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https://github.com/affaan-m/everything-claude-code.git
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Revert "feat: add orchestration workflows and harness skills"
This reverts commit cb43402d7d.
This commit is contained in:
Affaan Mustafa
@@ -1,337 +0,0 @@
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---
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name: claude-api
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description: Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.
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origin: ECC
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---
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# Claude API
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Build applications with the Anthropic Claude API and SDKs.
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## When to Activate
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- Building applications that call the Claude API
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- Code imports `anthropic` (Python) or `@anthropic-ai/sdk` (TypeScript)
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- User asks about Claude API patterns, tool use, streaming, or vision
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- Implementing agent workflows with Claude Agent SDK
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- Optimizing API costs, token usage, or latency
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## Model Selection
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| Model | ID | Best For |
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|-------|-----|----------|
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| Opus 4.1 | `claude-opus-4-1` | Complex reasoning, architecture, research |
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| Sonnet 4 | `claude-sonnet-4-0` | Balanced coding, most development tasks |
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| Haiku 3.5 | `claude-3-5-haiku-latest` | Fast responses, high-volume, cost-sensitive |
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Default to Sonnet 4 unless the task requires deep reasoning (Opus) or speed/cost optimization (Haiku). For production, prefer pinned snapshot IDs over aliases.
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## Python SDK
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### Installation
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```bash
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pip install anthropic
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```
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### Basic Message
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```python
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import anthropic
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client = anthropic.Anthropic() # reads ANTHROPIC_API_KEY from env
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message = client.messages.create(
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model="claude-sonnet-4-0",
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max_tokens=1024,
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messages=[
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{"role": "user", "content": "Explain async/await in Python"}
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]
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)
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print(message.content[0].text)
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```
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### Streaming
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```python
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with client.messages.stream(
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model="claude-sonnet-4-0",
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max_tokens=1024,
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messages=[{"role": "user", "content": "Write a haiku about coding"}]
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) as stream:
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for text in stream.text_stream:
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print(text, end="", flush=True)
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```
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### System Prompt
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```python
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message = client.messages.create(
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model="claude-sonnet-4-0",
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max_tokens=1024,
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system="You are a senior Python developer. Be concise.",
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messages=[{"role": "user", "content": "Review this function"}]
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)
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```
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## TypeScript SDK
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### Installation
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```bash
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npm install @anthropic-ai/sdk
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```
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### Basic Message
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```typescript
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import Anthropic from "@anthropic-ai/sdk";
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const client = new Anthropic(); // reads ANTHROPIC_API_KEY from env
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const message = await client.messages.create({
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model: "claude-sonnet-4-0",
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max_tokens: 1024,
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messages: [
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{ role: "user", content: "Explain async/await in TypeScript" }
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],
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});
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console.log(message.content[0].text);
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```
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### Streaming
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```typescript
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const stream = client.messages.stream({
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model: "claude-sonnet-4-0",
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max_tokens: 1024,
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messages: [{ role: "user", content: "Write a haiku" }],
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});
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for await (const event of stream) {
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if (event.type === "content_block_delta" && event.delta.type === "text_delta") {
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process.stdout.write(event.delta.text);
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}
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}
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```
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## Tool Use
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Define tools and let Claude call them:
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```python
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tools = [
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{
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"name": "get_weather",
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"description": "Get current weather for a location",
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"input_schema": {
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"type": "object",
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"properties": {
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"location": {"type": "string", "description": "City name"},
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"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
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},
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"required": ["location"]
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}
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}
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]
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message = client.messages.create(
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model="claude-sonnet-4-0",
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max_tokens=1024,
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tools=tools,
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messages=[{"role": "user", "content": "What's the weather in SF?"}]
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)
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# Handle tool use response
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for block in message.content:
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if block.type == "tool_use":
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# Execute the tool with block.input
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result = get_weather(**block.input)
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# Send result back
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follow_up = client.messages.create(
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model="claude-sonnet-4-0",
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max_tokens=1024,
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tools=tools,
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messages=[
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{"role": "user", "content": "What's the weather in SF?"},
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{"role": "assistant", "content": message.content},
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{"role": "user", "content": [
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{"type": "tool_result", "tool_use_id": block.id, "content": str(result)}
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]}
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]
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)
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```
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## Vision
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Send images for analysis:
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```python
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import base64
