[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M69AVHQFJM4E4BPD8G5B76RF":3,"skill-same:01M69AVHQFJM4E4BPD8G5B76RF":105},{"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":23,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":54,"repositoryHtmlUrl":55,"repositoryHomepageUrl":56,"ownerHtmlUrl":57,"ownerBlogUrl":58,"ownerDescription":59,"updatedAt":60,"install":61,"resources":74},"01M69AVHQFJM4E4BPD8G5B76RF","azure-ai-translation-ts",null,"Build translation applications using Azure Translation SDKs for JavaScript (@azure-rest\u002Fai-translation-text, @azure-rest\u002Fai-translation-document). Use when implementing text translation, transliteration, language detection, or batch document translation.","microsoft\u002Fskills","microsoft","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6154722?v=4","01M698VYDXZ6JW5WH3CRV650R8","Microsoft","01M69AJYYW4E1RMSYCQDJ4FJSH",3195,"MIT","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fplugins\u002Fazure-sdk-typescript\u002Fskills\u002Fazure-ai-translation-ts\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000002","文档处理","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,9],"azure ai","azure","azure ai translation ts","ai translation","translation","use","when","for","implementing","sdks","ai-translation","build","document","javascript","text","applications","detection","REST","batch","2026-11-10 10:12:30",false,"---\nname: azure-ai-translation-ts\ndescription: Build translation applications using Azure Translation SDKs for JavaScript (@azure-rest\u002Fai-translation-text, @azure-rest\u002Fai-translation-document). Use when implementing text translation, transliteration, language detection, or batch document translation.\nlicense: MIT\nmetadata:\n  author: Microsoft\n  version: \"1.0.0\"\n  package: '@azure-rest\u002Fai-translation-text, @azure-rest\u002Fai-translation-document'\n---\n\n# Azure Translation SDKs for TypeScript\n\nText and document translation with REST-style clients.\n\n## Installation\n\n```bash\n# Text translation\nnpm install @azure-rest\u002Fai-translation-text @azure\u002Fidentity\n\n# Document translation\nnpm install @azure-rest\u002Fai-translation-document @azure\u002Fidentity\n```\n\n## Environment Variables\n\n```bash\nTRANSLATOR_ENDPOINT=https:\u002F\u002Fapi.cognitive.microsofttranslator.com\nTRANSLATOR_SUBSCRIPTION_KEY=\u003Cyour-api-key>\nTRANSLATOR_REGION=\u003Cyour-region>  # e.g., westus, eastus\nAZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production\n```\n\n## Text Translation Client\n\n### Authentication\n\n```typescript\nimport TextTranslationClient, { TranslatorCredential } from \"@azure-rest\u002Fai-translation-text\";\n\n\u002F\u002F API Key + Region\nconst credential: TranslatorCredential = {\n  key: process.env.TRANSLATOR_SUBSCRIPTION_KEY!,\n  region: process.env.TRANSLATOR_REGION!,\n};\nconst client = TextTranslationClient(process.env.TRANSLATOR_ENDPOINT!, credential);\n\n\u002F\u002F Or just credential (uses global endpoint)\nconst client2 = TextTranslationClient(credential);\n```\n\n### Translate Text\n\n```typescript\nimport TextTranslationClient, { isUnexpected } from \"@azure-rest\u002Fai-translation-text\";\n\nconst response = await client.path(\"\u002Ftranslate\").post({\n  body: {\n    inputs: [\n      {\n        text: \"Hello, how are you?\",\n        language: \"en\",  \u002F\u002F source (optional, auto-detect)\n        targets: [\n          { language: \"es\" },\n          { language: \"fr\" },\n        ],\n      },\n    ],\n  },\n});\n\nif (isUnexpected(response)) {\n  throw response.body.error;\n}\n\nfor (const result of response.body.value) {\n  for (const translation of result.translations) {\n    console.log(`${translation.language}: ${translation.text}`);\n  }\n}\n```\n\n### Translate with Options\n\n```typescript\nconst response = await client.path(\"\u002Ftranslate\").post({\n  body: {\n    inputs: [\n      {\n        text: \"Hello world\",\n        language: \"en\",\n        textType: \"Plain\",  \u002F\u002F or \"Html\"\n        