleness or cache invalidation","Context window limits require careful chunk sizing; examples don't auto-optimize for token budgets","LlamaIndex abstraction adds complexity; direct vector DB integration may be simpler for some use cases"],"requires":["Vector database (Pinecone, Weaviate, Chroma, or compatible)","LlamaIndex library (llama-index>=0.9.0) or direct vector DB SDK","Embedding model API key (OpenAI, Anthropic, or local)","Document corpus in text/PDF format","Python 3.9+"],"input_types":["Documents (PDF, text, markdown)","User queries (natural language)","Vector database credentials"],"output_types":["Retrieved document chunks (ranked by relevance)","Claude response augmented with retrieved context","Retrieval metrics (relevance scores, chunk selection)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-cookbook__cap_4","uri":"capability://automation.workflow.prompt.caching.optimization.patterns","name":"prompt-caching-optimization-patterns","description":"Provides examples demonstrating Claude's prompt caching feature, which reduces latency and costs for repeated requests with large static context. Notebooks show how to structure prompts to maximize cache hits (placing cacheable content in system prompts or message prefixes), measure cache effectiveness, and calculate cost savings. Covers use cases like multi-turn conversations with large system prompts, batch processing with shared context, and document analysis workflows.","intents":["How do I reduce latency and costs for repeated API calls with the same large context?","Show me how to structure prompts to maximize cache hit rates","I need to measure the cost savings from prompt caching in my application","What's the pattern for caching large system prompts across multiple requests?"],"best_for":["teams with high-volume Claude API usage seeking cost optimization","applications with large static context (system prompts, knowledge bases) reused across requests","developers building multi-turn agents where context is stable across turns"],"limitations":["Cache hits only occur when prompt prefix matches exactly; dynamic content breaks caching","Minimum cache size is 1024 tokens; small prompts don't benefit from caching","Cache TTL is 5 minutes; not suitable for long-lived session caching","Cache effectiveness depends on request patterns; unpredictable query patterns may not benefit","Requires Claude 3.5 Sonnet or later; not available on older models"],"requires":["Anthropic Python SDK (anthropic>=0.7.0)","Claude 3.5 Sonnet or later model","Prompts with >1024 tokens of static content","API key with prompt caching enabled"],"input_types":["System prompts (static context)","User queries (dynamic content)","Large documents or context blocks"],"output_types":["API responses with cache usage metrics (cache_creation_input_tokens, cache_read_input_tokens)","Cost comparison reports (cached vs non-cached)","Cache hit rate analysis"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-cookbook__cap_5","uri":"capability://image.visual.multimodal.vision.processing.with.image.analysis","name":"multimodal-vision-processing-with-image-analysis","description":"Provides examples for Claude's vision capabilities, including image analysis, OCR, visual question answering, and image-based document processing. Notebooks demonstrate how to encode images (base64 or URLs), construct vision prompts with image context, and extract structured information from visual content. Covers use cases like document scanning, diagram interpretation, and visual data extraction.","intents":["How do I send images to Claude and get structured analysis results?","I need to extract text and data from scanned documents using Claude's vision","Show me how to build a visual Q&A system that analyzes images","What's the pattern for processing multiple images in a single request?"],"best_for":["developers building document processing applications","teams implementing visual data extraction pipelines","applications requiring OCR or diagram interpretation"],"limitations":["Image size limits apply (max 20MB per image); large images require preprocessing","Vision performance varies by image quality and complexity; no guarantees on OCR accuracy","No built-in image preprocessing (rotation, enhancement); requires external tools","Structured output from images requires careful prompt engineering; no native vision-to-JSON","Batch image processing requires sequential API calls; no bulk vision API"],"requires":["Anthropic Python SDK (anthropic>=0.7.0)","Claude 3 Sonnet or later (vision-capable model)","Images in JPEG, PNG, GIF, or WebP format","Base64 encoding or publicly accessible image URLs"],"input_types":["Images (JPEG, PNG, GIF, WebP)","Image URLs or base64-encoded image data","Text prompts describing analysis task"],"output_types":["Text descriptions of image content","Extracted structured data (tables, lists)","OCR results","Visual analysis and