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anthropic-api skill

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This skill provides concise guidance and references for using the Anthropic API, helping you implement, integrate, and troubleshoot endpoints efficiently.

npx playbooks add skill bankkroll/skills-builder --skill anthropic-api

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SKILL.md

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---
name: "anthropic-api"
description: "Scraped from https://docs.anthropic.com/ Source: https://docs.anthropic.com."
---

# Anthropic Api

> Official documentation: https://docs.anthropic.com

## Overview

This skill provides comprehensive documentation for anthropic api.

**Total references:** 1 files (~176 tokens)

## Reference Files

Load only the reference files relevant to the user's question:

- **[Documentation](references/general.md)** (~176 tokens)

## Usage Guidelines

1. **Identify relevant sections** - Match the user's question to the appropriate reference file(s)
2. **Load minimally** - Only read files directly relevant to the question to conserve context
3. **Cite sources** - Reference specific sections when answering
4. **Combine knowledge** - For complex questions, you may need multiple reference files

### When to use each reference:

- **General**: General documentation, overview, and getting started

Overview

This skill delivers practical, developer-focused documentation and examples for using the Anthropic API. It condenses the official docs into actionable guidance for authentication, client setup, request structure, and handling responses. The goal is to help engineers implement Anthropic models quickly and reliably in Python projects.

How this skill works

The skill inspects the canonical Anthropic documentation and extracts the key steps: installing the client, initializing the API client with a secure key, forming requests (model, prompt, parameters), and parsing model responses. It highlights required headers, common parameters (temperature, max tokens, stop sequences), and recommended error handling patterns. Where relevant, it shows concise code snippets and practical request/response workflows.

When to use it

You need a quick reference to authenticate and call Anthropic models from Python.
You want concise examples for composing prompts and setting parameters like temperature and max tokens.
You need guidance on handling API errors, rate limits, or streaming responses.
You are integrating Anthropic models into production services and need recommended request patterns.
You want to compare typical payloads for completion-style calls and how to apply stop sequences.

Best practices

Store API keys securely (environment variables or secret manager), never hard-code them in source.
Start with conservative temperature and max tokens in production, then tune using real logs.
Use explicit stop sequences to bound responses and avoid unexpected trailing text.
Implement retry/backoff for transient errors and respect rate-limit headers.
Validate and sanitize model outputs before using them in security-sensitive contexts.

Example use cases

Implementing a chat or assistant endpoint that forwards user messages to an Anthropic model and returns structured replies.
Generating structured text snippets (summaries, code, docs) with controlled length using max tokens and stop sequences.
Building a content-filtering or moderation pipeline that calls the model then applies deterministic checks.
Prototyping conversational flows locally with sample prompts and quickly iterating on temperature and system instructions.
Streaming long responses to a client UI while handling partial chunks and reconnect logic.

FAQ

How do I authenticate with the Anthropic API?

Provide your API key in an authorization header or client config, typically sourced from an environment variable or a secret manager.

What parameters should I tune first?

Start with temperature (controls randomness) and max tokens (controls length). Use stop sequences to reliably end responses.