targets: [\n          {\n            language: \"de\",\n            profanityAction: \"NoAction\",  \u002F\u002F \"Marked\" | \"Deleted\"\n            tone: \"formal\",  \u002F\u002F LLM-specific\n          },\n        ],\n      },\n    ],\n  },\n});\n```\n\n### Get Supported Languages\n\n```typescript\nconst response = await client.path(\"\u002Flanguages\").get();\n\nif (isUnexpected(response)) {\n  throw response.body.error;\n}\n\n\u002F\u002F Translation languages\nfor (const [code, lang] of Object.entries(response.body.translation || {})) {\n  console.log(`${code}: ${lang.name} (${lang.nativeName})`);\n}\n```\n\n### Transliterate\n\n```typescript\nconst response = await client.path(\"\u002Ftransliterate\").post({\n  body: { inputs: [{ text: \"这是个测试\" }] },\n  queryParameters: {\n    language: \"zh-Hans\",\n    fromScript: \"Hans\",\n    toScript: \"Latn\",\n  },\n});\n\nif (!isUnexpected(response)) {\n  for (const t of response.body.value) {\n    console.log(`${t.script}: ${t.text}`);  \u002F\u002F Latn: zhè shì gè cè shì\n  }\n}\n```\n\n### Detect Language\n\n```typescript\nconst response = await client.path(\"\u002Fdetect\").post({\n  body: { inputs: [{ text: \"Bonjour le monde\" }] },\n});\n\nif (!isUnexpected(response)) {\n  for (const result of response.body.value) {\n    console.log(`Language: ${result.language}, Score: ${result.score}`);\n  }\n}\n```\n\n## Document Translation Client\n\n### Authentication\n\n```typescript\nimport DocumentTranslationClient from \"@azure-rest\u002Fai-translation-document\";\nimport { DefaultAzureCredential, ManagedIdentityCredential } from \"@azure\u002Fidentity\";\n\nconst endpoint = \"https:\u002F\u002F\u003Ctranslator>.cognitiveservices.azure.com\";\n\n\u002F\u002F Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=\u003Cspecific_credential>\nconst credential = new DefaultAzureCredential({requiredEnvVars: [\"AZURE_TOKEN_CREDENTIALS\"]});\n\u002F\u002F Or use a specific credential directly in production:\n\u002F\u002F See https:\u002F\u002Flearn.microsoft.com\u002Fjavascript\u002Fapi\u002Foverview\u002Fazure\u002Fidentity-readme?view=azure-node-latest#credential-classes\n\u002F\u002F const credential = new ManagedIdentityCredential();\n\n\u002F\u002F TokenCredential\nconst client = DocumentTranslationClient(endpoint, credential);\n\n\u002F\u002F API Key\nconst client2 = DocumentTranslationClient(endpoint, { key: \"\u003Capi-key>\" });\n```\n\n### Single Document Translation\n\n```typescript\nimport DocumentTranslationClient from \"@azure-rest\u002Fai-translation-document\";\nimport { writeFile } from \"node:fs\u002Fpromises\";\n\nconst response = await client.path(\"\u002Fdocument:translate\").post({\n  queryParameters: {\n    targetLanguage: \"es\",\n    sourceLanguage: \"en\",  \u002F\u002F optional\n  },\n  contentType: \"multipart\u002Fform-data\",\n  body: [\n    {\n      name: \"document\",\n      body: \"Hello, this is a test document.\",\n      filename: \"test.txt\",\n      contentType: \"text\u002Fplain\",\n    },\n  ],\n}).asNodeStream();\n\nif (response.status === \"200\") {\n  await writeFile(\"translated.txt\", response.body);\n}\n```\n\n### Batch Document Translation\n\n```typescript\nimport { ContainerSASPermissions, BlobServiceClient } from \"@azure\u002Fstorage-blob\";\n\n\u002F\u002F Generate SAS URLs for source and target containers\nconst sourceSas = await sourceContainer.generateSasUrl({\n  permissions: ContainerSASPermissions.parse(\"rl\"),\n  expiresOn: new Date(Date.now() + 24 * 60 * 60 * 1000),\n});\n\nconst targetSas = await targetContainer.generateSasUrl({\n  permissions: ContainerSASPermissions.parse(\"rwl\"),\n  expiresOn: new Date(Date.now() + 24 * 60 * 60 * 1000),\n});\n\n\u002F\u002F Start batch translation\nconst response = await client.path(\"\u002Fdocument\u002Fbatches\").post({\n  body: {\n    inputs: [\n      {\n        source: { sourceUrl: sourceSas },\n        targets: [\n          { targetUrl: targetSas, language: \"fr\" },\n        ],\n      },\n    ],\n  },\n});\n\n\u002F\u002F