[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M69B4FD57V1MYKRWAJJECTZ7":3,"skill-same:01M69B4FD57V1MYKRWAJJECTZ7":101},{"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":32,"publishedAt":56,"linkOnly":57,"markdownContent":58,"markdownBody":59,"frontMatter":60,"refName":61,"commitSha":62,"skillDirectory":63,"forksCount":64,"repositoryHtmlUrl":65,"repositoryHomepageUrl":66,"ownerHtmlUrl":67,"ownerBlogUrl":68,"ownerDescription":69,"updatedAt":70,"install":71,"resources":84},"01M69B4FD57V1MYKRWAJJECTZ7","podcast-generation",null,"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.","microsoft\u002Fskills","microsoft","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6154722?v=4","01M698VYDXZ6JW5WH3CRV650R8","Microsoft","01M69AJYYW4E1RMSYCQDJ4FJSH",3199,"MIT","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fpodcast-generation\u002FSKILL.md",[18,23,26,29],{"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",{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":30,"categoryName":31,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55],"podcast generation","generation","podcast","stack","fastapi backend","when","features","narrative","python fastapi","creation","fastapi","model","text","frontend","Audio","api","streaming","generate","azure openai","azure","realtime","react","python","2026-11-10 10:12:30",false,"---\nname: podcast-generation\ndescription: 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.\n---\n\n# Podcast Generation with GPT Realtime Mini\n\nGenerate real audio narratives from text content using Azure OpenAI's Realtime API.\n\n## Quick Start\n\n1. Configure environment variables for Realtime API\n2. Connect via WebSocket to Azure OpenAI Realtime endpoint\n3. Send text prompt, collect PCM audio chunks + transcript\n4. Convert PCM to WAV format\n5. Return base64-encoded audio to frontend for playback\n\n## Environment Configuration\n\n```env\nAZURE_OPENAI_AUDIO_API_KEY=your_realtime_api_key\nAZURE_OPENAI_AUDIO_ENDPOINT=https:\u002F\u002Fyour-resource.cognitiveservices.azure.com\nAZURE_OPENAI_AUDIO_DEPLOYMENT=gpt-realtime-mini\n```\n\n**Note**: Endpoint should NOT include `\u002Fopenai\u002Fv1\u002F` - just the base URL.\n\n## Core Workflow\n\n### Backend Audio Generation\n\n```python\nfrom openai import AsyncOpenAI\nimport base64\n\n# Convert HTTPS endpoint to WebSocket URL\nws_url = endpoint.replace(\"https:\u002F\u002F\", \"wss:\u002F\u002F\") + \"\u002Fopenai\u002Fv1\"\n\nclient = AsyncOpenAI(\n    websocket_base_url=ws_url,\n    api_key=api_key\n)\n\naudio_chunks = []\ntranscript_parts = []\n\nasync with client.realtime.connect(model=\"gpt-realtime-mini\") as conn:\n    # Configure for audio-only output\n    await conn.session.update(session={\n        \"output_modalities\": [\"audio\"],\n        \"instructions\": \"You are a narrator. Speak naturally.\"\n    })\n    \n    # Send text to narrate\n    await conn.conversation.item.create(item={\n        \"type\": \"message\",\n        \"role\": \"user\",\n        \"content\": [{\"type\": \"input_text\", \"text\": prompt}]\n    })\n    \n    await conn.response.create()\n    \n    # Collect streaming events\n    async for event in conn:\n        if event.type == \"response.output_audio.delta\":\n            audio_chunks.append(base64.b64decode(event.delta))\n        elif event.type == \"response.output_audio_transcript.delta\":\n            transcript_parts.append(event.delta)\n        elif event.type == \"response.done\":\n            break\n\n# Convert PCM to WAV (see scripts\u002Fpcm_to_wav.py)\npcm_audio = b''.join(audio_chunks)\nwav_audio = pcm_to_wav(pcm_audio, sample_rate=24000)\n```\n\n### Frontend Audio Playback\n\n```javascript\n\u002F\u002F Convert base64 WAV to playable blob\nconst base64ToBlob = (base64, mimeType) => {\n  const bytes = atob(base64);\n  const arr = new Uint8Array(bytes.length);\n  for (let i = 0; i \u003C bytes.length; i++) arr[i] = bytes.charCodeAt(i);\n  return new Blob([arr], { type: mimeType });\n};\n\nconst audioBlob = base64ToBlob(response.audio_data, 'audio\u002Fwav');\nconst audioUrl = URL.createObjectURL(audioBlob);\nnew Audio(audioUrl).play();\n```\n\n## Voice Options\n\n| Voice | Character |\n|-------|-----------|\n| alloy | Neutral |\n| echo | Warm |\n| fable | Expressive |\n| onyx | Deep |\n| nova | Friendly |\n| shimmer | Clear |\n\n## Realtime API Events\n\n- `response.output_audio.delta` - Base64 audio chunk\n- `response.output_audio_transcript.delta` - Transcript text\n- `response.done` - Generation complete\n- `error` - Handle with `event.error.message`\n\n## Audio Format\n\n- **Input**: Text prompt\n- **Output**: PCM audio (24kHz, 16-bit, mono)\n- **Storage**: Base64-encoded WAV\n\n## References\n\n- **Full architecture**: See [references\u002Farchitecture.md](references\u002Farchitecture.md) for complete stack design\n- **Code examples**: See [references\u002Fcode-examples.md](references\u002Fcode-examples.md) for production patterns\n- **PCM conversion**: Use [scripts\u002Fpcm_to_wav.py](scripts\u002Fpcm_to_wav.py) for audio format conversion\n","# Podcast Generation with GPT Realtime Mini\n\nGenerate real audio narratives from text content using Azure OpenAI's Realtime API.\n\n## Quick Start\n\n1. Configure environment variables for Realtime API\n2. Connect via WebSocket to Azure OpenAI Realtime endpoint\n3. Send text prompt, collect PCM audio chunks + transcript\n4. Convert PCM to WAV format\n5. Return base64-encoded audio to frontend for playback\n\n## Environment Configuration\n\n```env\nAZURE_OPENAI_AUDIO_API_KEY=your_realtime_api_key\nAZURE_OPENAI_AUDIO_ENDPOINT=https:\u002F\u002Fyour-resource.cognitiveservices.azure.com\nAZURE_OPENAI_AUDIO_DEPLOYMENT=gpt-realtime-mini\n```\n\n**Note**: Endpoint should NOT include `\u002Fopenai\u002Fv1\u002F` - just the base URL.\n\n## Core Workflow\n\n### Backend Audio Generation\n\n```python\nfrom openai import AsyncOpenAI\nimport base64\n\n# Convert HTTPS endpoint to WebSocket URL\nws_url = endpoint.replace(\"https:\u002F\u002F\", \"wss:\u002F\u002F\") + \"\u002Fopenai\u002Fv1\"\n\nclient = AsyncOpenAI(\n    websocket_base_url=ws_url,\n    api_key=api_key\n)\n\naudio_chunks = []\ntranscript_parts = []\n\nasync with client.realtime.connect(model=\"gpt-realtime-mini\") as conn:\n    # Configure for audio-only output\n    await conn.session.update(session={\n        \"output_modalities\": [\"audio\"],\n        \"instructions\": \"You are a narrator. Speak naturally.\"\n    })\n    \n    # Send text to narrate\n    await conn.conversation.item.create(item={\n        \"type\": \"message\",\n        \"role\": \"user\",\n        \"content\": [{\"type\": \"input_text\", \"text\": prompt}]\n    })\n    \n    await conn.response.create()\n    \n    # Collect streaming events\n    async for event in conn:\n        if event.type == \"response.output_audio.delta\":\n            audio_chunks.append(base64.b64decode(event.delta))\n        elif event.type == \"response.output_audio_transcript.delta\":\n            transcript_parts.append(event.delta)\n        elif event.type == \"response.done\":\n            break\n\n# Convert PCM to WAV (see scripts\u002Fpcm_to_wav.py)\npcm_audio = b''.join(audio_chunks)\nwav_audio = pcm_to_wav(pcm_audio, sample_rate=24000)\n```\n\n### Frontend Audio Playback\n\n```javascript\n\u002F\u002F Convert base64 WAV to playable blob\nconst base64ToBlob = (base64, mimeType) => {\n  const bytes = atob(base64);\n  const arr = new Uint8Array(bytes.length);\n  for (let i = 0; i \u003C