315 lines
8.5 KiB
Markdown
315 lines
8.5 KiB
Markdown
TRANSLATED CONTENT:
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---
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name: claude-cookbooks
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description: Claude AI cookbooks - code examples, tutorials, and best practices for using Claude API. Use when learning Claude API integration, building Claude-powered applications, or exploring Claude capabilities.
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---
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# Claude Cookbooks Skill
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Comprehensive code examples and guides for building with Claude AI, sourced from the official Anthropic cookbooks repository.
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## When to Use This Skill
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This skill should be triggered when:
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- Learning how to use Claude API
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- Implementing Claude integrations
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- Building applications with Claude
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- Working with tool use and function calling
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- Implementing multimodal features (vision, image analysis)
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- Setting up RAG (Retrieval Augmented Generation)
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- Integrating Claude with third-party services
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- Building AI agents with Claude
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- Optimizing prompts for Claude
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- Implementing advanced patterns (caching, sub-agents, etc.)
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## Quick Reference
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### Basic API Usage
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```python
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import anthropic
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client = anthropic.Anthropic(api_key="your-api-key")
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# Simple message
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response = client.messages.create(
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model="claude-3-5-sonnet-20241022",
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max_tokens=1024,
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messages=[{
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"role": "user",
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"content": "Hello, Claude!"
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}]
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)
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```
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### Tool Use (Function Calling)
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```python
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# Define a tool
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tools = [{
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"name": "get_weather",
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"description": "Get current weather for a location",
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"input_schema": {
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"type": "object",
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"properties": {
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"location": {"type": "string", "description": "City name"}
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},
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"required": ["location"]
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}
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}]
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# Use the tool
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response = client.messages.create(
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model="claude-3-5-sonnet-20241022",
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max_tokens=1024,
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tools=tools,
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messages=[{"role": "user", "content": "What's the weather in San Francisco?"}]
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)
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```
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### Vision (Image Analysis)
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```python
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# Analyze an image
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response = client.messages.create(
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model="claude-3-5-sonnet-20241022",
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max_tokens=1024,
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messages=[{
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"role": "user",
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"content": [
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{
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"type": "image",
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"source": {
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"type": "base64",
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"media_type": "image/jpeg",
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"data": base64_image
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}
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},
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{"type": "text", "text": "Describe this image"}
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]
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}]
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)
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```
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### Prompt Caching
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```python
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# Use prompt caching for efficiency
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response = client.messages.create(
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model="claude-3-5-sonnet-20241022",
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max_tokens=1024,
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system=[{
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"type": "text",
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"text": "Large system prompt here...",
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"cache_control": {"type": "ephemeral"}
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}],
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messages=[{"role": "user", "content": "Your question"}]
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)
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```
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## Key Capabilities Covered
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### 1. Classification
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- Text classification techniques
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- Sentiment analysis
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- Content categorization
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- Multi-label classification
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### 2. Retrieval Augmented Generation (RAG)
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- Vector database integration
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- Semantic search
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- Context retrieval
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- Knowledge base queries
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### 3. Summarization
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- Document summarization
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- Meeting notes
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- Article condensing
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- Multi-document synthesis
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### 4. Text-to-SQL
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- Natural language to SQL queries
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- Database schema understanding
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- Query optimization
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- Result interpretation
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### 5. Tool Use & Function Calling
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- Tool definition and schema
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- Parameter validation
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- Multi-tool workflows
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- Error handling
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### 6. Multimodal
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- Image analysis and OCR
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- Chart/graph interpretation
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- Visual question answering
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- Image generation integration
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### 7. Advanced Patterns
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- Agent architectures
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- Sub-agent delegation
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- Prompt optimization
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- Cost optimization with caching
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## Repository Structure
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The cookbooks are organized into these main categories:
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- **capabilities/** - Core AI capabilities (classification, RAG, summarization, text-to-SQL)
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- **tool_use/** - Function calling and tool integration examples
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- **multimodal/** - Vision and image-related examples
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- **patterns/** - Advanced patterns like agents and workflows
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- **third_party/** - Integrations with external services (Pinecone, LlamaIndex, etc.)
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- **claude_agent_sdk/** - Agent SDK examples and templates
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- **misc/** - Additional utilities (PDF upload, JSON mode, evaluations, etc.)