Get operation ID from header\nconst operationId = new URL(response.headers[\"operation-location\"])\n  .pathname.split(\"\u002F\").pop();\n```\n\n### Get Translation Status\n\n```typescript\nimport { isUnexpected, paginate } from \"@azure-rest\u002Fai-translation-document\";\n\nconst statusResponse = await client.path(\"\u002Fdocument\u002Fbatches\u002F{id}\", operationId).get();\n\nif (!isUnexpected(statusResponse)) {\n  const status = statusResponse.body;\n  console.log(`Status: ${status.status}`);\n  console.log(`Total: ${status.summary.total}`);\n  console.log(`Success: ${status.summary.success}`);\n}\n\n\u002F\u002F List documents with pagination\nconst docsResponse = await client.path(\"\u002Fdocument\u002Fbatches\u002F{id}\u002Fdocuments\", operationId).get();\nconst documents = paginate(client, docsResponse);\n\nfor await (const doc of documents) {\n  console.log(`${doc.id}: ${doc.status}`);\n}\n```\n\n### Get Supported Formats\n\n```typescript\nconst response = await client.path(\"\u002Fdocument\u002Fformats\").get();\n\nif (!isUnexpected(response)) {\n  for (const format of response.body.value) {\n    console.log(`${format.format}: ${format.fileExtensions.join(\", \")}`);\n  }\n}\n```\n\n## Key Types\n\n```typescript\n\u002F\u002F Text Translation\nimport type {\n  TranslatorCredential,\n  TranslatorTokenCredential,\n} from \"@azure-rest\u002Fai-translation-text\";\n\n\u002F\u002F Document Translation\nimport type {\n  DocumentTranslateParameters,\n  StartTranslationDetails,\n  TranslationStatus,\n} from \"@azure-rest\u002Fai-translation-document\";\n```\n\n## Best Practices\n\n1. **Auto-detect source** - Omit `language` parameter to auto-detect\n2. **Batch requests** - Translate multiple texts in one call for efficiency\n3. **Use SAS tokens** - For document translation, use time-limited SAS URLs\n4. **Handle errors** - Always check `isUnexpected(response)` before accessing body\n5. **Regional endpoints** - Use regional endpoints for lower latency\n","# Azure Translation SDKs for TypeScript\n\nText and document translation with REST-style clients.\n\n## Installation\n\n```bash\n# Text translation\nnpm install @azure-rest\u002Fai-translation-text @azure\u002Fidentity\n\n# Document translation\nnpm install @azure-rest\u002Fai-translation-document @azure\u002Fidentity\n```\n\n## Environment Variables\n\n```bash\nTRANSLATOR_ENDPOINT=https:\u002F\u002Fapi.cognitive.microsofttranslator.com\nTRANSLATOR_SUBSCRIPTION_KEY=\u003Cyour-api-key>\nTRANSLATOR_REGION=\u003Cyour-region>  # e.g., westus, eastus\nAZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production\n```\n\n## Text Translation Client\n\n### Authentication\n\n```typescript\nimport TextTranslationClient, { TranslatorCredential } from \"@azure-rest\u002Fai-translation-text\";\n\n\u002F\u002F API Key + Region\nconst credential: TranslatorCredential = {\n  key: process.env.TRANSLATOR_SUBSCRIPTION_KEY!,\n  region: process.env.TRANSLATOR_REGION!,\n};\nconst client = TextTranslationClient(process.env.TRANSLATOR_ENDPOINT!, credential);\n\n\u002F\u002F Or just credential (uses global endpoint)\nconst client2 = TextTranslationClient(credential);\n```\n\n### Translate Text\n\n```typescript\nimport TextTranslationClient, { isUnexpected } from \"@azure-rest\u002Fai-translation-text\";\n\nconst response = await client.path(\"\u002Ftranslate\").post({\n  body: {\n    inputs: [\n      {\n        text: \"Hello, how are you?