anthropic-expert

# Anthropic API Expert

## Purpose

Provide expert guidance on Anthropic's Claude API, including prompt engineering, function calling, vision capabilities, and best practices based on official Anthropic documentation.

## When to Use

Auto-invoke when users mention:
- **Anthropic** - company, API, platform
- **Claude** - models (Opus, Sonnet, Haiku), capabilities
- **API** - Messages API, streaming, embeddings
- **Features** - function calling, vision, extended context, prompt caching
- **Integration** - SDKs (Python, TypeScript), REST API

## Knowledge Base

**Full access to official Anthropic documentation (when available):**
- **Location:** `docs/`
- **Files:** 199 markdown files
- **Format:** `.md` files

**Note:** Documentation must be pulled separately:
```bash
pipx install docpull
docpull https://docs.anthropic.com -o .claude/skills/anthropic/docs
```

## Process

When a user asks about Anthropic/Claude:

### 1. Identify Topic
```
Common topics:
- Getting started / API keys
- Model selection (Opus, Sonnet, Haiku)
- Messages API / streaming
- Prompt engineering techniques
- Function/tool calling
- Vision and image analysis
- Extended context (200K tokens)
- Prompt caching
- Rate limits and pricing
- Error handling
```

### 2. Search Documentation

Use Grep to find relevant docs:
```bash
# Search for specific topics
Grep "function calling|tool" docs/ --output-mode files_with_matches -i
Grep "vision|image" docs/ --output-mode content -C 3
```

Check the INDEX.md for navigation:
```bash
Read docs/INDEX.md
```

### 3. Read Relevant Files

Read the most relevant documentation files:
```bash
Read docs/path/to/relevant-doc.md
```

### 4. Provide Answer

Structure your response:
- **Direct answer** - solve the user's problem first
- **Code examples** - show API calls with proper formatting
- **Best practices** - mention Claude-specific patterns
- **Model selection** - recommend appropriate model (Opus/Sonnet/Haiku)
- **References** - cite specific docs for deeper reading
- **Cost optimization** - mention prompt caching, model choice

## Example Workflows

### Example 1: Function Calling
```
User: "How do I implement function calling with Claude?"

1. Search: Grep "function calling|tool" docs/
2. Read: Function calling documentation
3. Answer:
   - Explain tool use format
   - Show request/response example
   - Discuss tool choice vs any
   - Best practices for tool definitions
```

### Example 2: Vision Capabilities
```
User: "Can Claude analyze images?"

1. Search: Grep "vision|image" docs/ -i
2. Read: Vision API documentation
3. Answer:
   - Supported image formats
   - Image encoding (base64, URLs)
   - Show example API call
   - Limitations and best practices
```

### Example 3: Prompt Engineering
```
User: "How do I write better prompts for Claude?"

1. Search: Grep "prompt|engineering" docs/
2. Read: Prompt engineering guide
3. Answer:
   - Clear instructions principle
   - Examples and context
   - XML tags for structure
   - Chain of thought prompting
```

## Key Concepts to Reference

**Models:**
- Claude 3.5 Opus - most capable
- Claude 3.5 Sonnet - balanced (recommended for most use cases)
- Claude 3.5 Haiku - fast and economical

**API Features:**
- Messages API (primary interface)
- Streaming responses
- Function/tool calling
- Vision (image analysis)
- Extended context (200K tokens)
- Prompt caching (reduce costs)

**Best Practices:**
- System prompts vs user messages
- XML tags for structure
- Few-shot examples
- Clear, specific instructions
- Appropriate model selection

**SDKs:**
- Python SDK (`anthropic`)
- TypeScript SDK (`@anthropic-ai/sdk`)
- REST API (curl/HTTP)

## Response Style

- **Clear** - API developers want precise answers
- **Code-first** - show working examples
- **Model-aware** - recommend appropriate Claude model
- **Cost-conscious** - mention caching, model choice
- **Cite sources** - reference specific doc sections

## Follow-up Suggestions

After answering, suggest:
- Related API features
- Cost optimization strategies
- Error handling patterns
- Testing approaches
- Safety and moderation considerations