[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6JKEY7WTCYZ9P1ASSGAEMBA":3,"skill-same:01M6JKEY7WTCYZ9P1ASSGAEMBA":81},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":31,"publishedAt":52,"linkOnly":53,"markdownContent":54,"markdownBody":55,"frontMatter":56,"refName":58,"commitSha":59,"skillDirectory":60,"forksCount":61,"repositoryHtmlUrl":62,"repositoryHomepageUrl":63,"ownerHtmlUrl":64,"ownerBlogUrl":65,"ownerDescription":6,"updatedAt":66,"install":67,"resources":80},"01M6JKEY7WTCYZ9P1ASSGAEMBA","claude-api",null,"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.","LongLeo287\u002FOmniClaw","LongLeo287","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F141803367?v=4","01M6JK75C62VCNZKR5BRZ386YV","LongLeo","01M6JK75GQ78TSP1ZPXNKK3MCH",9,"MIT","https:\u002F\u002Fgithub.com\u002FLongLeo287\u002FOmniClaw\u002Fblob\u002Fmain\u002Fbrain\u002Fknowledge\u002Frepositories\u002Feverything_claude_code\u002F.agents\u002Fskills\u002Fclaude_api\u002FSKILL.md",[18,23,26],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":27,"categoryName":28,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51],"api","claude api","tool","for","sdks","agent sdk","caching","patterns","thinking","vision","use","prompt","applications","api patterns","prompt caching","anthropic claude","messages","streaming","python","typescript","2026-11-10 10:12:30",false,"---\nname: claude-api\ndescription: 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.\norigin: ECC\n---\n\n# Claude API\n\nBuild applications with the Anthropic Claude API and SDKs.\n\n## When to Activate\n\n- Building applications that call the Claude API\n- Code imports `anthropic` (Python) or `@anthropic-ai\u002Fsdk` (TypeScript)\n- User asks about Claude API patterns, tool use, streaming, or vision\n- Implementing agent workflows with Claude Agent SDK\n- Optimizing API costs, token usage, or latency\n\n## Model Selection\n\n| Model | ID | Best For |\n|-------|-----|----------|\n| Opus 4.6 | `claude-opus-4-6` | Complex reasoning, architecture, research |\n| Sonnet 4.6 | `claude-sonnet-4-6` | Balanced coding, most development tasks |\n| Haiku 4.5 | `claude-haiku-4-5-20251001` | Fast responses, high-volume, cost-sensitive |\n\nDefault to Sonnet 4.6 unless the task requires deep reasoning (Opus) or speed\u002Fcost optimization (Haiku).\n\n## Python SDK\n\n### Installation\n\n```bash\npip install anthropic\n```\n\n### Basic Message\n\n```python\nimport anthropic\n\nclient = anthropic.Anthropic()  # reads ANTHROPIC_API_KEY from env\n\nmessage = client.messages.create(\n    model=\"claude-sonnet-4-6\",\n    max_tokens=1024,\n    messages=[\n        {\"role\": \"user\", \"content\": \"Explain async\u002Fawait in Python\"}\n    ]\n)\nprint(message.content[0].text)\n```\n\n### Streaming\n\n```python\nwith client.messages.stream(\n    model=\"claude-sonnet-4-6\",\n    max_tokens=1024,\n    messages=[{\"role\": \"user\", \"content\": \"Write a haiku about coding\"}]\n) as stream:\n    for text in stream.text_stream:\n        print(text, end=\"\", flush=True)\n```\n\n### System Prompt\n\n```python\nmessage = client.messages.create(\n    model=\"claude-sonnet-4-6\",\n    max_tokens=1024,\n    system=\"You are a senior Python developer. Be concise.\",\n    messages=[{\"role\": \"user\", \"content\": \"Review this function\"}]\n)\n```\n\n## TypeScript SDK\n\n### Installation\n\n```bash\nnpm install @anthropic-ai\u002Fsdk\n```\n\n### Basic Message\n\n```typescript\nimport Anthropic from \"@anthropic-ai\u002Fsdk\";\n\nconst client = new Anthropic(); \u002F\u002F reads ANTHROPIC_API_KEY from env\n\nconst message = await client.messages.create({\n  model: \"claude-sonnet-4-6\",\n  max_tokens: 1024,\n  messages: [\n    { role: \"user\", content: \"Explain async\u002Fawait in TypeScript\" }\n  ],\n});\nconsole.log(message.content[0].text);\n```\n\n### Streaming\n\n```typescript\nconst