\",\n        language: \"en\",  \u002F\u002F source (optional, auto-detect)\n        targets: [\n          { language: \"es\" },\n          { language: \"fr\" },\n        ],\n      },\n    ],\n  },\n});\n\nif (isUnexpected(response)) {\n  throw response.body.error;\n}\n\nfor (const result of response.body.value) {\n  for (const translation of result.translations) {\n    console.log(`${translation.language}: ${translation.text}`);\n  }\n}\n```\n\n### Translate with Options\n\n```typescript\nconst response = await client.path(\"\u002Ftranslate\").post({\n  body: {\n    inputs: [\n      {\n        text: \"Hello world\",\n        language: \"en\",\n        textType: \"Plain\",  \u002F\u002F or \"Html\"\n        targets: [\n          {\n            language: \"de\",\n            profanityAction: \"NoAction\",  \u002F\u002F \"Marked\" | \"Deleted\"\n            tone: \"formal\",  \u002F\u002F LLM-specific\n          },\n        ],\n      },\n    ],\n  },\n});\n```\n\n### Get Supported Languages\n\n```typescript\nconst response = await client.path(\"\u002Flanguages\").get();\n\nif (isUnexpected(response)) {\n  throw response.body.error;\n}\n\n\u002F\u002F Translation languages\nfor (const [code, lang] of Object.entries(response.body.translation || {})) {\n  console.log(`${code}: ${lang.name} (${lang.nativeName})`);\n}\n```\n\n### Transliterate\n\n```typescript\nconst response = await client.path(\"\u002Ftransliterate\").post({\n  body: { inputs: [{ text: \"这是个测试\" }] },\n  queryParameters: {\n    language: \"zh-Hans\",\n    fromScript: \"Hans\",\n    toScript: \"Latn\",\n  },\n});\n\nif (!isUnexpected(response)) {\n  for (const t of response.body.value) {\n    console.log(`${t.script}: ${t.text}`);  \u002F\u002F Latn: zhè shì gè cè shì\n  }\n}\n```\n\n### Detect Language\n\n```typescript\nconst response = await client.path(\"\u002Fdetect\").post({\n  body: { inputs: [{ text: \"Bonjour le monde\" }] },\n});\n\nif (!isUnexpected(response)) {\n  for (const result of response.body.value) {\n    console.log(`Language: ${result.language}, Score: ${result.score}`);\n  }\n}\n```\n\n## Document Translation Client\n\n### Authentication\n\n```typescript\nimport DocumentTranslationClient from \"@azure-rest\u002Fai-translation-document\";\nimport { DefaultAzureCredential, ManagedIdentityCredential } from \"@azure\u002Fidentity\";\n\nconst endpoint = \"https:\u002F\u002F\u003Ctranslator>.cognitiveservices.azure.com\";\n\n\u002F\u002F Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=\u003Cspecific_credential>\nconst credential = new DefaultAzureCredential({requiredEnvVars: [\"AZURE_TOKEN_CREDENTIALS\"]});\n\u002F\u002F Or use a specific credential directly in production:\n\u002F\u002F See https:\u002F\u002Flearn.microsoft.com\u002Fjavascript\u002Fapi\u002Foverview\u002Fazure\u002Fidentity-readme?view=azure-node-latest#credential-classes\n\u002F\u002F const credential = new ManagedIdentityCredential();\n\n\u002F\u002F TokenCredential\nconst client = DocumentTranslationClient(endpoint, credential);\n\n\u002F\u002F API Key\nconst client2 = DocumentTranslationClient(endpoint, { key: \"\u003Capi-key>\" });\n```\n\n### Single Document Translation\n\n```typescript\nimport DocumentTranslationClient from \"@azure-rest\u002Fai-translation-document\";\nimport { writeFile } from \"node:fs\u002Fpromises\";\n\nconst response = await client.path(\"\u002Fdocument:translate\").post({\n  queryParameters: {\n    targetLanguage: \"es\",\n    sourceLanguage: \"en\",  \u002F\u002F optional\n  },\n  contentType: \"multipart\u002Fform-data\",\n  body: [\n    {\n      name: \"document\",\n      body: \"Hello, this is a test document.\",\n      filename: \"test.txt\",\n      contentType: \"text\u002Fplain\",\n    },\n  ],\n}).asNodeStream();\n\nif (response.status === \"200\") {\n  await writeFile(\"translated.txt\", response.body);\n}\n```\n\n### Batch Document Translation\n\n```typescript\nimport { ContainerSASPermissions, BlobServiceClient } from \"@azure\u002Fstorage-blob\";\n\n\u002F\u002F Generate SAS URLs for source and target containers\nconst sourceSas = await sourceContainer.generateSasUrl({\n  permissions: ContainerSASPermissions.parse(\"rl\"),\n  expiresOn: new Date(Date.now() + 24 * 60 * 60 * 1000),\n});\n\nconst targetSas = await targetContainer.generateSasUrl({\n  permissions: ContainerSASPermissions.parse(\"rwl\"),\n  expiresOn: new Date(Date.now() + 24 * 60 * 60 * 1000),\n});\n\n\u002F\u002F Start batch translation\nconst response = await client.path(\"\u002Fdocument\u002Fbatches\").post({\n  body: {\n    inputs: [\n      {\n        source: { sourceUrl: sourceSas },\n        