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with open("diagram.png", "rb") as f:
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image_data = base64.standard_b64encode(f.read()).decode("utf-8")
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message = client.messages.create(
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model="claude-sonnet-4-0",
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max_tokens=1024,
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messages=[{
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"role": "user",
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"content": [
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{"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": image_data}},
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{"type": "text", "text": "Describe this diagram"}
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]
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}]
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)
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```
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## Extended Thinking
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For complex reasoning tasks:
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```python
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message = client.messages.create(
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model="claude-sonnet-4-0",
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max_tokens=16000,
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thinking={
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"type": "enabled",
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"budget_tokens": 10000
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},
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messages=[{"role": "user", "content": "Solve this math problem step by step..."}]
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)
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for block in message.content:
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if block.type == "thinking":
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print(f"Thinking: {block.thinking}")
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elif block.type == "text":
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print(f"Answer: {block.text}")
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```
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## Prompt Caching
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Cache large system prompts or context to reduce costs:
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```python
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message = client.messages.create(
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model="claude-sonnet-4-0",
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max_tokens=1024,
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system=[
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{"type": "text", "text": large_system_prompt, "cache_control": {"type": "ephemeral"}}
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],
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messages=[{"role": "user", "content": "Question about the cached context"}]
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)
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# Check cache usage
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print(f"Cache read: {message.usage.cache_read_input_tokens}")
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print(f"Cache creation: {message.usage.cache_creation_input_tokens}")
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```
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## Batches API
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Process large volumes asynchronously at 50% cost reduction:
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```python
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import time
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batch = client.messages.batches.create(
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requests=[
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{
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"custom_id": f"request-{i}",
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"params": {
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"model": "claude-sonnet-4-0",
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"max_tokens": 1024,
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"messages": [{"role": "user", "content": prompt}]
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}
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}
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for i, prompt in enumerate(prompts)
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]
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)
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# Poll for completion
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while True:
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status = client.messages.batches.retrieve(batch.id)
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if status.processing_status == "ended":
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break
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time.sleep(30)
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# Get results
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for result in client.messages.batches.results(batch.id):
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print(result.result.message.content[0].text)
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```
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## Claude Agent SDK
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Build multi-step agents:
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```python
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# Note: Agent SDK API surface may change — check official docs
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import anthropic
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# Define tools as functions
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tools = [{
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"name": "search_codebase",
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"description": "Search the codebase for relevant code",
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"input_schema": {
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"type": "object",
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"properties": {"query": {"type": "string"}},
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"required": ["query"]
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}
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}]
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# Run an agentic loop with tool use
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client = anthropic.Anthropic()
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messages = [{"role": "user", "content": "Review the auth module for security issues"}]
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while True:
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response = client.messages.create(
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model="claude-sonnet-4-0",
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max_tokens=4096,
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tools=tools,
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messages=messages,
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)
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if response.stop_reason == "end_turn":
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break
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# Handle tool calls and continue the loop
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messages.append({"role": "assistant", "content": response.content})
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# ... execute tools and append tool_result messages
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```
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## Cost Optimization
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| Strategy | Savings | When to Use |
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|----------|---------|-------------|
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| Prompt caching | Up to 90% on cached tokens | Repeated system prompts or context |
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| Batches API | 50% | Non-time-sensitive bulk processing |
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| Haiku instead of Sonnet | ~75% | Simple tasks, classification, extraction |
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| Shorter max_tokens | Variable | When you know output will be short |
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| Streaming | None (same cost) | Better UX, same price |
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## Error Handling
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```python
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import time
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from anthropic import APIError, RateLimitError, APIConnectionError
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try:
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message = client.messages.create(...)
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except RateLimitError:
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# Back off and retry
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time.sleep(60)
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except APIConnectionError:
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# Network issue, retry with backoff
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pass
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except APIError as e:
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print(f"API error {e.status_code}: {e.message}")
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```
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## Environment Setup
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```bash
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# Required
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export ANTHROPIC_API_KEY="your-api-key-here"
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# Optional: set default model
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export ANTHROPIC_MODEL="claude-sonnet-4-0"
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```
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Never hardcode API keys. Always use environment variables.
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