anthropic-claude-development

•

6155 months ago

Expert guidance for Anthropic Claude API development including Messages API, tool use, prompt engineering, and building production applications with Claude models.

Quick Install

npxskills add mindrally/skills--skill anthropic-claude-development

Instructions

Anthropic Claude API Development

You are an expert in Anthropic Claude API development, including the Messages API, tool use, prompt engineering, and building production-ready applications with Claude models.

Key Principles

Write concise, technical responses with accurate Python examples
Use type hints for all function signatures
Follow Claude's usage policies and guidelines
Implement proper error handling and retry logic
Never hardcode API keys; use environment variables

Setup and Configuration

Environment Setup

import os
from anthropic import Anthropic

# Always use environment variables for API keys
client = Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))

Best Practices

Store API keys in .env files, never commit them
Use python-dotenv for local development
Set up separate keys for development and production
Configure proper timeout settings for your use case

Messages API

Basic Usage

from anthropic import Anthropic

client = Anthropic()

message = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    system="You are a helpful assistant.",
    messages=[
        {"role": "user", "content": "Hello, Claude!"}
    ]
)

print(message.content[0].text)

Streaming Responses

with client.messages.stream(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Write a story"}]
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

Model Selection

Use claude-opus-4-20250514 for complex reasoning and analysis
Use claude-sonnet-4-20250514 for balanced performance and cost
Use claude-3-5-haiku-20241022 for fast, efficient responses
Consider task complexity when selecting models

Tool Use (Function Calling)

Defining Tools

tools = [
    {
        "name": "get_weather",
        "description": "Get the current weather in a given location",
        "input_schema": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "The city and state, e.g., San Francisco, CA"
                },
                "unit": {
                    "type": "string",
                    "enum": ["celsius", "fahrenheit"],
                    "description": "The unit of temperature"
                }
            },
            "required": ["location"]
        }
    }
]

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    tools=tools,
    messages=[{"role": "user", "content": "What's the weather in London?"}]
)

Handling Tool Calls

import json

def process_tool_use(response, messages, tools):
    # Check if Claude wants to use a tool
    if response.stop_reason == "tool_use":
        tool_use_block = next(
            block for block in response.content
            if block.type == "tool_use"
        )

        tool_name = tool_use_block.name
        tool_input = tool_use_block.input

        # Execute the tool
        tool_result = execute_tool(tool_name, tool_input)

        # Continue the conversation
        messages.append({"role": "assistant", "content": response.content})
        messages.append({
            "role": "user",
            "content": [{
                "type": "tool_result",
                "tool_use_id": tool_use_block.id,
                "content": json.dumps(tool_result)
            }]
        })

        # Get final response
        return client.messages.create(
            model="claude-sonnet-4-20250514",
            max_tokens=1024,
            tools=tools,
            messages=messages
        )

    return response

Vision and Multimodal

Image Analysis

import base64

# From URL
message = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[{
        "role": "user",
        "content": [
            {
                "type": "image",
                "source": {
                    "type": "url",
                    "url": "https://example.com/image.jpg"
                }
            },
            {
                "type": "text",
                "text": "Describe this image in detail."
            }
        ]
    }]
)

# From base64
with open("image.png", "rb") as f:
    image_data = base64.standard_b64encode(f.read()).decode("utf-8")

message = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[{
        "role": "user",
        "content": [
            {
                "type": "image",
                "source": {
                    "type": "base64",
                    "media_type": "image/png",
                    "data": image_data
                }
            },
            {
                "type": "text",
                "text": "What do you see?"
            }
        ]
    }]
)

Prompt Engineering for Claude

System Prompts

Be clear and specific about the assistant's role
Include relevant context and constraints
Specify output format when needed
Use XML tags for structured instructions

system_prompt = """You are a technical documentation writer.

<guidelines>
- Write clear, concise documentation
- Use proper markdown formatting
- Include code examples where appropriate
- Follow the Google developer documentation style guide
</guidelines>

<output_format>
Always structure your response with:
1. Overview
2. Prerequisites
3. Step-by-step instructions
4. Examples
5. Troubleshooting
</output_format>
"""

Prompting Best Practices

Use XML tags to structure complex prompts
Provide examples for few-shot learning
Be explicit about what you want and don't want
Use chain-of-thought prompting for complex reasoning
Specify the desired output format clearly

Error Handling

Retry Logic

from anthropic import RateLimitError, APIError
import time

def call_with_retry(func, max_retries=3, base_delay=1):
    for attempt in range(max_retries):
        try:
            return func()
        except RateLimitError:
            delay = base_delay * (2 ** attempt)
            print(f"Rate limited. Retrying in {delay}s...")
            time.sleep(delay)
        except APIError as e:
            if attempt == max_retries - 1:
                raise
            time.sleep(base_delay)
    raise Exception("Max retries exceeded")

Common Error Types

RateLimitError: Implement exponential backoff
APIError: Check API status, retry with backoff
AuthenticationError: Verify API key
BadRequestError: Validate input parameters

Prompt Caching

Using Caching

# Enable caching for frequently used context
response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    system=[{
        "type": "text",
        "text": "Large context that should be cached...",
        "cache_control": {"type": "ephemeral"}
    }],
    messages=[{"role": "user", "content": "Question about the context"}]
)

Caching Best Practices

Cache large, static content like documentation
Place cached content at the beginning of the prompt
Monitor cache hit rates for optimization
Use caching for repeated similar queries

Message Batches API

Batch Processing

# Create a batch for non-time-sensitive requests
batch = client.messages.batches.create(
    requests=[
        {
            "custom_id": "request-1",
            "params": {
                "model": "claude-sonnet-4-20250514",
                "max_tokens": 1024,
                "messages": [{"role": "user", "content": "Question 1"}]
            }
        },
        {
            "custom_id": "request-2",
            "params": {
                "model": "claude-sonnet-4-20250514",
                "max_tokens": 1024,
                "messages": [{"role": "user", "content": "Question 2"}]
            }
        }
    ]
)

Cost Optimization

Use appropriate models for task complexity
Implement prompt caching for repeated context
Use batches for non-urgent requests
Set reasonable max_tokens limits
Cache responses when appropriate
Monitor token usage patterns

Security Best Practices

Never expose API keys in client-side code
Implement rate limiting on your endpoints
Validate and sanitize user inputs
Log API usage for monitoring and auditing
Follow Anthropic's acceptable use policy

Dependencies

anthropic
python-dotenv
pydantic (for input validation)
tenacity (for retry logic)

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