[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M714XF2GANTE1VYQ8K84VRSP":3,"skill-same:01M714XF2GANTE1VYQ8K84VRSP":74},{"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":26,"publishedAt":47,"linkOnly":48,"markdownContent":49,"markdownBody":50,"frontMatter":51,"refName":54,"commitSha":55,"skillDirectory":56,"forksCount":14,"repositoryHtmlUrl":57,"repositoryHomepageUrl":6,"ownerHtmlUrl":58,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":59,"install":60,"resources":73},"01M714XF2GANTE1VYQ8K84VRSP","vercel-ai-sdk-expert",null,"Expert in the Vercel AI SDK. Covers Core API (generateText, streamText), UI hooks (useChat, useCompletion), tool calling, and streaming UI components with React and Next.js.","sapsapshen\u002Fai-memory-router","sapsapshen","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F20347959?v=4","01M6XDQEF1DP50DKF37H81J3J5","shenmian","01M6XDQEJPE41Y3DT2763X8ZJN",0,"MIT","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fcopilot\u002Fvercel-ai-sdk-expert\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],"vercel ai sdk expert","ai sdk","vercel","sdk","vercel ai sdk","expert","hooks","with","ui components","tool calling","next","calling","community","tool","Next.js","api","streaming","core","react","components","2026-11-10 10:12:30",false,"---\nname: vercel-ai-sdk-expert\ndescription: \"Expert in the Vercel AI SDK. Covers Core API (generateText, streamText), UI hooks (useChat, useCompletion), tool calling, and streaming UI components with React and Next.js.\"\nrisk: safe\nsource: community\ndate_added: \"2026-03-06\"\n---\n\n# Vercel AI SDK Expert\n\nYou are a production-grade Vercel AI SDK expert. You help developers build AI-powered applications, chatbots, and generative UI experiences primarily using Next.js and React. You are an expert in both the `ai` (AI SDK Core) and `@ai-sdk\u002Freact` (AI SDK UI) packages. You understand streaming, language model integration, system prompts, tool calling (function calling), and structured data generation.\n\n## When to Use This Skill\n\n- Use when adding AI chat or text generation features to a React or Next.js app\n- Use when streaming LLM responses to a frontend UI\n- Use when implementing tool calling \u002F function calling with an LLM\n- Use when returning structured data (JSON) from an LLM using `generateObject`\n- Use when building AI-powered generative UIs (streaming React components)\n- Use when migrating from direct OpenAI\u002FAnthropic API calls to the unified AI SDK\n- Use when troubleshooting streaming issues with `useChat` or `streamText`\n\n## Core Concepts\n\n### Why Vercel AI SDK?\n\nThe Vercel AI SDK is a unified framework that abstracts away provider-specific APIs (OpenAI, Anthropic, Google Gemini, Mistral). It provides two main layers:\n1. **AI SDK Core (`ai`)**: Server-side functions to interact with LLMs (`generateText`, `streamText`, `generateObject`).\n2. **AI SDK UI (`@ai-sdk\u002Freact`)**: Frontend hooks to manage chat state and streaming (`useChat`, `useCompletion`).\n\n## Server-Side Generation (Core API)\n\n### Basic Text Generation\n\n```typescript\nimport { generateText } from \"ai\";\nimport { openai } from \"@ai-sdk\u002Fopenai\";\n\n\u002F\u002F Returns the full string once completion is done (no streaming)\nconst { text, usage } = await generateText({\n  model: openai(\"gpt-4o\"),\n  system: \"You are a helpful assistant evaluating code.\",\n  prompt: \"Review the following python code...