[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6MM6KED3HJF2CTTS543C240":3,"skill-same:01M6MM6KED3HJF2CTTS543C240":97},{"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":22,"publishedAt":40,"linkOnly":41,"markdownContent":42,"markdownBody":43,"frontMatter":44,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":13,"repositoryHtmlUrl":54,"repositoryHomepageUrl":6,"ownerHtmlUrl":55,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":56,"install":57,"resources":70},"01M6MM6KED3HJF2CTTS543C240","speech-to-text",null,"Transcribe audio to text using ElevenLabs Scribe v2. Use when converting audio\u002Fvideo to text, generating subtitles, transcribing meetings, or processing spoken content.","gyalamanch001a\u002Fpur-new","gyalamanch001a","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F59396177?v=4","01M6KDYQ68SJ3A7KV08CGQ47DY","01M6KDYQAA5QHZEZ9C1C7Y5MG4",0,"MIT","https:\u002F\u002Fgithub.com\u002Fgyalamanch001a\u002Fpur-new\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fspeech-to-text\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],"speech to text","speech","text","access","transcribe audio to text","internet","processing","elevenlabs","use","meetings","Audio","Video","api","scribe","subtitles","content","transcribe","2026-11-10 10:12:30",false,"---\nname: speech-to-text\ndescription: Transcribe audio to text using ElevenLabs Scribe v2. Use when converting audio\u002Fvideo to text, generating subtitles, transcribing meetings, or processing spoken content.\nlicense: MIT\ncompatibility: Requires internet access and an ElevenLabs API key (ELEVENLABS_API_KEY).\nmetadata: {\"openclaw\": {\"requires\": {\"env\": [\"ELEVENLABS_API_KEY\"]}, \"primaryEnv\": \"ELEVENLABS_API_KEY\"}}\n---\n\n# ElevenLabs Speech-to-Text\n\nTranscribe audio to text with Scribe v2 - supports 90+ languages, speaker diarization, and word-level timestamps.\n\n> **Setup:** See [Installation Guide](references\u002Finstallation.md). For JavaScript, use `@elevenlabs\u002F*` packages only.\n\n## Quick Start\n\n### Python\n\n```python\nfrom elevenlabs import ElevenLabs\n\nclient = ElevenLabs()\n\nwith open(\"audio.mp3\", \"rb\") as audio_file:\n    result = client.speech_to_text.convert(file=audio_file, model_id=\"scribe_v2\")\n\nprint(result.text)\n```\n\n### JavaScript\n\n```javascript\nimport { ElevenLabsClient } from \"@elevenlabs\u002Felevenlabs-js\";\nimport { createReadStream } from \"fs\";\n\nconst client = new ElevenLabsClient();\nconst result = await client.speechToText.convert({\n  file: createReadStream(\"audio.mp3\"),\n  modelId: \"scribe_v2\",\n});\nconsole.log(result.text);\n```\n\n### cURL\n\n```bash\ncurl -X POST \"https:\u002F\u002Fapi.elevenlabs.io\u002Fv1\u002Fspeech-to-text\" \\\n  -H \"xi-api-key: $ELEVENLABS_API_KEY\" -F \"file=@audio.mp3\" -F \"model_id=scribe_v2\"\n```\n\n## Models\n\n| Model ID | Description | Best For |\n|----------|-------------|----------|\n| `scribe_v2` | State-of-the-art accuracy, 90+ languages | Batch transcription, subtitles, long-form audio |\n| `scribe_v2_realtime` | Low latency (~150ms) | Live transcription, voice agents |\n\n## Transcription with Timestamps\n\nWord-level timestamps include type classification and speaker identification:\n\n```python\nresult = client.speech_to_text.convert(\n    file=audio_file, model_id=\"scribe_v2\", timestamps_granularity=\"word\"\n)\n\nfor word in result.words:\n    print(f\"{word.text}: {word.start}s - {word.end}s (type: {word.type})\")\n\n```\n\n## Speaker Diarization\n\nIdentify WHO said WHAT - the model labels each word with a speaker ID, useful for meetings, interviews, or any multi-speaker audio:\n\n```python\nresult = client.speech_to_text.convert(\n    file=audio_file,\n    model_id=\"scribe_v2\",\n    diarize=True\n)\n\nfor word in result.words:\n    print(f\"[{word.speaker_id}] {word.text}\")\n```\n\n## Keyterm Prompting\n\nHelp the model recognize specific words it might otherwise mishear - product names, technical jargon, or unusual spellings (up to 100 terms):\n\n```python\nresult = client.speech_to_text.convert(\n    file=audio_file,\n    model_id=\"scribe_v2\",\n    keyterms=[\"ElevenLabs\", \"Scribe\", \"API\"]\n)\n```\n\n## Language Detection\n\nAutomatic detection with optional language