Introduction

Building applications with the Claude API is powerful, but keeping up with SDK updates, model migrations, and best practices can be overwhelming. Enter the Claude API Skill — an open-source Agent Skill that equips Claude with detailed, up-to-date reference material for two primary Anthropic surfaces: the Messages API and Claude Managed Agents (beta).

Whether you're a seasoned developer or just starting with Claude, this skill transforms your workflow by providing language-specific SDK documentation, tool use guidance, streaming patterns, and more — all while using progressive disclosure to keep context efficient. In this guide, you'll learn exactly what the skill offers, how it activates, and how to use it to build smarter, faster.

What Is the Claude API Skill?

The Claude API Skill is a specialized Agent Skill that lives in the open-source Anthropic skills repository and comes bundled with Claude Code. When triggered, it gives Claude access to:

• Messages API documentation across 8 programming languages: Python, TypeScript, Java, Go, Ruby, C#, PHP, and cURL.
• Managed Agents documentation across 7 languages (C# not yet supported).
• Progressive disclosure: Claude loads only the docs relevant to your project's language, surface, and task — not everything at once.

This means you get exactly the information you need, when you need it, without bloating the context window.

How the Skill Activates

The skill activates automatically in three key scenarios:

• Your code imports an Anthropic SDK — e.g., anthropic for Python or @anthropic-ai/sdk for TypeScript/JavaScript.
• You ask Claude to help build, debug, or optimize something with the Claude API, an Anthropic SDK, or Managed Agents.
• You add, modify, or tune a Claude feature in a file (like prompt caching, tool use, or streaming).

Once activated, Claude uses the skill to fetch the most relevant documentation, code examples, and best practices for your specific task.

What the Skill Provides

For the Messages API

The Messages API is the primary surface for single requests, streaming chat, tool use, batch processing, prompt caching, structured outputs, and custom agent loops. The skill covers:

• Language-specific SDK documentation: Installation, quick start, common patterns, and error handling for your chosen language.
• Tool use guidance: Language-specific examples and conceptual foundations for function calling, including the beta tool runner where available.
• Streaming patterns: Implementation details for building chat UIs and handling incremental display.
• Batch processing: Offline batch processing at 50% cost.
• Prompt caching: Prefix-stability design, breakpoint placement, and silent-invalidator audit.
• Model migration: Step-by-step guidance for migrating to newer Claude models (including breaking changes on Claude Opus 4.7).
• Current model information: Model IDs, context window sizes, and pricing.
• Common pitfalls: Detailed guidance on avoiding frequent integration mistakes.

For Managed Agents (beta)

Managed Agents is a first-party surface for server-managed stateful agents with Anthropic-hosted tool execution, persistent agent configs, and per-session containers. The skill provides:

• Onboarding flow: An interview-driven walkthrough for setting up a new Managed Agent from scratch, available via the /claude-api managed-agents-onboard subcommand.
• Language-specific docs: Creating persistent agents, starting sessions, streaming events, and handling tool confirmations.
• Client patterns: Lossless stream reconnect, processed_at queued/processed gate, interrupt handling, file-mount gotchas, and credential handling.
• Deployment constraints: Managed Agents is first-party only (not available on Amazon Bedrock, Google Vertex AI, or Microsoft Foundry) — the skill routes third-party deployments to Messages API + tool use instead.

Practical Code Examples

Example 1: Using the Messages API with Python (with Skill Activation)

When you import the Anthropic SDK, the skill activates automatically. Here's a basic streaming chat example:

import anthropic
client = anthropic.Anthropic()
with client.messages.stream(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Explain how the Claude API Skill works in one sentence."}
    ]
) as stream:
    for text in stream.text_stream:
        print(text, end="")

Because the skill detected the anthropic import, Claude can now provide you with real-time guidance on streaming patterns, error handling, and more.

Example 2: Tool Use with TypeScript

import Anthropic from '@anthropic-ai/sdk';
const client = new Anthropic();
const response = await client.messages.create({
  model: 'claude-sonnet-4-20250514',
  max_tokens: 1024,
  tools: [{
    name: 'get_weather',
    description: 'Get the current weather for a location',
    input_schema: {
      type: 'object',
      properties: {
        location: { type: 'string' }
      },
      required: ['location']
    }
  }],
  messages: [{ role: 'user', content: 'What\'s the weather in Tokyo?' }]
});
console.log(response.content);

The skill will automatically load tool use documentation specific to TypeScript, including how to handle tool calls and parallel tool use.

Example 3: Batch Processing with Python

import anthropic
from anthropic.types import MessageCreateParams
client = anthropic.Anthropic()
messages = [
    {"role": "user", "content": "Summarize this article."},
    {"role": "user", "content": "Translate this to French."},
]
Batch processing at 50% cost
results = client.batches.create(
    requests=[
        {"custom_id": "req-1", "params": MessageCreateParams(
            model="claude-sonnet-4-20250514",
            max_tokens=1024,
            messages=[{"role": "user", "content": "Summarize quantum computing."}]
        )},
        {"custom_id": "req-2", "params": MessageCreateParams(
            model="claude-sonnet-4-20250514",
            max_tokens=1024,
            messages=[{"role": "user", "content": "Explain machine learning."}]
        )}
    ]
)

Best Practices for Using the Skill

• Let the skill activate naturally — Don't force it. Just start coding with the Anthropic SDK or ask Claude to help with an API task.
• Specify your language — If you're working in Go or Ruby, mention it explicitly so the skill loads the right docs.
• Use progressive disclosure to your advantage — The skill won't load everything at once, so be specific about your task (e.g., "help me with prompt caching in Python").
• For Managed Agents, use the onboarding subcommand — Run /claude-api managed-agents-onboard to get an interview-driven setup walkthrough.
• Check deployment constraints — If you're using Amazon Bedrock or Vertex AI, the skill will automatically route you to Messages API + tool use instead of Managed Agents.

Common Pitfalls to Avoid

• Assuming all languages are equal — C# is not supported for Managed Agents. Always verify language support in the skill docs.
• Forgetting to specify the surface — If you're building a custom agent loop, make sure to mention "Messages API" so the skill doesn't load Managed Agents docs.
• Ignoring model migration guidance — When upgrading to Claude Opus 4.7, the skill provides breaking change details. Don't skip this.
• Overlooking prompt caching best practices — The skill includes a silent-invalidator audit; use it to optimize your caching strategy.

Key Takeaways

• The Claude API Skill is an open-source Agent Skill that provides language-specific, up-to-date documentation for both the Messages API and Managed Agents, using progressive disclosure to keep context efficient.
• It activates automatically when you import an Anthropic SDK, ask for help with the Claude API, or modify Claude-related features in your code.
• The skill covers 8 languages for the Messages API and 7 for Managed Agents, with detailed guidance on tool use, streaming, batch processing, prompt caching, and model migration.
• For Managed Agents, use the /claude-api managed-agents-onboard subcommand for an interview-driven setup, and remember that Managed Agents is first-party only.
• By leveraging the skill's progressive disclosure and language-specific examples, you can build, debug, and optimize Claude-powered applications faster and with fewer mistakes.