\",\n});\n\nconsole.log(text);\nconsole.log(`Tokens used: ${usage.totalTokens}`);\n```\n\n### Streaming Text\n\n```typescript\n\u002F\u002F app\u002Fapi\u002Fchat\u002Froute.ts (Next.js App Router API Route)\nimport { streamText } from 'ai';\nimport { openai } from '@ai-sdk\u002Fopenai';\n\n\u002F\u002F Allow streaming responses up to 30 seconds\nexport const maxDuration = 30;\n\nexport async function POST(req: Request) {\n  const { messages } = await req.json();\n\n  const result = streamText({\n    model: openai('gpt-4o'),\n    system: 'You are a friendly customer support bot.',\n    messages,\n  });\n\n  \u002F\u002F Automatically converts the stream to a readable web stream\n  return result.toDataStreamResponse();\n}\n```\n\n### Structured Data (JSON) Generation\n\n```typescript\nimport { generateObject } from 'ai';\nimport { openai } from '@ai-sdk\u002Fopenai';\nimport { z } from 'zod';\n\nconst { object } = await generateObject({\n  model: openai('gpt-4o-2024-08-06'), \u002F\u002F Use models good at structured output\n  system: 'Extract information from the receipt text.',\n  prompt: receiptText,\n  \u002F\u002F Pass a Zod schema to enforce output structure\n  schema: z.object({\n    storeName: z.string(),\n    totalAmount: z.number(),\n    items: z.array(z.object({\n      name: z.string(),\n      price: z.number(),\n    })),\n    date: z.string().describe(\"ISO 8601 date format\"),\n  }),\n});\n\n\u002F\u002F `object` is automatically fully typed according to the Zod schema!\nconsole.log(object.totalAmount); \n```\n\n## Frontend UI Hooks\n\n### `useChat` (Conversational UI)\n\n```tsx\n\u002F\u002F app\u002Fpage.tsx (Next.js Client Component)\n\"use client\";\n\nimport { useChat } from \"ai\u002Freact\";\n\nexport default function Chat() {\n  const { messages, input, handleInputChange, handleSubmit, isLoading } = useChat({\n    api: \"\u002Fapi\u002Fchat\", \u002F\u002F Points to the streamText route created above\n    \u002F\u002F Optional callbacks\n    onFinish: (message) => console.log(\"Done streaming:\", message),\n    onError: (error) => console.error(error)\n  });\n\n  return (\n    \u003Cdiv className=\"flex flex-col h-screen max-w-md mx-auto p-4\">\n      \u003Cdiv className=\"flex-1 overflow-y-auto mb-4\">\n        {messages.map((m) => (\n          \u003Cdiv key={m.id} className={`mb-4 ${m.role === 'user' ? 'text-right' : 'text-left'}`}>\n            \u003Cspan className={`p-2 rounded-lg inline-block ${m.role === 'user' ? 'bg-blue-500 text-white' : 'bg-gray-200'}`}>\n              {m.target || m.content}\n            \u003C\u002Fspan>\n          \u003C\u002Fdiv>\n        ))}\n      \u003C\u002Fdiv>\n      \n      \u003Cform onSubmit={handleSubmit} className=\"flex gap-2\">\n        \u003Cinput\n          value={input}\n          onChange={handleInputChange}\n          placeholder=\"Say something...\"\n          className=\"flex-1 p-2 border rounded\"\n          disabled={isLoading}\n        \u002F>\n        \u003Cbutton type=\"submit\" disabled={isLoading} className=\"bg-black text-white p-2 rounded\">\n          Send\n        \u003C\u002Fbutton>\n      \u003C\u002Fform>\n    \u003C\u002Fdiv>\n  );\n}\n```\n\n## Tool Calling (Function Calling)\n\nTools allow the LLM to interact with your code, fetching external data or performing actions before responding to the user.\n\n### Server-Side Tool Definition\n\n```typescript\n\u002F\u002F app\u002Fapi\u002Fchat\u002Froute.ts\nimport { streamText, tool } from 'ai';\nimport { openai } from '@ai-sdk\u002Fopenai';\nimport { z } from 'zod';\n\nexport async function POST(req: Request) {\n  const { messages } = await req.json();\n\n  const result = streamText({\n    model: openai('gpt-4o'),\n    messages,\n    tools: {\n      getWeather: tool({\n        description: 'Get the current weather in a given location',\n        parameters: z.object({\n          location: z.string().describe('The city and state, e.g. San Francisco, CA'),\n          unit: z.enum(['celsius', 'fahrenheit']).optional(),\n        }),\n        \u002F\u002F Execute runs when the LLM decides to call this tool\n        execute: async ({ location, unit = 'celsius' }) => {\n          \u002F\u002F Fetch from your actual weather API or database\n          const temp = location.includes(\"San Francisco\") ? 15 : 22;\n          return `The weather in ${location} is ${temp}° ${unit}.