stream = client.messages.stream({\n  model: \"claude-sonnet-4-6\",\n  max_tokens: 1024,\n  messages: [{ role: \"user\", content: \"Write a haiku\" }],\n});\n\nfor await (const event of stream) {\n  if (event.type === \"content_block_delta\" && event.delta.type === \"text_delta\") {\n    process.stdout.write(event.delta.text);\n  }\n}\n```\n\n## Tool Use\n\nDefine tools and let Claude call them:\n\n```python\ntools = [\n    {\n        \"name\": \"get_weather\",\n        \"description\": \"Get current weather for a location\",\n        \"input_schema\": {\n            \"type\": \"object\",\n            \"properties\": {\n                \"location\": {\"type\": \"string\", \"description\": \"City name\"},\n                \"unit\": {\"type\": \"string\", \"enum\": [\"celsius\", \"fahrenheit\"]}\n            },\n            \"required\": [\"location\"]\n        }\n    }\n]\n\nmessage = client.messages.create(\n    model=\"claude-sonnet-4-6\",\n    max_tokens=1024,\n    tools=tools,\n    messages=[{\"role\": \"user\", \"content\": \"What's the weather in SF?\"}]\n)\n\n# Handle tool use response\nfor block in message.content:\n    if block.type == \"tool_use\":\n        # Execute the tool with block.input\n        result = get_weather(**block.input)\n        # Send result back\n        follow_up = client.messages.create(\n            model=\"claude-sonnet-4-6\",\n            max_tokens=1024,\n            tools=tools,\n            messages=[\n                {\"role\": \"user\", \"content\": \"What's the weather in SF?\"},\n                {\"role\": \"assistant\", \"content\": message.content},\n                {\"role\": \"user\", \"content\": [\n                    {\"type\": \"tool_result\", \"tool_use_id\": block.id, \"content\": str(result)}\n                ]}\n            ]\n        )\n```\n\n## Vision\n\nSend images for analysis:\n\n```python\nimport base64\n\nwith open(\"diagram.png\", \"rb\") as f:\n    image_data = base64.standard_b64encode(f.read()).decode(\"utf-8\")\n\nmessage = client.messages.create(\n    model=\"claude-sonnet-4-6\",\n    max_tokens=1024,\n    messages=[{\n        \"role\": \"user\",\n        \"content\": [\n            {\"type\": \"image\", \"source\": {\"type\": \"base64\", \"media_type\": \"image\u002Fpng\", \"data\": image_data}},\n            {\"type\": \"text\", \"text\": \"Describe this diagram\"}\n        ]\n    }]\n)\n```\n\n## Extended Thinking\n\nFor complex reasoning tasks:\n\n```python\nmessage = client.messages.create(\n    model=\"claude-sonnet-4-6\",\n    max_tokens=16000,\n    thinking={\n        \"type\": \"enabled\",\n        \"budget_tokens\": 10000\n    },\n    messages=[{\"role\": \"user\", \"content\": \"Solve this math problem step by step...\"}]\n)\n\nfor block in message.content:\n    if block.type == \"thinking\":\n        print(f\"Thinking: {block.thinking}\")\n    elif block.type == \"text\":\n        print(f\"Answer: {block.text}\")\n```\n\n## Prompt Caching\n\nCache large system prompts or context to reduce costs:\n\n```python\nmessage = client.messages.create(\n    model=\"claude-sonnet-4-6\",\n    max_tokens=1024,\n    system=[\n        {\"type\": \"text\", \"text\": large_system_prompt, \"cache_control\": {\"type\": \"ephemeral\"}}\n    ],\n    messages=[{\"role\": \"user\", \"content\": \"Question about the cached context\"}]\n)\n# Check cache usage\nprint(f\"Cache read: {message.usage.cache_read_input_tokens}\")\nprint(f\"Cache creation: {message.usage.cache_creation_input_tokens}\")\n```\n\n## Batches API\n\nProcess large volumes asynchronously at 50% cost reduction:\n\n```python\nimport time\n\nbatch = client.messages.batches.create(\n    requests=[\n        {\n            \"custom_id\": f\"request-{i}\",\n            \"params\": {\n                \"model\": \"claude-sonnet-4-6\",\n                \"max_tokens\": 1024,\n                \"messages\": [{\"role\": \"user\", \"content\": prompt}]\n            }\n        }\n        for i, prompt in enumerate(prompts)\n    ]\n)\n\n# Poll for completion\nwhile True:\n    status = client.messages.batches.retrieve(batch.id)\n    if status.processing_status == \"ended\":\n        break\n    time.sleep(30)\n\n# Get results\nfor result in client.messages.batches.results(batch.id):\n    print(result.result.message.content[0].text)\n```\n\n## Claude Agent SDK\n\nBuild multi-step agents:\n\n```python\n# Note: Agent SDK API surface may change — check official docs\nimport anthropic\n\n# Define tools as functions\ntools = [{\n    \"name\": \"search_codebase\",\n    \"description\": \"Search the codebase for relevant code\",\n    \"input_schema\": {\n        \"type\": \"object\",\n        \"properties\": {\"query\": {\"type\": \"string\"}},\n        \"required\": [\"query\"]\n    }\n}]\n\n# Run an agentic loop with tool use\nclient = anthropic.Anthropic()\nmessages = [{\"role\": \"user\", \"content\": \"Review the auth module for security issues\"}]\n\nwhile True:\n    response = client.messages.create(\n        model=\"claude-sonnet-4-6\",\n        max_tokens=4096,\n        tools=tools,\n        messages=messages,\n    )\n    if response.stop_reason == \"end_turn\":\n        break\n    # Handle tool calls and continue the loop\n    messages.append({\"role\": \"assistant\", \"content\": response.content})\n    # ... execute tools and append tool_result messages\n```\n\n## Cost Optimization\n\n| Strategy | Savings | When to Use |\n|----------|---------|-------------|\n| Prompt caching | Up to 90% on cached tokens | Repeated system prompts or context |\n| Batches API | 50% | Non-time-sensitive bulk processing |\n| Haiku instead of Sonnet | ~75% | Simple tasks, classification, extraction |\n| Shorter max_tokens | Variable | When you know output will be short |\n| Streaming | None (same cost) | Better UX, same price |\n\n## Error Handling\n\n```python\nimport time\n\nfrom anthropic import APIError, RateLimitError, APIConnectionError\n\ntry:\n    message = client.messages.create(...)\nexcept RateLimitError:\n    # Back off and retry\n    time.sleep(60)\nexcept APIConnectionError:\n    # Network issue, retry with backoff\n    pass\nexcept APIError as e:\n    print(f\"API error {e.status_code}: {e.message}\")\n```\n\n## Environment Setup\n\n```bash\n# Required\nexport ANTHROPIC_API_KEY='[REDACTED_API_KEY]'\n\n# Optional: set default model\nexport ANTHROPIC_MODEL=\"claude-sonnet-4-6\"\n```\n\nNever hardcode API keys. Always use environment variables.\n","# Claude API\n\nBuild applications with the Anthropic Claude API and SDKs.\n\n## When to Activate\n\n- Building applications that call the Claude API\n- Code imports `anthropic` (Python) or `@anthropic-ai\u002Fsdk` (TypeScript)\n- User asks about Claude API patterns, tool use, streaming, or vision\n- Implementing agent workflows with Claude Agent SDK\n- Optimizing API costs, token usage, or latency\n\n## Model Selection\n\n| Model | ID | Best For |\n|-------|-----|----------|\n| Opus 4.6 | `claude-opus-4-6` | Complex reasoning, architecture, research |\n| Sonnet 4.6 | `claude-sonnet-4-6` | Balanced coding, most development tasks |\n| Haiku 4.5 | `claude-haiku-4-5-20251001` | Fast responses, high-volume, cost-sensitive |\n\nDefault to Sonnet 4.6 unless the task requires deep reasoning (Opus) or speed\u002Fcost optimization (Haiku).