targets: [\n          { targetUrl: targetSas, language: \"fr\" },\n        ],\n      },\n    ],\n  },\n});\n\n\u002F\u002F Get operation ID from header\nconst operationId = new URL(response.headers[\"operation-location\"])\n  .pathname.split(\"\u002F\").pop();\n```\n\n### Get Translation Status\n\n```typescript\nimport { isUnexpected, paginate } from \"@azure-rest\u002Fai-translation-document\";\n\nconst statusResponse = await client.path(\"\u002Fdocument\u002Fbatches\u002F{id}\", operationId).get();\n\nif (!isUnexpected(statusResponse)) {\n  const status = statusResponse.body;\n  console.log(`Status: ${status.status}`);\n  console.log(`Total: ${status.summary.total}`);\n  console.log(`Success: ${status.summary.success}`);\n}\n\n\u002F\u002F List documents with pagination\nconst docsResponse = await client.path(\"\u002Fdocument\u002Fbatches\u002F{id}\u002Fdocuments\", operationId).get();\nconst documents = paginate(client, docsResponse);\n\nfor await (const doc of documents) {\n  console.log(`${doc.id}: ${doc.status}`);\n}\n```\n\n### Get Supported Formats\n\n```typescript\nconst response = await client.path(\"\u002Fdocument\u002Fformats\").get();\n\nif (!isUnexpected(response)) {\n  for (const format of response.body.value) {\n    console.log(`${format.format}: ${format.fileExtensions.join(\", \")}`);\n  }\n}\n```\n\n## Key Types\n\n```typescript\n\u002F\u002F Text Translation\nimport type {\n  TranslatorCredential,\n  TranslatorTokenCredential,\n} from \"@azure-rest\u002Fai-translation-text\";\n\n\u002F\u002F Document Translation\nimport type {\n  DocumentTranslateParameters,\n  StartTranslationDetails,\n  TranslationStatus,\n} from \"@azure-rest\u002Fai-translation-document\";\n```\n\n## Best Practices\n\n1. **Auto-detect source** - 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Use when creating new skills or updating existing skills.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fskill-creator\u002FSKILL.md",[112],{"categoryNo":113,"categoryName":114,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[116,117,118,9,119,120,121,25,122,123,124,29,125,126,33,31,127,30,128],"skill creator","creator","coding agents","foundry","microsoft foundry","ai coding agents","services","coding","guide","ai coding","with","Existing","new",{"skillNo":130,"name":131,"nameZh":6,"description":132,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":133,"categories":134,"keywords":145,"publishedAt":43},"01M69B4FD57V1MYKRWAJJECTZ7","podcast-generation","Generate AI-powered podcast-style audio narratives using Azure OpenAI's GPT Realtime Mini model via WebSocket. Use when building text-to-speech features, audio narrative generation, podcast creation from content, or integrating with Azure OpenAI Realtime API for real audio output. Covers full-stack implementation from React frontend to Python FastAPI backend with WebSocket streaming.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fpodcast-generation\u002FSKILL.md",[135,138,141,144],{"categoryNo":136,"categoryName":137,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":139,"categoryName":140,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":142,"categoryName":143,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":113,"categoryName":114,"taxonomyType":21,"taxonomyTypeDesc":22},[146,147,148,149,150,151,152,25,153,154,155,156,157,158,38,159,160,161,162,163,164,30,165],"podcast generation","podcast","generation","frontend","python","react","realtime","azure openai","generate","streaming","api","Audio","stack","model","fastapi","creation","python fastapi","narrative","features","fastapi backend",{"skillNo":167,"name":168,"nameZh":6,"description":169,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":170,"categories":171,"keywords":173,"publishedAt":43},"01M69B4CYPM5SESY5ZWEFDXEQZ","microsoft-docs","Understand Microsoft technologies by querying official documentation. Use whenever the user asks how something works, wants tutorials, needs configuration options, limits, quotas, or best practices for any Microsoft technology (Azure, .NET, M365, Windows, Power Platform, etc.)