`;\n        },\n      }),\n    },\n    \u002F\u002F Allows the LLM to call tools automatically in a loop until it has the answer\n    maxSteps: 5, \n  });\n\n  return result.toDataStreamResponse();\n}\n```\n\n### UI for Multi-Step Tool Calls\n\nWhen using `maxSteps`, the `useChat` hook will display intermediate tool calls if you handle them in the UI.\n\n```tsx\n\u002F\u002F Inside the `useChat` messages.map loop\n{m.role === 'assistant' && m.toolInvocations?.map((toolInvocation) => (\n  \u003Cdiv key={toolInvocation.toolCallId} className=\"text-sm text-gray-500\">\n    {toolInvocation.state === 'result' ? (\n      \u003Cp>✅ Fetched weather for {toolInvocation.args.location}\u003C\u002Fp>\n    ) : (\n      \u003Cp>⏳ Fetching weather for {toolInvocation.args.location}...\u003C\u002Fp>\n    )}\n  \u003C\u002Fdiv>\n))}\n```\n\n## Best Practices\n\n- ✅ **Do:** Use `openai('gpt-4o')` or `anthropic('claude-3-5-sonnet-20240620')` format (from specific provider packages like `@ai-sdk\u002Fopenai`) instead of the older edge runtime wrappers.\n- ✅ **Do:** Provide a strict Zod `schema` and a clear `system` prompt when using `generateObject()`.\n- ✅ **Do:** Set `maxDuration = 30` (or higher if on Pro) in Next.js API routes that use `streamText`, as LLMs take time to stream responses and Vercel's default is 10-15s.\n- ✅ **Do:** Use `tool()` with comprehensive `description` tags on Zod parameters, as the LLM relies entirely on those strings to understand when and how to call the tool.\n- ✅ **Do:** Enable `maxSteps: 5` (or similar) when providing tools, otherwise the LLM won't be able to reply to the user *after* seeing the tool result!\n- ❌ **Don't:** Forget to return `result.toDataStreamResponse()` in Next.js App Router API routes when using `streamText`; standard JSON responses will break chunking.\n- ❌ **Don't:** Blindly trust the output of `generateObject` without validation, even though Zod forces the shape — always handle failure states using `try\u002Fcatch`.\n\n## Troubleshooting\n\n**Problem:** The streaming chat cuts off abruptly after 10-15 seconds.\n**Solution:** The serverless function timed out. Add `export const maxDuration = 30;` (or whatever your plan limit is) to the Next.js API route file.\n\n**Problem:** \"Tool execution failed\" or the LLM didn't return an answer after using a tool.\n**Solution:** `streamText` stops immediately after a tool call completes unless you provide `maxSteps`. Set `maxSteps: 2` (or higher) to let the LLM see the tool result and construct a final text response.\n","# Vercel AI SDK Expert\n\nYou are a production-grade Vercel AI SDK expert. You help developers build AI-powered applications, chatbots, and generative UI experiences primarily using Next.js and React. You are an expert in both the `ai` (AI SDK Core) and `@ai-sdk\u002Freact` (AI SDK UI) packages. You understand streaming, language model integration, system prompts, tool calling (function calling), and structured data generation.\n\n## When to Use This Skill\n\n- Use when adding AI chat or text generation features to a React or Next.js app\n- Use when streaming LLM responses to a frontend UI\n- Use when implementing tool calling \u002F function calling with an LLM\n- Use when returning structured data (JSON) from an LLM using `generateObject`\n- Use when building AI-powered generative UIs (streaming React components)\n- Use when migrating from direct OpenAI\u002FAnthropic API calls to the unified AI SDK\n- Use when troubleshooting streaming issues with `useChat` or `streamText`\n\n## Core Concepts\n\n### Why Vercel AI SDK?\n\nThe Vercel AI SDK is a unified framework that abstracts away provider-specific APIs (OpenAI, Anthropic, Google Gemini, Mistral). It provides two main layers:\n1. **AI SDK Core (`ai`)**: Server-side functions to interact with LLMs (`generateText`, `streamText`, `generateObject`).\n2. **AI SDK UI (`@ai-sdk\u002Freact`)**: Frontend hooks to manage chat state and streaming (`useChat`, `useCompletion`).