hint:\n\n```python\nresult = client.speech_to_text.convert(\n    file=audio_file,\n    model_id=\"scribe_v2\",\n    language_code=\"eng\"  # ISO 639-1 or ISO 639-3 code\n)\n\nprint(f\"Detected: {result.language_code} ({result.language_probability:.0%})\")\n```\n\n## Supported Formats\n\n**Audio:** MP3, WAV, M4A, FLAC, OGG, WebM, AAC, AIFF, Opus\n**Video:** MP4, AVI, MKV, MOV, WMV, FLV, WebM, MPEG, 3GPP\n\n**Limits:** Up to 3GB file size, 10 hours duration\n\n## Response Format\n\n```json\n{\n  \"text\": \"The full transcription text\",\n  \"language_code\": \"eng\",\n  \"language_probability\": 0.98,\n  \"words\": [\n    {\"text\": \"The\", \"start\": 0.0, \"end\": 0.15, \"type\": \"word\", \"speaker_id\": \"speaker_0\"},\n    {\"text\": \" \", \"start\": 0.15, \"end\": 0.16, \"type\": \"spacing\", \"speaker_id\": \"speaker_0\"}\n  ]\n}\n```\n\n**Word types:**\n- `word` - An actual spoken word\n- `spacing` - Whitespace between words (useful for precise timing)\n- `audio_event` - Non-speech sounds the model detected (laughter, applause, music, etc.)\n\n## Error Handling\n\n```python\ntry:\n    result = client.speech_to_text.convert(file=audio_file, model_id=\"scribe_v2\")\nexcept Exception as e:\n    print(f\"Transcription failed: {e}\")\n```\n\nCommon errors:\n- **401**: Invalid API key\n- **422**: Invalid parameters\n- **429**: Rate limit exceeded\n\n## Tracking Costs\n\nMonitor usage via `request-id` response header:\n\n```python\nresponse = client.speech_to_text.convert.with_raw_response(file=audio_file, model_id=\"scribe_v2\")\nresult = response.parse()\nprint(f\"Request ID: {response.headers.get('request-id')}\")\n```\n\n## Real-Time Streaming\n\nFor live transcription with ultra-low latency (~150ms), use the real-time API. The real-time API produces two types of transcripts:\n\n- **Partial transcripts**: Interim results that update frequently as audio is processed - use these for live feedback (e.g., showing text as the user speaks)\n- **Committed transcripts**: Final, stable results after you \"commit\" - use these as the source of truth for your application\n\nA \"commit\" tells the model to finalize the current segment. You can commit manually (e.g., when the user pauses) or use Voice Activity Detection (VAD) to auto-commit on silence.\n\n### Python (Server-Side)\n\n```python\nimport asyncio\nfrom elevenlabs import ElevenLabs\n\nclient = ElevenLabs()\n\nasync def transcribe_realtime():\n    async with client.speech_to_text.realtime.connect(\n        model_id=\"scribe_v2_realtime\",\n        include_timestamps=True,\n    ) as connection:\n        await connection.stream_url(\"https:\u002F\u002Fexample.com\u002Faudio.mp3\")\n\n        async for event in connection:\n            if event.type == \"partial_transcript\":\n                print(f\"Partial: {event.text}\")\n            elif event.type == \"committed_transcript\":\n                print(f\"Final: {event.text}\")\n\nasyncio.run(transcribe_realtime())\n```\n\n### JavaScript (Client-Side with React)\n\n```typescript\nimport { useScribe, CommitStrategy } from \"@elevenlabs\u002Freact\";\n\nfunction TranscriptionComponent() {\n  const [transcript, setTranscript] = useState(\"\");\n\n  const scribe = useScribe({\n    modelId: \"scribe_v2_realtime\",\n    commitStrategy: CommitStrategy.VAD, \u002F\u002F Auto-commit on silence for mic input\n    onPartialTranscript: (data) => console.log(\"Partial:\", data.text),\n    onCommittedTranscript: (data) => setTranscript((prev) => prev + data.text),\n  });\n\n  const start = async () => {\n    \u002F\u002F Get token from your backend (never expose API key to client)\n    const { token } = await fetch(\"\u002Fscribe-token\").then((r) => r.json());\n\n    await scribe.connect({\n      token,\n      microphone: { echoCancellation: true, noiseSuppression: true },\n    });\n  };\n\n  return \u003Cbutton onClick={start}>Start Recording\u003C\u002Fbutton>;\n}\n```\n\n### Commit Strategies\n\n| Strategy | Description |\n|----------|-------------|\n| **Manual** | You call `commit()` when ready - use for file processing or when you control the audio segments |\n| **VAD** | Voice Activity Detection auto-commits when silence is detected - use for live microphone input |\n\n```typescript\n\u002F\u002F React: set