interpretation"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-cookbook__cap_6","uri":"capability://automation.workflow.batch.processing.api.for.cost.optimization","name":"batch-processing-api-for-cost-optimization","description":"Provides examples for Anthropic's Batch API, which enables asynchronous, cost-reduced processing of multiple requests. Notebooks demonstrate how to structure batch requests, submit them for processing, poll for completion, and retrieve results. Shows cost savings calculations and use cases like bulk document analysis, large-scale content generation, and overnight processing jobs.","intents":["How do I process thousands of API requests at reduced cost?","Show me how to structure batch requests for the Batch API","I need to process documents overnight and retrieve results in the morning","What's the cost savings from using batch processing vs real-time API?"],"best_for":["teams with high-volume, non-time-sensitive Claude API usage","batch processing workflows (document analysis, content generation at scale)","cost-sensitive applications that can tolerate 24-hour latency"],"limitations":["Batch API has 24-hour processing window; not suitable for real-time applications","Minimum batch size is 10,000 requests; small batches don't benefit from cost reduction","No streaming responses in batch mode; full response buffering required","Error handling is batch-level only; individual request failures don't stop batch","Requires careful request structuring; malformed requests fail silently"],"requires":["Anthropic Python SDK (anthropic>=0.7.0)","Batch API access enabled on account","Minimum 10,000 requests to process (for cost efficiency)","Ability to wait 24 hours for results"],"input_types":["Batch request objects (JSON Lines format)","Custom request IDs for result tracking","Multiple prompts or documents for processing"],"output_types":["Batch job ID for tracking","Batch results (JSON Lines format)","Cost comparison metrics (batch vs standard pricing)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-cookbook__cap_7","uri":"capability://safety.moderation.content.moderation.and.safety.filtering","name":"content-moderation-and-safety-filtering","description":"Provides examples for implementing content moderation using Claude, including classification of harmful content, safety filtering, and policy enforcement. Notebooks demonstrate how to use Claude as a moderation classifier, structure moderation prompts, and integrate moderation into application workflows. Covers use cases like user-generated content filtering, prompt injection detection, and safety policy enforcement.","intents":["How do I use Claude to moderate user-generated content?","Show me how to detect and filter harmful prompts before they reach Claude","I need to classify content against my safety policies","What's the pattern for integrating moderation into my application pipeline?"],"best_for":["platforms with user-generated content requiring moderation","applications needing to enforce custom safety policies","teams implementing prompt injection detection"],"limitations":["Moderation is probabilistic; no 100% accuracy guarantee","Requires careful prompt engineering to define moderation criteria","Adds latency to request pipeline; moderation calls are synchronous","No built-in appeal or human review workflow","Moderation decisions are not explainable; no reasoning provided"],"requires":["Anthropic Python SDK (anthropic>=0.7.0)","Clear definition of moderation policies","Content to moderate (text or structured data)"],"input_types":["User-generated content (text)","Prompts to analyze","Policy definitions"],"output_types":["Moderation classification (safe/unsafe)","Category of violation (if unsafe)","Confidence scores"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-cookbook__cap_8","uri":"capability://data.processing.analysis.embeddings.generation.and.semantic.search","name":"embeddings-generation-and-semantic-search","description":"Provides examples for generating embeddings using Anthropic's embedding models and implementing semantic search. Notebooks demonstrate how to embed documents and queries, compute similarity scores, and build search systems. Shows integration with vector databases and includes patterns for chunking documents, managing embedding costs, and handling large-scale embedding workloads.","intents":["How do I generate embeddings for my documents using Anthropic's models?","Show me how to implement semantic search with embeddings","I need to find similar documents in my corpus efficiently","What's the pattern for embedding large document collections?"],"best_for":["teams building semantic search systems","applications requiring document similarity matching","developers implementing recommendation systems"],"limitations":["Embedding API has rate limits; large-scale embedding requires batching and throttling","Embeddings are model-specific; changing models requires re-embedding entire corpus","No built-in similarity threshold guidance; requires empirical