[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6PXSN1PDESQBVGZ5FDV3999":3,"skill-same:01M6PXSN1PDESQBVGZ5FDV3999":100},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":28,"publishedAt":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":54,"commitSha":55,"skillDirectory":56,"forksCount":57,"repositoryHtmlUrl":58,"repositoryHomepageUrl":6,"ownerHtmlUrl":59,"ownerBlogUrl":6,"ownerDescription":60,"updatedAt":61,"install":62,"resources":75},"01M6PXSN1PDESQBVGZ5FDV3999","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.","Kernel8901\u002Fai-agent-skills-classification","Kernel8901","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F72647553?v=4","01M6KV388J2H5PNC8D8P940BJD","01M6KV38CS5B3AY63RKZB410YM",7,"MIT","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F媒体与流媒体\u002Fpodcast-generation\u002FSKILL.md",[17,22,25],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000009","前端开发","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":26,"categoryName":27,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"podcast generation","generation","podcast","text","features","narrative","python fastapi","creation","full","fastapi","model","frontend","stack","Audio","api","streaming","openai","generate","azure openai","react","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","f4cb2d49f7667e79ef7804f50396d5aaffdf1f56","媒体与流媒体\u002Fpodcast-generation",1,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification","https:\u002F\u002Fgithub.com\u002FKernel8901","a little little little dobe in life but science geek","2026-04-05 16:43:56",{"source":8,"skillName":5,"commands":63,"prompt":73,"zipAvailable":74},[64,67,70],{"tool":65,"command":66},"npx","npx skills add Kernel8901\u002Fai-agent-skills-classification --skill podcast-generation",{"tool":68,"command":69},"bunx","bunx skills add Kernel8901\u002Fai-agent-skills-classification --skill podcast-generation",{"tool":71,"command":72},"pnpm","pnpm dlx skills add Kernel8901\u002Fai-agent-skills-classification --skill podcast-generation","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F媒体与流媒体\u002Fpodcast-generation\u002FSKILL.md",true,[76,82,86,90,94],{"path":77,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":80,"sourceHtmlUrl":81},"_meta.json","REFERENCE","参考文档",314,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F媒体与流媒体\u002Fpodcast-generation\u002F_meta.json",{"path":83,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":84,"sourceHtmlUrl":85},"references\u002Facceptance-criteria.md",12414,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F媒体与流媒体\u002Fpodcast-generation\u002Freferences\u002Facceptance-criteria.md",{"path":87,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":88,"sourceHtmlUrl":89},"references\u002Farchitecture.md",4736,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F媒体与流媒体\u002Fpodcast-generation\u002Freferences\u002Farchitecture.md",{"path":91,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":92,"sourceHtmlUrl":93},"references\u002Fcode-examples.md",6116,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F媒体与流媒体\u002Fpodcast-generation\u002Freferences\u002Fcode-examples.md",{"path":95,"resourceType":96,"resourceTypeDesc":97,"sizeBytes":98,"sourceHtmlUrl":99},"scripts\u002Fpcm_to_wav.py","SCRIPT","脚本",2475,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F媒体与流媒体\u002Fpodcast-generation\u002Fscripts\u002Fpcm_to_wav.py",[101,125,156,167,177,207],{"skillNo":102,"name":103,"nameZh":6,"description":104,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":105,"categories":106,"keywords":107,"publishedAt":49},"01M6Q5R6WZ06RRB9RQTW54B34B","zyfai","Earn yield on any Ethereum wallet on Base, Arbitrum, and Plasma. Use when a user wants passive DeFi yield on their funds. Deploys a non-custodial subaccount (Safe) linked to their EOA, enables automated yield optimization, and lets them deposit\u002Fwithdraw anytime.","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzyfai-sdk\u002FSKILL.md",[],[103,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124],"safe","ethereum","defi","Optimization","user","use","Wallet","base","deposit","withdraw","yield","arbitrum","non-custodial","passive","defi yield","earn","yield optimization",{"skillNo":126,"name":127,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":128,"categories":129,"keywords":139,"publishedAt":49},"01M6Q5R337B5JSNER4P4YS03TN","zerion-api","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzerion-api\u002FSKILL.md",[130,131,134],{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":132,"categoryName":133,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":135,"categoryName":136,"taxonomyType":137,"taxonomyTypeDesc":138},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[43,140,141,142,143,144,145,110,146,114,147,148,149,150,151,152,153,154,155],"zerion api","zerion","MCP","mcp server","server","blockchain","transactions","crypto wallet","crypto","evm","solana","query","across","positions","token","prices",{"skillNo":157,"name":158,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":159,"categories":160,"keywords":164,"publishedAt":49},"01M6Q5QZFYTDAZMZ27BN5VZXAR","zerion-api-skill","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzerion-api-skill\u002FSKILL.md",[161,162,163],{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":132,"categoryName":133,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":135,"categoryName":136,"taxonomyType":137,"taxonomyTypeDesc":138},[43,165,166,140,141,142,143,144,145,110,146,114,148,149,150,151,152,153,154,155],"api skill","zerion api skill",{"skillNo":168,"name":169,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":170,"categories":171,"keywords":175,"publishedAt":49},"01M6Q5QW17QW98RA3FE45E4RD8","zerion-api-mcp","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzerion-api-mcp\u002FSKILL.md",[172,173,174],{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":132,"categoryName":133,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":135,"categoryName":136,"taxonomyType":137,"taxonomyTypeDesc":138},[142,43,176,140,141,143,144,110,146,114,147,148,149,150,151,152,153,154,155],"zerion api mcp",{"skillNo":178,"name":179,"nameZh":6,"description":180,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":181,"categories":182,"keywords":186,"publishedAt":49},"01M6Q5QQYZB7A501BDEQWKGSJT","yahoo-finance-forex","Fetches real-time FOREX news and market data from Yahoo Finance for major currency pairs (EUR\u002FUSD, GBP\u002FUSD, USD\u002FJPY, etc.). Analyzes sentiment and provides trading context.","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fyahoo-finance-forex\u002FSKILL.md",[183],{"categoryNo":184,"categoryName":185,"taxonomyType":137,"taxonomyTypeDesc":138},"01JBSEEDCAT000000000000040","财务",[187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206],"finance","yahoo finance","forex","yahoo","yahoo finance forex","github","https","sentiment","real-time","python3","install","Market","usd","news","time","currency","gbp","pip","market data","for",{"skillNo":208,"name":209,"nameZh":6,"description":210,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":211,"categories":212,"keywords":214,"publishedAt":49},"01M6Q5QM6VS84WZVM7BKXXPF32","yahoo-finance","This skill should be used when the user asks to \"get stock prices\", \"check stock quotes\", \"look up earnings\", \"get financial data\", \"find trending stocks\", or needs stock market data from Yahoo Finance.","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fyahoo-finance-cli\u002FSKILL.md",[213],{"categoryNo":184,"categoryName":185,"taxonomyType":137,"taxonomyTypeDesc":138},[187,188,190,215,216,217,218,219,220,198,221,222,223,224,225,226,227,228,155,205],"cli","homebrew","find","check","brew","up","local","get","stock","link","trending","sf","quotes","ln"]