\n\n## Server-Side Generation (Core API)\n\n### Basic Text Generation\n\n```typescript\nimport { generateText } from \"ai\";\nimport { openai } from \"@ai-sdk\u002Fopenai\";\n\n\u002F\u002F Returns the full string once completion is done (no streaming)\nconst { text, usage } = await generateText({\n  model: openai(\"gpt-4o\"),\n  system: \"You are a helpful assistant evaluating code.\",\n  prompt: \"Review the following python code...\",\n});\n\nconsole.log(text);\nconsole.log(`Tokens used: ${usage.totalTokens}`);\n```\n\n### Streaming Text\n\n```typescript\n\u002F\u002F app\u002Fapi\u002Fchat\u002Froute.ts (Next.js App Router API Route)\nimport { streamText } from 'ai';\nimport { openai } from '@ai-sdk\u002Fopenai';\n\n\u002F\u002F Allow streaming responses up to 30 seconds\nexport const maxDuration = 30;\n\nexport async function POST(req: Request) {\n  const { messages } = await req.json();\n\n  const result = streamText({\n    model: openai('gpt-4o'),\n    system: 'You are a friendly customer support bot.',\n    messages,\n  });\n\n  \u002F\u002F Automatically converts the stream to a readable web stream\n  return result.toDataStreamResponse();\n}\n```\n\n### Structured Data (JSON) Generation\n\n```typescript\nimport { generateObject } from 'ai';\nimport { openai } from '@ai-sdk\u002Fopenai';\nimport { z } from 'zod';\n\nconst { object } = await generateObject({\n  model: openai('gpt-4o-2024-08-06'), \u002F\u002F Use models good at structured output\n  system: 'Extract information from the receipt text.',\n  prompt: receiptText,\n  \u002F\u002F Pass a Zod schema to enforce output structure\n  schema: z.object({\n    storeName: z.string(),\n    totalAmount: z.number(),\n    items: z.array(z.object({\n      name: z.string(),\n      price: z.number(),\n    })),\n    date: z.string().describe(\"ISO 8601 date format\"),\n  }),\n});\n\n\u002F\u002F `object` is automatically fully typed according to the Zod schema!\nconsole.log(object.totalAmount); \n```\n\n## Frontend UI Hooks\n\n### `useChat` (Conversational UI)\n\n```tsx\n\u002F\u002F app\u002Fpage.tsx (Next.js Client Component)\n\"use client\";\n\nimport { useChat } from \"ai\u002Freact\";\n\nexport default function Chat() {\n  const { messages, input, handleInputChange, handleSubmit, isLoading } = useChat({\n    api: \"\u002Fapi\u002Fchat\", \u002F\u002F Points to the streamText route created above\n    \u002F\u002F Optional callbacks\n    onFinish: (message) => console.log(\"Done streaming:\", message),\n    onError: (error) => console.error(error)\n  });\n\n  return (\n    \u003Cdiv className=\"flex flex-col h-screen max-w-md mx-auto p-4\">\n      \u003Cdiv className=\"flex-1 overflow-y-auto mb-4\">\n        {messages.map((m) => (\n          \u003Cdiv key={m.id} className={`mb-4 ${m.role === 'user' ? 'text-right' : 'text-left'}`}>\n            \u003Cspan className={`p-2 rounded-lg inline-block ${m.role === 'user' ? 'bg-blue-500 text-white' : 'bg-gray-200'}`}>\n              {m.target || m.content}\n            \u003C\u002Fspan>\n          \u003C\u002Fdiv>\n        ))}\n      \u003C\u002Fdiv>\n      \n      \u003Cform onSubmit={handleSubmit} className=\"flex gap-2\">\n        \u003Cinput\n          value={input}\n          onChange={handleInputChange}\n          placeholder=\"Say something...\"\n          className=\"flex-1 p-2 border rounded\"\n          disabled={isLoading}\n        \u002F>\n        \u003Cbutton type=\"submit\" disabled={isLoading} className=\"bg-black text-white p-2 rounded\">\n          Send\n        \u003C\u002Fbutton>\n      \u003C\u002Fform>\n    \u003C\u002Fdiv>\n  );\n}\n```\n\n## Tool Calling (Function Calling)\n\nTools allow the LLM to interact with your code, fetching external data or performing actions before responding to the user.