tuning","Embedding quality depends on document length and content type; no auto-optimization","Batch embedding API has different limits than real-time API"],"requires":["Anthropic Python SDK (anthropic>=0.7.0)","API key with embeddings access","Documents or text to embed","Vector database for storing embeddings (optional but recommended)"],"input_types":["Text documents or chunks","Query text","Batch of documents for embedding"],"output_types":["Embedding vectors (1024-dimensional)","Similarity scores","Ranked search results"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-cookbook__cap_9","uri":"capability://memory.knowledge.multi.turn.conversation.context.management","name":"multi-turn-conversation-context-management","description":"Provides patterns for managing conversation history and context in multi-turn interactions with Claude. Notebooks demonstrate how to structure message arrays, manage token budgets across turns, implement conversation memory, and handle context window limits. Covers use cases like chatbots, interactive agents, and stateful dialogue systems.","intents":["How do I build a chatbot that maintains conversation history?","Show me how to manage context across multiple turns without exceeding token limits","I need to implement conversation memory with automatic summarization","What's the pattern for handling long conversations efficiently?"],"best_for":["developers building conversational AI applications","teams implementing stateful dialogue systems","applications requiring multi-turn reasoning"],"limitations":["Token budgets require careful management; no automatic context pruning","Conversation history grows linearly with turns; long conversations become expensive","No built-in conversation summarization; requires custom implementation","Message ordering is strict; out-of-order messages cause errors","No built-in conversation persistence; requires external storage"],"requires":["Anthropic Python SDK (anthropic>=0.7.0)","Understanding of Claude's message format (role/content pairs)","Token counting library for budget management"],"input_types":["User messages (text)","System prompts","Previous conversation history"],"output_types":["Assistant responses (text)","Updated conversation history","Token usage metrics"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"anthropic-cookbook__headline","uri":"capability://tool.use.integration.practical.code.examples.for.claude.api.development","name":"practical code examples for claude api development","description":"The Anthropic Cookbook is a comprehensive collection of executable Jupyter notebooks designed for developers building applications with the Claude API, featuring practical examples and templates for various capabilities like RAG and tool integration.","intents":["best cookbook for Claude API","Jupyter notebooks for Claude development","examples of RAG with Claude","how to use Claude API effectively","Claude API integration templates"],"best_for":["developers looking to implement Claude API"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Jupyter or JupyterLab installed","Anthropic API key (ANTHROPIC_API_KEY environment variable)","Dependencies specified in pyproject.toml (anthropic SDK, optional integrations)","YAML parser (built into most languages)","Access to registry.yaml file in repository","Understanding of registry schema (documented in .claude/commands/add-registry.md)","nbformat library for notebook parsing","Anthropic API key for execution validation","CI/CD system (GitHub Actions, etc.) for automation"],"failure_modes":["Notebooks require Jupyter environment or compatible notebook runner","Examples are point-in-time snapshots and may lag behind latest API changes","No interactive feedback loop — examples are static code, not adaptive tutorials","Requires manual adaptation for production use; not a framework or library","Registry is manually maintained — requires human updates when notebooks change","YAML schema validation is basic; no complex constraint checking","No versioning system for tracking capability evolution across API releases","Registry discovery is read-only; no built-in search or filtering API","Validation is static analysis only; doesn't catch runtime errors in all cases","Requires API credentials to run full validation; can't validate in all CI environments","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.6,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:02.370Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=anthropic-cookbook","compare_url":"https://unfragile.ai/compare?artifact=anthropic-cookbook"}},"signature":"q8w0yZNUCWJ2c5J+33OfPcoZ0BpLdqajOmQMX9SXxs42Pr45dELh0cxl2clexcDwiHDGyNKgCnT+z1jM7rCLBw==","signedAt":"2026-06-21T12:03:27.920Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/anthropic-cookbook","artifact":"https://unfragile.ai/anthropic-cookbook","verify":"https://unfragile.ai/api/v1/verify?slug=anthropic-cookbook","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}