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## Reference Files
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This skill includes comprehensive documentation in `references/`:
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- **main_readme.md** - Main repository overview
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- **capabilities.md** - Core capabilities documentation
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- **tool_use.md** - Tool use and function calling guides
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- **multimodal.md** - Vision and multimodal capabilities
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- **third_party.md** - Third-party integrations
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- **patterns.md** - Advanced patterns and agents
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- **index.md** - Complete reference index
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## Common Use Cases
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### Building a Customer Service Agent
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1. Define tools for CRM access, ticket creation, knowledge base search
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2. Use tool use API to handle function calls
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3. Implement conversation memory
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4. Add fallback mechanisms
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See: `references/tool_use.md#customer-service`
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### Implementing RAG
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1. Create embeddings of your documents
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2. Store in vector database (Pinecone, etc.)
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3. Retrieve relevant context on query
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4. Augment Claude's response with context
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See: `references/capabilities.md#rag`
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### Processing Documents with Vision
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1. Convert document to images or PDF
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2. Use vision API to extract content
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3. Structure the extracted data
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4. Validate and post-process
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See: `references/multimodal.md#vision`
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### Building Multi-Agent Systems
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1. Define specialized agents for different tasks
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2. Implement routing logic
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3. Use sub-agents for delegation
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4. Aggregate results
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See: `references/patterns.md#agents`
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## Best Practices
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### API Usage
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- Use appropriate model for task (Sonnet for balance, Haiku for speed, Opus for complex tasks)
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- Implement retry logic with exponential backoff
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- Handle rate limits gracefully
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- Monitor token usage for cost optimization
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### Prompt Engineering
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- Be specific and clear in instructions
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- Provide examples when needed
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- Use system prompts for consistent behavior
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- Structure outputs with JSON mode when needed
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### Tool Use
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- Define clear, specific tool schemas
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- Validate inputs and outputs
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- Handle errors gracefully
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- Keep tool descriptions concise but informative
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### Multimodal
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- Use high-quality images (higher resolution = better results)
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- Be specific about what to extract/analyze
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- Respect size limits (5MB per image)
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- Use appropriate image formats (JPEG, PNG, GIF, WebP)
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## Performance Optimization
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### Prompt Caching
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- Cache large system prompts
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- Cache frequently used context
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- Monitor cache hit rates
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- Balance caching vs. fresh content
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### Cost Optimization
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- Use Haiku for simple tasks
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- Implement prompt caching for repeated context
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- Set appropriate max_tokens
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- Batch similar requests
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### Latency Optimization
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- Use streaming for long responses
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- Minimize message history
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- Optimize image sizes
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- Use appropriate timeout values
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## Resources
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### Official Documentation
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- [Anthropic Developer Docs](https://docs.claude.com)
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- [API Reference](https://docs.claude.com/claude/reference)
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- [Anthropic Support](https://support.anthropic.com)
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### Community
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- [Anthropic Discord](https://www.anthropic.com/discord)
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- [GitHub Cookbooks Repo](https://github.com/anthropics/claude-cookbooks)
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### Learning Resources
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- [Claude API Fundamentals Course](https://github.com/anthropics/courses/tree/master/anthropic_api_fundamentals)
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- [Prompt Engineering Guide](https://docs.claude.com/claude/docs/guide-to-anthropics-prompt-engineering-resources)
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## Working with This Skill
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### For Beginners
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Start with `references/main_readme.md` and explore basic examples in `references/capabilities.md`
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### For Specific Features
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- Tool use → `references/tool_use.md`
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- Vision → `references/multimodal.md`
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- RAG → `references/capabilities.md#rag`
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- Agents → `references/patterns.md#agents`
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### For Code Examples
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Each reference file contains practical, copy-pasteable code examples
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## Examples Available
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The cookbook includes 50+ practical examples including:
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- Customer service chatbot with tool use
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- RAG with Pinecone vector database
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- Document summarization
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- Image analysis and OCR
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- Chart/graph interpretation
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- Natural language to SQL
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- Content moderation filter
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- Automated evaluations
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- Multi-agent systems
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- Prompt caching optimization
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## Notes
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- All examples use official Anthropic Python SDK
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- Code is production-ready with error handling
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- Examples follow current API best practices
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- Regular updates from Anthropic team
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- Community contributions welcome
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## Skill Source
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This skill was created from the official Anthropic Claude Cookbooks repository:
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https://github.com/anthropics/claude-cookbooks
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Repository cloned and processed on: 2025-10-29
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