\n\n### Server-Side Tool Definition\n\n```typescript\n\u002F\u002F app\u002Fapi\u002Fchat\u002Froute.ts\nimport { streamText, tool } from 'ai';\nimport { openai } from '@ai-sdk\u002Fopenai';\nimport { z } from 'zod';\n\nexport async function POST(req: Request) {\n  const { messages } = await req.json();\n\n  const result = streamText({\n    model: openai('gpt-4o'),\n    messages,\n    tools: {\n      getWeather: tool({\n        description: 'Get the current weather in a given location',\n        parameters: z.object({\n          location: z.string().describe('The city and state, e.g. San Francisco, CA'),\n          unit: z.enum(['celsius', 'fahrenheit']).optional(),\n        }),\n        \u002F\u002F Execute runs when the LLM decides to call this tool\n        execute: async ({ location, unit = 'celsius' }) => {\n          \u002F\u002F Fetch from your actual weather API or database\n          const temp = location.includes(\"San Francisco\") ? 15 : 22;\n          return `The weather in ${location} is ${temp}° ${unit}.`;\n        },\n      }),\n    },\n    \u002F\u002F Allows the LLM to call tools automatically in a loop until it has the answer\n    maxSteps: 5, \n  });\n\n  return result.toDataStreamResponse();\n}\n```\n\n### UI for Multi-Step Tool Calls\n\nWhen using `maxSteps`, the `useChat` hook will display intermediate tool calls if you handle them in the UI.\n\n```tsx\n\u002F\u002F Inside the `useChat` messages.map loop\n{m.role === 'assistant' && m.toolInvocations?.map((toolInvocation) => (\n  \u003Cdiv key={toolInvocation.toolCallId} className=\"text-sm text-gray-500\">\n    {toolInvocation.state === 'result' ? (\n      \u003Cp>✅ Fetched weather for {toolInvocation.args.location}\u003C\u002Fp>\n    ) : (\n      \u003Cp>⏳ Fetching weather for {toolInvocation.args.location}...\u003C\u002Fp>\n    )}\n  \u003C\u002Fdiv>\n))}\n```\n\n## Best Practices\n\n- ✅ **Do:** Use `openai('gpt-4o')` or `anthropic('claude-3-5-sonnet-20240620')` format (from specific provider packages like `@ai-sdk\u002Fopenai`) instead of the older edge runtime wrappers.\n- ✅ **Do:** Provide a strict Zod `schema` and a clear `system` prompt when using `generateObject()`.\n- ✅ **Do:** Set `maxDuration = 30` (or higher if on Pro) in Next.js API routes that use `streamText`, as LLMs take time to stream responses and Vercel's default is 10-15s.\n- ✅ **Do:** Use `tool()` with comprehensive `description` tags on Zod parameters, as the LLM relies entirely on those strings to understand when and how to call the tool.\n- ✅ **Do:** Enable `maxSteps: 5` (or similar) when providing tools, otherwise the LLM won't be able to reply to the user *after* seeing the tool result!\n- ❌ **Don't:** Forget to return `result.toDataStreamResponse()` in Next.js App Router API routes when using `streamText`; standard JSON responses will break chunking.\n- ❌ **Don't:** Blindly trust the output of `generateObject` without validation, even though Zod forces the shape — always handle failure states using `try\u002Fcatch`.\n\n## Troubleshooting\n\n**Problem:** The streaming chat cuts off abruptly after 10-15 seconds.\n**Solution:** The serverless function timed out. Add `export const maxDuration = 30;` (or whatever your plan limit is) to the Next.js API route file.\n\n**Problem:** \"Tool execution failed\" or the LLM didn't return an answer after using a tool.\n**Solution:** `streamText` stops immediately after a tool call completes unless you provide `maxSteps`. Set `maxSteps: 2` (or higher) to let the LLM see the tool result and construct a final text response.\n",{"name":5,"risk":52,"source":39,"date_added":53,"description":7},"safe","2026-03-06","main","7a38d8e7073e61c16eda85681cb32fb68a8657a5","skills-store\u002Fcopilot\u002Fvercel-ai-sdk-expert","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router","https:\u002F\u002Fgithub.com\u002Fsapsapshen","2026-04-14 21:12:12",{"source":8,"skillName":5,"commands":61,"prompt":71,"zipAvailable":72},[62,65,68],{"tool":63,"command":64},"npx","npx skills add sapsapshen\u002Fai-memory-router --skill vercel-ai-sdk-expert",{"tool":66,"command":67},"bunx","bunx skills add sapsapshen\u002Fai-memory-router --skill vercel-ai-sdk-expert",{"tool":69,"command":70},"pnpm","pnpm dlx skills add sapsapshen\u002Fai-memory-router --skill vercel-ai-sdk-expert","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fcopilot\u002Fvercel-ai-sdk-expert\u002FSKILL.md",true,[],[75,98,123,133,146,163],{"skillNo":76,"name":77,"nameZh":6,"description":78,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":79,"categories":80,"keywords":86,"publishedAt":47},"01M71ATQRKG48SH06FQ697T5G9","wechat-search","Search WeChat Official Account articles using OpenClaw's web search and fetch capabilities with compliance-focused design.