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:

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:

# 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:

Read docs/INDEX.md

3. Read Relevant Files

Read the most relevant documentation files:

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