Anthropic API

Anthropic API Client AI Skill Guide

Overview & Engine Architecture

The Anthropic SDK talks to the Messages API for Claude models. Requests include model, max_tokens, optional system, and alternating user/assistant messages; tool use returns tool_use blocks the client must execute and continue with tool_result. Agents pin model ids, always set max_tokens, stream when UX needs tokens early, and keep tools allowlisted.

messages.create
   -> content blocks (text / tool_use)
   -> client executes tools
   -> messages continues with tool_result

When to use this skill

• Direct Claude integrations in apps/backends
• Tool-calling workflows with strict schemas
• Streaming assistants and batch analysis jobs

Operational directives

1. Use ANTHROPIC_API_KEY from the environment only.
2. Always pass max_tokens; do not rely on implicit defaults for prod.
3. Put durable instructions in system; keep user turns free of secret keys.
4. On tool_use, execute only allowlisted tools with validated input.
5. Record message.id / usage for debugging and cost attribution.

Messages example

import os
from anthropic import Anthropic

client = Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])

msg = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=512,
    system="Answer briefly. If context is missing, say what you need.",
    messages=[
        {"role": "user", "content": "Give two risks of skipping data validation in ETL."},
    ],
)
for block in msg.content:
    if block.type == "text":
        print(block.text)
print(msg.usage)

Streaming

with client.messages.stream(
    model="claude-sonnet-4-20250514",
    max_tokens=256,
    messages=[{"role": "user", "content": "Outline a dbt testing plan."}],
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

Common failures

Best practices

• Prefer tools with JSON schemas over free-form function strings.
• Separate evaluation prompts (temp-like sampling controls) from prod configs.
• Use prompt caching features when supported for large stable system contexts.
• Pair with @langchain only when orchestration complexity justifies it.

Limitations

• Exact model ids rotate; verify against current Anthropic docs.
• Bedrock/Vertex exposures differ slightly from the public API.
• Safety filters and rate limits are account-specific.

Related skills

• @openai-api - alternate provider
• @langchain / @llamaindex - app frameworks
• @chromadb - retrieval backing store