\n\n## Python SDK\n\n### Installation\n\n```bash\npip install anthropic\n```\n\n### Basic Message\n\n```python\nimport anthropic\n\nclient = anthropic.Anthropic()  # reads ANTHROPIC_API_KEY from env\n\nmessage = client.messages.create(\n    model=\"claude-sonnet-4-6\",\n    max_tokens=1024,\n    messages=[\n        {\"role\": \"user\", \"content\": \"Explain async\u002Fawait in Python\"}\n    ]\n)\nprint(message.content[0].text)\n```\n\n### Streaming\n\n```python\nwith client.messages.stream(\n    model=\"claude-sonnet-4-6\",\n    max_tokens=1024,\n    messages=[{\"role\": \"user\", \"content\": \"Write a haiku about coding\"}]\n) as stream:\n    for text in stream.text_stream:\n        print(text, end=\"\", flush=True)\n```\n\n### System Prompt\n\n```python\nmessage = client.messages.create(\n    model=\"claude-sonnet-4-6\",\n    max_tokens=1024,\n    system=\"You are a senior Python developer. Be concise.\",\n    messages=[{\"role\": \"user\", \"content\": \"Review this function\"}]\n)\n```\n\n## TypeScript SDK\n\n### Installation\n\n```bash\nnpm install @anthropic-ai\u002Fsdk\n```\n\n### Basic Message\n\n```typescript\nimport Anthropic from \"@anthropic-ai\u002Fsdk\";\n\nconst client = new Anthropic(); \u002F\u002F reads ANTHROPIC_API_KEY from env\n\nconst message = await client.messages.create({\n  model: \"claude-sonnet-4-6\",\n  max_tokens: 1024,\n  messages: [\n    { role: \"user\", content: \"Explain async\u002Fawait in TypeScript\" }\n  ],\n});\nconsole.log(message.content[0].text);\n```\n\n### Streaming\n\n```typescript\nconst stream = client.messages.stream({\n  model: \"claude-sonnet-4-6\",\n  max_tokens: 1024,\n  messages: [{ role: \"user\", content: \"Write a haiku\" }],\n});\n\nfor await (const event of stream) {\n  if (event.type === \"content_block_delta\" && event.delta.type === \"text_delta\") {\n    process.stdout.write(event.delta.text);\n  }\n}\n```\n\n## Tool Use\n\nDefine tools and let Claude call them:\n\n```python\ntools = [\n    {\n        \"name\": \"get_weather\",\n        \"description\": \"Get current weather for a location\",\n        \"input_schema\": {\n            \"type\": \"object\",\n            \"properties\": {\n                \"location\": {\"type\": \"string\", \"description\": \"City name\"},\n                \"unit\": {\"type\": \"string\", \"enum\": [\"celsius\", \"fahrenheit\"]}\n            },\n            \"required\": [\"location\"]\n        }\n    }\n]\n\nmessage = client.messages.create(\n    model=\"claude-sonnet-4-6\",\n    max_tokens=1024,\n    tools=tools,\n    messages=[{\"role\": \"user\", \"content\": \"What's the weather in SF?\"}]\n)\n\n# Handle tool use response\nfor block in message.content:\n    if block.type == \"tool_use\":\n        # Execute the tool with block.input\n        result = get_weather(**block.input)\n        # Send result back\n        follow_up = client.messages.create(\n            model=\"claude-sonnet-4-6\",\n            max_tokens=1024,\n            tools=tools,\n            messages=[\n                {\"role\": \"user\", \"content\": \"What's the weather in SF?\"},\n                {\"role\": \"assistant\", \"content\": message.content},\n                {\"role\": \"user\", \"content\": [\n                    {\"type\": \"tool_result\", \"tool_use_id\": block.id, \"content\": str(result)}\n                ]}\n            ]\n        )\n```\n\n## Vision\n\nSend images for analysis:\n\n```python\nimport base64\n\nwith open(\"diagram.png\", \"rb\") as f:\n    image_data = base64.standard_b64encode(f.read()).decode(\"utf-8\")\n\nmessage = client.messages.create(\n    model=\"claude-sonnet-4-6\",\n    max_tokens=1024,\n    messages=[{\n        \"role\": \"user\",\n        \"content\": [\n            {\"type\": \"image\", \"source\": {\"type\": \"base64\", \"media_type\": \"image\u002Fpng\", \"data\": image_data}},\n            {\"type\": \"text\", \"text\": \"Describe this diagram\"}\n        ]\n    }]\n)\n```\n\n## Extended Thinking\n\nFor complex reasoning tasks:\n\n```python\nmessage = client.messages.create(\n    model=\"claude-sonnet-4-6\",\n    max_tokens=16000,\n    thinking={\n        \"type\": \"enabled\",\n        \"budget_tokens\": 10000\n    },\n    messages=[{\"role\": \"user\", \"content\": \"Solve this math problem step by step...