claude-api

SKILL.md

Claude API

Build applications with the Anthropic Claude API and SDKs.

When to Activate

Building applications that call the Claude API

Code imports anthropic (Python) or @anthropic-ai/sdk (TypeScript)

User asks about Claude API patterns, tool use, streaming, or vision

Implementing agent workflows with Claude Agent SDK

Optimizing API costs, token usage, or latency

Model Selection

Model
ID
Best For

Opus 4.1
claude-opus-4-1
Complex reasoning, architecture, research

Sonnet 4
claude-sonnet-4-0
Balanced coding, most development tasks

Haiku 3.5
claude-3-5-haiku-latest
Fast responses, high-volume, cost-sensitive

Default to Sonnet 4 unless the task requires deep reasoning (Opus) or speed/cost optimization (Haiku). For production, prefer pinned snapshot IDs over aliases.

Python SDK

Installation

pip install anthropic

Basic Message

import anthropic

client = anthropic.Anthropic()  # reads ANTHROPIC_API_KEY from env

message = client.messages.create(
    model="claude-sonnet-4-0",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Explain async/await in Python"}
    ]
)
print(message.content[0].text)

Streaming

with client.messages.stream(
    model="claude-sonnet-4-0",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Write a haiku about coding"}]
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

System Prompt

message = client.messages.create(
    model="claude-sonnet-4-0",
    max_tokens=1024,
    system="You are a senior Python developer. Be concise.",
    messages=[{"role": "user", "content": "Review this function"}]
)

TypeScript SDK

Installation

npm install @anthropic-ai/sdk

Basic Message

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic(); // reads ANTHROPIC_API_KEY from env

const message = await client.messages.create({
  model: "claude-sonnet-4-0",
  max_tokens: 1024,
  messages: [
    { role: "user", content: "Explain async/await in TypeScript" }
  ],
});
console.log(message.content[0].text);

Streaming

const stream = client.messages.stream({
  model: "claude-sonnet-4-0",
  max_tokens: 1024,
  messages: [{ role: "user", content: "Write a haiku" }],
});

for await (const event of stream) {
  if (event.type === "content_block_delta" && event.delta.type === "text_delta") {
    process.stdout.write(event.delta.text);
  }
}

Tool Use

Define tools and let Claude call them:

tools = [
    {
        "name": "get_weather",
        "description": "Get current weather for a location",
        "input_schema": {
            "type": "object",
            "properties": {
                "location": {"type": "string", "description": "City name"},
                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
            },
            "required": ["location"]
        }
    }
]

message = client.messages.create(
    model="claude-sonnet-4-0",
    max_tokens=1024,
    tools=tools,
    messages=[{"role": "user", "content": "What's the weather in SF?"}]
)

# Handle tool use response
for block in message.content:
    if block.type == "tool_use":
        # Execute the tool with block.input
        result = get_weather(**block.input)
        # Send result back
        follow_up = client.messages.create(
            model="claude-sonnet-4-0",
            max_tokens=1024,
            tools=tools,
            messages=[
                {"role": "user", "content": "What's the weather in SF?"},
                {"role": "assistant", "content": message.content},
                {"role": "user", "content": [
                    {"type": "tool_result", "tool_use_id": block.id, "content": str(result)}
                ]}
            ]
        )

Vision

Send images for analysis:

import base64

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

message = client.messages.create(
    model="claude-sonnet-4-0",
    max_tokens=1024,
    messages=[{
        "role": "user",
        "content": [
            {"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": image_data}},
            {"type": "text", "text": "Describe this diagram"}
        ]
    }]
)

Extended Thinking

For complex reasoning tasks:

message = client.messages.create(
    model="claude-sonnet-4-0",
    max_tokens=16000,
    thinking={
        "type": "enabled",
        "budget_tokens": 10000
    },
    messages=[{"role": "user", "content": "Solve this math problem step by step..."}]
)

for block in message.content:
    if block.type == "thinking":
        print(f"Thinking: {block.thinking}")
    elif block.type == "text":
        print(f"Answer: {block.text}")

Prompt Caching

Cache large system prompts or context to reduce costs:

message = client.messages.create(
    model="claude-sonnet-4-0",
    max_tokens=1024,
    system=[
        {"type": "text", "text": large_system_prompt, "cache_control": {"type": "ephemeral"}}
    ],
    messages=[{"role": "user", "content": "Question about the cached context"}]
)
# Check cache usage
print(f"Cache read: {message.usage.cache_read_input_tokens}")
print(f"Cache creation: {message.usage.cache_creation_input_tokens}")

Batches API

Process large volumes asynchronously at 50% cost reduction:

import time

batch = client.messages.batches.create(
    requests=[
        {
            "custom_id": f"request-{i}",
            "params": {
                "model": "claude-sonnet-4-0",
                "max_tokens": 1024,
                "messages": [{"role": "user", "content": prompt}]
            }
        }
        for i, prompt in enumerate(prompts)
    ]
)

# Poll for completion
while True:
    status = client.messages.batches.retrieve(batch.id)
    if status.processing_status == "ended":
        break
    time.sleep(30)

# Get results
for result in client.messages.batches.results(batch.id):
    print(result.result.message.content[0].text)

Claude Agent SDK

Build multi-step agents:

# Note: Agent SDK API surface may change — check official docs
import anthropic

# Define tools as functions
tools = [{
    "name": "search_codebase",
    "description": "Search the codebase for relevant code",
    "input_schema": {
        "type": "object",
        "properties": {"query": {"type": "string"}},
        "required": ["query"]
    }
}]

# Run an agentic loop with tool use
client = anthropic.Anthropic()
messages = [{"role": "user", "content": "Review the auth module for security issues"}]

while True:
    response = client.messages.create(
        model="claude-sonnet-4-0",
        max_tokens=4096,
        tools=tools,
        messages=messages,
    )
    if response.stop_reason == "end_turn":
        break
    # Handle tool calls and continue the loop
    messages.append({"role": "assistant", "content": response.content})
    # ... execute tools and append tool_result messages

Cost Optimization

Strategy
Savings
When to Use

Prompt caching
Up to 90% on cached tokens
Repeated system prompts or context

Batches API
50%
Non-time-sensitive bulk processing

Haiku instead of Sonnet
~75%
Simple tasks, classification, extraction

Shorter max_tokens
Variable
When you know output will be short

Streaming
None (same cost)
Better UX, same price

Error Handling

import time

from anthropic import APIError, RateLimitError, APIConnectionError

try:
    message = client.messages.create(...)
except RateLimitError:
    # Back off and retry
    time.sleep(60)
except APIConnectionError:
    # Network issue, retry with backoff
    pass
except APIError as e:
    print(f"API error {e.status_code}: {e.message}")

Environment Setup

# Required
export ANTHROPIC_API_KEY="your-api-key-here"

# Optional: set default model
export ANTHROPIC_MODEL="claude-sonnet-4-0"

Never hardcode API keys. Always use environment variables.
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