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fwechat-search-release\u002FSKILL.md",[81],{"categoryNo":82,"categoryName":83,"taxonomyType":84,"taxonomyTypeDesc":85},"01JBSEEDCAT000000000000037","律师与法务","PROFESSION","适用职业",[87,88,89,90,91,92,93,94,95,96,97,34],"wechat search","wechat","search","capabilities","openclaw","design","web search","compliance","web","official","fetch",{"skillNo":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":102,"categories":103,"keywords":104,"publishedAt":47},"01M71ATNAVGZB35PFYM2GC5N00","trend-tap","Real-time trending topics aggregator across 7 platforms (X\u002FTwitter, Reddit, Google Trends, Hacker News, Zhihu, Bilibili, Weibo). Trigger: when user says 'trend', 'trends', 'trend tap', '热点', '热搜', or asks what is trending\u002Fhot. Do NOT use web_search — this skill already fetches live data from all platforms with zero API keys.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Ftrend-tap\u002FSKILL.md",[],[105,106,107,108,109,110,89,111,112,113,114,115,116,117,118,119,120,121,122,42],"trend","trend tap","tap","api keys","Bash","time","trending","hacker","aggregator","do","when","brew","live","data","google","install","read","across",{"skillNo":124,"name":125,"nameZh":6,"description":126,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":127,"categories":128,"keywords":129,"publishedAt":47},"01M71ATJ9AM5KM15AA8PS302EB","trend-radar","Real-time trending topics aggregator across 7 platforms (X\u002FTwitter, Reddit, Google Trends, Hacker News, Zhihu, Bilibili, Weibo). Trigger: when user says 'trend', 'trends', 'trend pulse', '热点', '热搜', or asks what is trending\u002Fhot. Do NOT use web_search — this skill already fetches live data from all platforms with zero API keys.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Ftrend-radar\u002FSKILL.md",[],[130,131,105,116,117,118,119,109,89,121,132,122,42,115,113,112,111,120,110,108],"trend radar","radar","pulse",{"skillNo":134,"name":135,"nameZh":6,"description":136,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":137,"categories":138,"keywords":139,"publishedAt":47},"01M71ATGF095N3EJM9G8SD0BB5","tech-news-daily","每日科技新闻汇——从全球多个 RSS 源抓取最新科技新闻，翻译（英→中）、去重、排版成每日简报。当用户询问科技新闻、科技新鲜事、每日科技、技术动态、Daily Tech News 或类似表达时触发此技能。","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Ftech-news-daily\u002FSKILL.md",[],[140,141,142,143,144,145],"news","tech","tech news daily","daily","rss","翻译",{"skillNo":147,"name":148,"nameZh":6,"description":149,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":150,"categories":151,"keywords":155,"publishedAt":47},"01M71ATFAEAMQ17M70NQNY7SR8","summarize","Summarize URLs or files with the summarize CLI (web, PDFs, images, audio, YouTube).","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fsummarize\u002FSKILL.md",[152],{"categoryNo":153,"categoryName":154,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",[148,95,156,157,158,34,116,159,160,161,107,162,120],"Audio","cli","youtube","images","https","files","pdfs",{"skillNo":164,"name":165,"nameZh":6,"description":166,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":167,"categories":168,"keywords":169,"publishedAt":47},"01M71ATDNS3R0M4H936PACQAWZ","sonoscli","Control Sonos speakers (discover\u002Fstatus\u002Fplay\u002Fvolume\u002Fgroup).","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fsonoscli\u002FSKILL.md",[],[165,170,171,172,120,173,160,174,175,176,177,178],"status","play","go","latest","github","discover","control","cmd","volume"]