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Use when building applications with the Claude API or Anthropic SDKs.","hashgraph-online\u002Fawesome-codex-plugins","hashgraph-online","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F160371962?v=4","01M6CNB6NRJVNNGN14TR01Q6D3","HOL (Hashgraph Online)","01M6JT1HSKKG9V68KQAQGBA7XP",1571,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FColin4k1024\u002Ftsp\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",[32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52],"api","claude api","tool","when","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 4.1 | `claude-opus-4-1` | Complex reasoning, architecture, research |\n| Sonnet 4 | `claude-sonnet-4-0` | Balanced coding, most development tasks |\n| Haiku 3.5 | `claude-3-5-haiku-latest` | Fast responses, high-volume, cost-sensitive |\n\nDefault to Sonnet 4 unless the task requires deep reasoning (Opus) or speed\u002Fcost optimization (Haiku). For production, prefer pinned snapshot IDs over aliases.\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-4-0\",\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-4-0\",\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-4-0\",\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-4-0\",\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-4-0\",\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-4-0\",\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-4-0\",\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-4-0\",\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-4-0\",\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-4-0\",\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-4-0\",\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-4-0\",\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=\"your-api-key-here\"\n\n# Optional: set default model\nexport ANTHROPIC_MODEL=\"claude-sonnet-4-0\"\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 4.1 | `claude-opus-4-1` | Complex reasoning, architecture, research |\n| Sonnet 4 | `claude-sonnet-4-0` | Balanced coding, most development tasks |\n| Haiku 3.5 | `claude-3-5-haiku-latest` | Fast responses, high-volume, cost-sensitive |\n\nDefault to Sonnet 4 unless the task requires deep reasoning (Opus) or speed\u002Fcost optimization (Haiku). For production, prefer pinned snapshot IDs over aliases.\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-4-0\",\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-4-0\",\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-4-0\",\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-4-0\",\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-4-0\",\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-4-0\",\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-4-0\",\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-4-0\",\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-4-0\",\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-4-0\",\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-4-0\",\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-4-0\",\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=\"your-api-key-here\"\n\n# Optional: set default model\nexport ANTHROPIC_MODEL=\"claude-sonnet-4-0\"\n```\n\nNever hardcode API keys. 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Also drives a project's running app live over the e2e MCP server to look at a screen, check a UI change, take a screenshot or record a video, and try a locator; and runs bug bashes, parallel e2e explore runs with each bug proven by a repro test. Use when a project depends on e2e or has an e2e.config.ts, when asked for end-to-end, browser, mobile, or agentic UI tests, to check a change in the running app, to QA or bug bash an app or branch, or when an e2e run fails.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fe2e\u002FSKILL.md",[],[],{"skillNo":100,"name":101,"nameZh":6,"description":102,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":103,"categories":104,"keywords":105,"publishedAt":91},"01M746ZJN784HVHMDP9MQGCSGG","create-verification-skill","Generate a project-local verify-\u003Capp> skill on top of e2e, so any coding agent can launch the app, check it is worth driving, exercise a feature the way a user does, keep evidence, and bug bash it. Writes the skill, a feature map with one explore charter per feature, and proves it once. Use for \u002Fcreate-verification-skill, \"make a verification skill for this repo\", or when a project has no scripted way to prove UI behavior.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fcreate-verification-skill\u002FSKILL.md",[],[],{"skillNo":107,"name":108,"nameZh":6,"description":109,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":110,"categories":111,"keywords":112,"publishedAt":53},"01M716A6ETNB9B1SQ3GXZC75VR","deja-search","deja-vu memory — search the user's past AI coding sessions with the deja CLI. Holds what they already settled and never wrote into the repo (naming rules, banned or already-tried tools, required checks before merge, preferred values, who owns what). Use before your first edit in a task, before you call a change done or ready to merge, before you recommend a tool, library, design or reviewer, before re-debugging an error that may already be solved, when they say 'didn't we fix this before', 'what did we decide about X' or 'I already have that', and before telling them something on this machine does not exist.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-search\u002FSKILL.md",[],[113,114,115,116,117,118,119,120,121,122,123,35,124,125,126,127,128,129,130,131],"search","deja search","never","error","ready","memory","re","decide","fix","design","debugging","you","done","reviewer","io","change","cli","merge","past",{"skillNo":133,"name":134,"nameZh":6,"description":135,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":136,"categories":137,"keywords":138,"publishedAt":53},"01M716A3J2W5SENSE9VGMXB375","deja-reflect","End-of-session reflection and memory curation with deja-vu. Review what was decided, what commands broke and what repaired them, and record durable project rules or decisions with deja remember so the next session starts with them.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-reflect\u002FSKILL.md",[],[139,140,141,127,142,143,144,145,146,147,148,149,150,151,118,152,153,154,155,156],"deja reflect","reflect","review","with","record","github","next","https","node","Rules","decisions","brew","remember","project rules","project","session","end","reflection",{"skillNo":158,"name":159,"nameZh":6,"description":160,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":161,"categories":162,"keywords":168,"publishedAt":53},"01M715T3RR4BAYSR57HPPTJ0PF","revid","Make, edit, export and post AI videos with Revid (revid.ai) through the Revid MCP server. Use whenever the user wants a short-form or long-form video made (from an idea, a script, a URL, an article, a PDF, a tweet, a product page, their own clips, or a song), wants to find what is going viral on TikTok, YouTube, Instagram or X before making something, wants to edit or re-export an existing Revid project, generate a standalone AI image or clip, make a series episode, or publish and schedule a finished video to connected social accounts. Covers setup (hosted MCP with OAuth, or an API key), the 36 tools, credits, polling and the rules for publishing. Routes to the more specific revid-* skills when one fits.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftmakersas\u002Frevid-skills\u002Fskills\u002Frevid\u002FSKILL.md",[163,166,167],{"categoryNo":164,"categoryName":165,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[159,169,170,171,172,173,174,153,175,119,176,177,178,179,35,180,181,182,32,183],"URL","Video","short-form","standalone","PDF","viral","tiktok","short","social","MCP","song","image","mcp server","schedule","publish"]