\"}]\n)\n\nfor block in message.content:\n    if block.type == \"thinking\":\n        print(f\"Thinking: {block.thinking}\")\n    elif block.type == \"text\":\n        print(f\"Answer: {block.text}\")\n```\n\n## Prompt Caching\n\nCache large system prompts or context to reduce costs:\n\n```python\nmessage = client.messages.create(\n    model=\"claude-sonnet-4-6\",\n    max_tokens=1024,\n    system=[\n        {\"type\": \"text\", \"text\": large_system_prompt, \"cache_control\": {\"type\": \"ephemeral\"}}\n    ],\n    messages=[{\"role\": \"user\", \"content\": \"Question about the cached context\"}]\n)\n# Check cache usage\nprint(f\"Cache read: {message.usage.cache_read_input_tokens}\")\nprint(f\"Cache creation: {message.usage.cache_creation_input_tokens}\")\n```\n\n## Batches API\n\nProcess large volumes asynchronously at 50% cost reduction:\n\n```python\nimport time\n\nbatch = client.messages.batches.create(\n    requests=[\n        {\n            \"custom_id\": f\"request-{i}\",\n            \"params\": {\n                \"model\": \"claude-sonnet-4-6\",\n                \"max_tokens\": 1024,\n                \"messages\": [{\"role\": \"user\", \"content\": prompt}]\n            }\n        }\n        for i, prompt in enumerate(prompts)\n    ]\n)\n\n# Poll for completion\nwhile True:\n    status = client.messages.batches.retrieve(batch.id)\n    if status.processing_status == \"ended\":\n        break\n    time.sleep(30)\n\n# Get results\nfor result in client.messages.batches.results(batch.id):\n    print(result.result.message.content[0].text)\n```\n\n## Claude Agent SDK\n\nBuild multi-step agents:\n\n```python\n# Note: Agent SDK API surface may change — check official docs\nimport anthropic\n\n# Define tools as functions\ntools = [{\n    \"name\": \"search_codebase\",\n    \"description\": \"Search the codebase for relevant code\",\n    \"input_schema\": {\n        \"type\": \"object\",\n        \"properties\": {\"query\": {\"type\": \"string\"}},\n        \"required\": [\"query\"]\n    }\n}]\n\n# Run an agentic loop with tool use\nclient = anthropic.Anthropic()\nmessages = [{\"role\": \"user\", \"content\": \"Review the auth module for security issues\"}]\n\nwhile True:\n    response = client.messages.create(\n        model=\"claude-sonnet-4-6\",\n        max_tokens=4096,\n        tools=tools,\n        messages=messages,\n    )\n    if response.stop_reason == \"end_turn\":\n        break\n    # Handle tool calls and continue the loop\n    messages.append({\"role\": \"assistant\", \"content\": response.content})\n    # ... execute tools and append tool_result messages\n```\n\n## Cost Optimization\n\n| Strategy | Savings | When to Use |\n|----------|---------|-------------|\n| Prompt caching | Up to 90% on cached tokens | Repeated system prompts or context |\n| Batches API | 50% | Non-time-sensitive bulk processing |\n| Haiku instead of Sonnet | ~75% | Simple tasks, classification, extraction |\n| Shorter max_tokens | Variable | When you know output will be short |\n| Streaming | None (same cost) | Better UX, same price |\n\n## Error Handling\n\n```python\nimport time\n\nfrom anthropic import APIError, RateLimitError, APIConnectionError\n\ntry:\n    message = client.messages.create(...)\nexcept RateLimitError:\n    # Back off and retry\n    time.sleep(60)\nexcept APIConnectionError:\n    # Network issue, retry with backoff\n    pass\nexcept APIError as e:\n    print(f\"API error {e.status_code}: {e.message}\")\n```\n\n## Environment Setup\n\n```bash\n# Required\nexport ANTHROPIC_API_KEY='[REDACTED_API_KEY]'\n\n# Optional: set default model\nexport ANTHROPIC_MODEL=\"claude-sonnet-4-6\"\n```\n\nNever hardcode API keys. 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