—even if they don't mention \"docs.\" If the question is about understanding a concept rather than writing code, this is the right skill.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fmicrosoft-docs\u002FSKILL.md",[172],{"categoryNo":113,"categoryName":114,"taxonomyType":21,"taxonomyTypeDesc":22},[174,9,175,176,177,178,179,180,181,182,183,25,184,185,186,29,187,64,188,189,190,191,192,31],"docs","microsoft docs","MCP","writing","cli","mcp server","windows","power platform","official","documentation","learn","best practices","question","power","back","technology","platform","learn mcp","concept",{"skillNo":194,"name":195,"nameZh":6,"description":196,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":197,"categories":198,"keywords":200,"publishedAt":43},"01M69B49C3YPD1ZJ3NC0HPCY58","mcp-builder","Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP), Node\u002FTypeScript (MCP SDK), or C#\u002F.NET (Microsoft MCP SDK).","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fmcp-builder\u002FSKILL.md",[199],{"categoryNo":113,"categoryName":114,"taxonomyType":21,"taxonomyTypeDesc":22},[176,201,202,203,150,204,205,206,9,207,208,159,209,210,124,29,211,212,213,126,31,30],"mcp builder","builder","typescript","llms","sdk","model context protocol","fastmcp","Tools","node","context","integrate","mcp sdk","building mcp servers",{"skillNo":215,"name":216,"nameZh":6,"description":217,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":218,"categories":219,"keywords":232,"publishedAt":43},"01M69B467A7CDMFQ0T6Q0ZA12Q","kql","KQL language expertise for writing correct, efficient Kusto Query Language queries. Covers syntax gotchas, join patterns, dynamic types, datetime pitfalls, regex patterns, serialization, memory management, result-size discipline, and advanced functions (geo, vector, graph). USE THIS SKILL whenever writing, debugging, or reviewing KQL queries — even simple ones — because the gotchas section prevents the most common errors that waste tool calls and cause expensive retry cascades. Trigger on: KQL, Kusto, ADX, Azure Data Explorer, Fabric Real-Time Intelligence, EventHouse, Log Analytics, log analysis, data exploration, time series, anomaly detection, summarize, where clause, join, extend, project, let statement, parse operator, extract function, any mention of pipe-forward query syntax.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fkql\u002FSKILL.md",[220,223,224,229],{"categoryNo":221,"categoryName":222,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":113,"categoryName":114,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":225,"categoryName":226,"taxonomyType":227,"taxonomyTypeDesc":228},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",{"categoryNo":230,"categoryName":231,"taxonomyType":227,"taxonomyTypeDesc":228},"01JBSEEDCAT000000000000036","内容创作者",[216,177,233,234,235,25,236,237,238,239,240,241,242,243,244,245,246,247,248,31,249,250,251],"summarize","memory","analytics","vector","time series","tool","discipline","log","geo","exploration","pipe","time","project","regex","join","datetime","section","anomaly","parse",{"skillNo":253,"name":254,"nameZh":6,"description":255,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":256,"categories":257,"keywords":258,"publishedAt":43},"01M69B44HJQ7897EZ7NE7DWQX3","github-issue-creator","Convert raw notes, error logs, voice dictation, or screenshots into crisp GitHub-flavored markdown issue reports. Use when the user pastes bug info, error messages, or informal descriptions and wants a structured GitHub issue. Supports images\u002FGIFs for visual evidence.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fgithub-issue-creator\u002FSKILL.md",[],[259,260,261,262,117,263,264,265,266,267,268,269,29,270,271,272,273,274,275,31,30],"github","github issue creator","github issue","issue","issue creator","screenshots","notes","logs","error messages","messages","convert","evidence","reports","images","info","visual","error"]