bytes.length; i++) arr[i] = bytes.charCodeAt(i);\n  return new Blob([arr], { type: mimeType });\n};\n\nconst audioBlob = base64ToBlob(response.audio_data, 'audio\u002Fwav');\nconst audioUrl = URL.createObjectURL(audioBlob);\nnew Audio(audioUrl).play();\n```\n\n## Voice Options\n\n| Voice | Character |\n|-------|-----------|\n| alloy | Neutral |\n| echo | Warm |\n| fable | Expressive |\n| onyx | Deep |\n| nova | Friendly |\n| shimmer | Clear |\n\n## Realtime API Events\n\n- `response.output_audio.delta` - Base64 audio chunk\n- `response.output_audio_transcript.delta` - Transcript text\n- `response.done` - Generation complete\n- `error` - Handle with `event.error.message`\n\n## Audio Format\n\n- **Input**: Text prompt\n- **Output**: PCM audio (24kHz, 16-bit, mono)\n- **Storage**: Base64-encoded WAV\n\n## References\n\n- **Full architecture**: See [references\u002Farchitecture.md](references\u002Farchitecture.md) for complete stack design\n- **Code examples**: See [references\u002Fcode-examples.md](references\u002Fcode-examples.md) for production patterns\n- **PCM conversion**: Use [scripts\u002Fpcm_to_wav.py](scripts\u002Fpcm_to_wav.py) for audio format conversion\n",{"name":5,"description":7},"main","3f5b461b2314e01c400564051e92469114c5b68f",".github\u002Fskills\u002Fpodcast-generation",366,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills","https:\u002F\u002Fmicrosoft.github.io\u002Fskills\u002F","https:\u002F\u002Fgithub.com\u002Fmicrosoft","https:\u002F\u002Fopensource.microsoft.com","Open source projects and samples from Microsoft","2026-11-13 02:10:26",{"source":8,"skillName":5,"commands":72,"prompt":82,"zipAvailable":83},[73,76,79],{"tool":74,"command":75},"npx","npx skills add microsoft\u002Fskills --skill podcast-generation",{"tool":77,"command":78},"bunx","bunx skills add microsoft\u002Fskills --skill podcast-generation",{"tool":80,"command":81},"pnpm","pnpm dlx skills add microsoft\u002Fskills --skill podcast-generation","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fpodcast-generation\u002FSKILL.md",true,[85,91,95],{"path":86,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":89,"sourceHtmlUrl":90},"references\u002Farchitecture.md","REFERENCE","参考文档",4736,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fpodcast-generation\u002Freferences\u002Farchitecture.md",{"path":92,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":93,"sourceHtmlUrl":94},"references\u002Fcode-examples.md",6116,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fpodcast-generation\u002Freferences\u002Fcode-examples.md",{"path":96,"resourceType":97,"resourceTypeDesc":98,"sizeBytes":99,"sourceHtmlUrl":100},"scripts\u002Fpcm_to_wav.py","SCRIPT","脚本",2475,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fpodcast-generation\u002Fscripts\u002Fpcm_to_wav.py",[102,126,153,174,212,236],{"skillNo":103,"name":104,"nameZh":6,"description":105,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":106,"categories":107,"keywords":109,"publishedAt":56},"01M69B4JCRQV5SPXAFCKXQRV0H","skill-creator","Guide for creating effective skills for AI coding agents working with Azure SDKs and Microsoft Foundry services. Use when creating new skills or updating existing skills.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fskill-creator\u002FSKILL.md",[108],{"categoryNo":30,"categoryName":31,"taxonomyType":21,"taxonomyTypeDesc":22},[110,111,112,9,113,114,115,52,116,117,118,119,120,121,122,123,124,38,125],"skill creator","creator","coding agents","foundry","microsoft foundry","ai coding agents","services","coding","guide","use","ai