Claude Cookbooks Skill

Comprehensive code examples and guides for building with Claude AI, sourced from the official Anthropic cookbooks repository.

When to Use This Skill

This skill should be triggered when:

Learning how to use Claude API Implementing Claude integrations Building applications with Claude Working with tool use and function calling Implementing multimodal features (vision, image analysis) Setting up RAG (Retrieval Augmented Generation) Integrating Claude with third-party services Building AI agents with Claude Optimizing prompts for Claude Implementing advanced patterns (caching, sub-agents, etc.) Quick Reference Basic API Usage import anthropic

client = anthropic.Anthropic(api_key="your-api-key")

Simple message

response = client.messages.create( model="claude-3-5-sonnet-20241022", max_tokens=1024, messages=[{ "role": "user", "content": "Hello, Claude!" }] )

Tool Use (Function Calling)

Define a tool

tools = [{ "name": "get_weather", "description": "Get current weather for a location", "input_schema": { "type": "object", "properties": { "location": {"type": "string", "description": "City name"} }, "required": ["location"] } }]

Use the tool

response = client.messages.create( model="claude-3-5-sonnet-20241022", max_tokens=1024, tools=tools, messages=[{"role": "user", "content": "What's the weather in San Francisco?"}] )

Vision (Image Analysis)

Analyze an image

response = client.messages.create( model="claude-3-5-sonnet-20241022", max_tokens=1024, messages=[{ "role": "user", "content": [ { "type": "image", "source": { "type": "base64", "media_type": "image/jpeg", "data": base64_image } }, {"type": "text", "text": "Describe this image"} ] }] )

Prompt Caching

Use prompt caching for efficiency

response = client.messages.create( model="claude-3-5-sonnet-20241022", max_tokens=1024, system=[{ "type": "text", "text": "Large system prompt here...", "cache_control": {"type": "ephemeral"} }], messages=[{"role": "user", "content": "Your question"}] )

Key Capabilities Covered 1. Classification Text classification techniques Sentiment analysis Content categorization Multi-label classification 2. Retrieval Augmented Generation (RAG) Vector database integration Semantic search Context retrieval Knowledge base queries 3. Summarization Document summarization Meeting notes Article condensing Multi-document synthesis 4. Text-to-SQL Natural language to SQL queries Database schema understanding Query optimization Result interpretation 5. Tool Use & Function Calling Tool definition and schema Parameter validation Multi-tool workflows Error handling 6. Multimodal Image analysis and OCR Chart/graph interpretation Visual question answering Image generation integration 7. Advanced Patterns Agent architectures Sub-agent delegation Prompt optimization Cost optimization with caching Repository Structure

The cookbooks are organized into these main categories:

capabilities/ - Core AI capabilities (classification, RAG, summarization, text-to-SQL) tool_use/ - Function calling and tool integration examples multimodal/ - Vision and image-related examples patterns/ - Advanced patterns like agents and workflows third_party/ - Integrations with external services (Pinecone, LlamaIndex, etc.) claude_agent_sdk/ - Agent SDK examples and templates misc/ - Additional utilities (PDF upload, JSON mode, evaluations, etc.) Reference Files

This skill includes comprehensive documentation in references/:

main_readme.md - Main repository overview capabilities.md - Core capabilities documentation tool_use.md - Tool use and function calling guides multimodal.md - Vision and multimodal capabilities third_party.md - Third-party integrations patterns.md - Advanced patterns and agents index.md - Complete reference index Common Use Cases Building a Customer Service Agent Define tools for CRM access, ticket creation, knowledge base search Use tool use API to handle function calls Implement conversation memory Add fallback mechanisms

See: references/tool_use.md#customer-service

Implementing RAG Create embeddings of your documents Store in vector database (Pinecone, etc.) Retrieve relevant context on query Augment Claude's response with context

See: references/capabilities.md#rag

Processing Documents with Vision Convert document to images or PDF Use vision API to extract content Structure the extracted data Validate and post-process

See: references/multimodal.md#vision

Building Multi-Agent Systems Define specialized agents for different tasks Implement routing logic Use sub-agents for delegation Aggregate results

See: references/patterns.md#agents

Best Practices API Usage Use appropriate model for task (Sonnet for balance, Haiku for speed, Opus for complex tasks) Implement retry logic with exponential backoff Handle rate limits gracefully Monitor token usage for cost optimization Prompt Engineering Be specific and clear in instructions Provide examples when needed Use system prompts for consistent behavior Structure outputs with JSON mode when needed Tool Use Define clear, specific tool schemas Validate inputs and outputs Handle errors gracefully Keep tool descriptions concise but informative Multimodal Use high-quality images (higher resolution = better results) Be specific about what to extract/analyze Respect size limits (5MB per image) Use appropriate image formats (JPEG, PNG, GIF, WebP) Performance Optimization Prompt Caching Cache large system prompts Cache frequently used context Monitor cache hit rates Balance caching vs. fresh content Cost Optimization Use Haiku for simple tasks Implement prompt caching for repeated context Set appropriate max_tokens Batch similar requests Latency Optimization Use streaming for long responses Minimize message history Optimize image sizes Use appropriate timeout values Resources Official Documentation Anthropic Developer Docs API Reference Anthropic Support Community Anthropic Discord GitHub Cookbooks Repo Learning Resources Claude API Fundamentals Course Prompt Engineering Guide Working with This Skill For Beginners

Start with references/main_readme.md and explore basic examples in references/capabilities.md

For Specific Features Tool use → references/tool_use.md Vision → references/multimodal.md RAG → references/capabilities.md#rag Agents → references/patterns.md#agents For Code Examples

Each reference file contains practical, copy-pasteable code examples

Examples Available

The cookbook includes 50+ practical examples including:

Customer service chatbot with tool use RAG with Pinecone vector database Document summarization Image analysis and OCR Chart/graph interpretation Natural language to SQL Content moderation filter Automated evaluations Multi-agent systems Prompt caching optimization Notes All examples use official Anthropic Python SDK Code is production-ready with error handling Examples follow current API best practices Regular updates from Anthropic team Community contributions welcome Skill Source

This skill was created from the official Anthropic Claude Cookbooks repository: https://github.com/anthropics/claude-cookbooks

Repository cloned and processed on: 2025-10-29