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

Community

Learning Resources

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

Loading…

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

Community

Learning Resources

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

claude-cookbooks

Claude Cookbooks Skill

When to Use This Skill

Quick Reference

Basic API Usage

Tool Use (Function Calling)

Vision (Image Analysis)

Prompt Caching

Key Capabilities Covered

1. Classification

2. Retrieval Augmented Generation (RAG)

3. Summarization

4. Text-to-SQL

5. Tool Use & Function Calling

6. Multimodal

7. Advanced Patterns

Repository Structure

Reference Files

Common Use Cases

Building a Customer Service Agent

Implementing RAG

Processing Documents with Vision

Building Multi-Agent Systems

Best Practices

API Usage

Prompt Engineering

Tool Use

Multimodal

Performance Optimization

Prompt Caching

Cost Optimization

Latency Optimization

Resources

Official Documentation

Community

Learning Resources

Working with This Skill

For Beginners

For Specific Features

For Code Examples

Examples Available

Notes

Skill Source

Related Skills

flow

verify

feature-flags

flags

Claude Cookbooks Skill

When to Use This Skill

Quick Reference

Basic API Usage

Tool Use (Function Calling)

Vision (Image Analysis)

Prompt Caching

Key Capabilities Covered

1. Classification

2. Retrieval Augmented Generation (RAG)

3. Summarization

4. Text-to-SQL

5. Tool Use & Function Calling

6. Multimodal

7. Advanced Patterns

Repository Structure

Reference Files

Common Use Cases

Building a Customer Service Agent

Implementing RAG

Processing Documents with Vision

Building Multi-Agent Systems

Best Practices

API Usage

Prompt Engineering

Tool Use

Multimodal

Performance Optimization

Prompt Caching

Cost Optimization

Latency Optimization

Resources