coding","with","sdks","for","Existing","new",{"skillNo":127,"name":128,"nameZh":6,"description":129,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":130,"categories":131,"keywords":133,"publishedAt":56},"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",[132],{"categoryNo":30,"categoryName":31,"taxonomyType":21,"taxonomyTypeDesc":22},[134,9,135,136,137,138,139,140,141,142,143,52,144,145,146,119,147,74,148,149,150,151,152,123],"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":154,"name":155,"nameZh":6,"description":156,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":157,"categories":158,"keywords":160,"publishedAt":56},"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",[159],{"categoryNo":30,"categoryName":31,"taxonomyType":21,"taxonomyTypeDesc":22},[136,161,162,163,55,164,165,166,9,167,168,44,169,170,118,119,171,172,173,121,123,38],"mcp builder","builder","typescript","llms","sdk","model context protocol","fastmcp","Tools","node","context","integrate","mcp sdk","building mcp servers",{"skillNo":175,"name":176,"nameZh":6,"description":177,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":178,"categories":179,"keywords":192,"publishedAt":56},"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",[180,183,184,189],{"categoryNo":181,"categoryName":182,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":30,"categoryName":31,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":185,"categoryName":186,"taxonomyType":187,"taxonomyTypeDesc":188},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",{"categoryNo":190,"categoryName":191,"taxonomyType":187,"taxonomyTypeDesc":188},"01JBSEEDCAT000000000000036","内容创作者",[176,137,193,194,195,52,196,197,198,199,200,201,202,203,204,205,206,207,208,123,209,210,211],"summarize","memory","analytics","vector","time series","tool","discipline","log","geo","exploration","pipe","time","project","regex","join","datetime","section","anomaly","parse",{"skillNo":213,"name":214,"nameZh":6,"description":215,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":216,"categories":217,"keywords":218,"publishedAt":56},"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",[],[219,220,221,222,111,223,224,225,226,227,228,229,119,230,231,232,233,234,235,123,38],"github","github issue creator","github issue","issue","issue creator","screenshots","notes","logs","error messages","messages","convert","evidence","reports","images","info","visual","error",{"skillNo":237,"name":238,"nameZh":6,"description":239,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":240,"categories":241,"keywords":250,"publishedAt":56},"01M69B41ATQG90X1J7ACEP3VV7","frontend-design-review","Review and create distinctive, production-grade frontend interfaces with high design quality and design system compliance. Evaluates using three pillars: frictionless insight-to-action, quality craft, and trustworthy building. USE FOR: PR reviews, design reviews, accessibility audits, design system compliance checks, creative frontend design, UI code review, component reviews, responsive design checks, theme testing, and creating memorable UI. DO NOT USE FOR: Backend API reviews, database schema reviews, infrastructure or DevOps work, pure business logic without UI, or non-frontend code.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Ffrontend-design-review\u002FSKILL.md",[242,243,244,247],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":245,"categoryName":246,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":248,"categoryName":249,"taxonomyType":187,"taxonomyTypeDesc":188},"01JBSEEDCAT000000000000032","设计师",[46,251,252,253,254,255,256,257,219,258,259,260,261,262,263,264,265,48,266,267,268,61,269,270,271],"design","frontend design","review","design review","frontend design review","code review","testing","accessibility","component","design system","pr","infrastructure","create","code","database","work","craft","responsive design","database schema","reviews","frontend-design"]