commitStrategy on the hook (recommended for mic input)\nimport { useScribe, CommitStrategy } from \"@elevenlabs\u002Freact\";\n\nconst scribe = useScribe({\n  modelId: \"scribe_v2_realtime\",\n  commitStrategy: CommitStrategy.VAD,\n  \u002F\u002F Optional VAD tuning:\n  vadSilenceThresholdSecs: 1.5,\n  vadThreshold: 0.4,\n});\n```\n\n```javascript\n\u002F\u002F JavaScript client: pass vad config on connect\nconst connection = await client.speechToText.realtime.connect({\n  modelId: \"scribe_v2_realtime\",\n  vad: {\n    silenceThresholdSecs: 1.5,\n    threshold: 0.4,\n  },\n});\n```\n\n### Event Types\n\n| Event | Description |\n|-------|-------------|\n| `partial_transcript` | Live interim results |\n| `committed_transcript` | Final results after commit |\n| `committed_transcript_with_timestamps` | Final with word timing |\n| `error` | Error occurred |\n\nSee real-time references for complete documentation.\n\n## References\n\n- [Installation Guide](references\u002Finstallation.md)\n- [Transcription Options](references\u002Ftranscription-options.md)\n- [Real-Time Client-Side Streaming](references\u002Frealtime-client-side.md)\n- [Real-Time Server-Side Streaming](references\u002Frealtime-server-side.md)\n- [Commit Strategies](references\u002Frealtime-commit-strategies.md)\n- [Real-Time Event Reference](references\u002Frealtime-events.md)\n","# ElevenLabs Speech-to-Text\n\nTranscribe audio to text with Scribe v2 - supports 90+ languages, speaker diarization, and word-level timestamps.\n\n> **Setup:** See [Installation Guide](references\u002Finstallation.md). For JavaScript, use `@elevenlabs\u002F*` packages only.\n\n## Quick Start\n\n### Python\n\n```python\nfrom elevenlabs import ElevenLabs\n\nclient = ElevenLabs()\n\nwith open(\"audio.mp3\", \"rb\") as audio_file:\n    result = client.speech_to_text.convert(file=audio_file, model_id=\"scribe_v2\")\n\nprint(result.text)\n```\n\n### JavaScript\n\n```javascript\nimport { ElevenLabsClient } from \"@elevenlabs\u002Felevenlabs-js\";\nimport { createReadStream } from \"fs\";\n\nconst client = new ElevenLabsClient();\nconst result = await client.speechToText.convert({\n  file: createReadStream(\"audio.mp3\"),\n  modelId: \"scribe_v2\",\n});\nconsole.log(result.text);\n```\n\n### cURL\n\n```bash\ncurl -X POST \"https:\u002F\u002Fapi.elevenlabs.io\u002Fv1\u002Fspeech-to-text\" \\\n  -H \"xi-api-key: $ELEVENLABS_API_KEY\" -F \"file=@audio.mp3\" -F \"model_id=scribe_v2\"\n```\n\n## Models\n\n| Model ID | Description | Best For |\n|----------|-------------|----------|\n| `scribe_v2` | State-of-the-art accuracy, 90+ languages | Batch transcription, subtitles, long-form audio |\n| `scribe_v2_realtime` | Low latency (~150ms) | Live transcription, voice agents |\n\n## Transcription with Timestamps\n\nWord-level timestamps include type classification and speaker identification:\n\n```python\nresult = client.speech_to_text.convert(\n    file=audio_file, model_id=\"scribe_v2\", timestamps_granularity=\"word\"\n)\n\nfor word in result.words:\n    print(f\"{word.text}: {word.start}s - {word.end}s (type: {word.type})\")\n\n```\n\n## Speaker Diarization\n\nIdentify WHO said WHAT - the model labels each word with a speaker ID, useful for meetings, interviews, or any multi-speaker audio:\n\n```python\nresult = client.speech_to_text.convert(\n    file=audio_file,\n    model_id=\"scribe_v2\",\n    diarize=True\n)\n\nfor word in result.words:\n    print(f\"[{word.speaker_id}] {word.text}\")\n```\n\n## Keyterm Prompting\n\nHelp the model recognize specific words it might otherwise mishear - product names, technical jargon, or unusual spellings (up to 100 terms):\n\n```python\nresult = client.speech_to_text.convert(\n    file=audio_file,\n    model_id=\"scribe_v2\",\n    keyterms=[\"ElevenLabs\", \"Scribe\", \"API\"]\n)\n```\n\n## Language Detection\n\nAutomatic detection with optional language hint:\n\n```python\nresult = client.speech_to_text.convert(\n    file=audio_file,\n    model_id=\"scribe_v2\",\n    language_code=\"eng\"  # ISO 639-1 or ISO 639-3 code\n)\n\nprint(f\"Detected: {result.language_code} ({result.language_probability:.0%})\")\n```\n\n## Supported Formats\n\n**Audio:** MP3, WAV, M4A, FLAC, OGG, WebM, AAC, AIFF, Opus\n**Video:** MP4, AVI, MKV, MOV, WMV, FLV, WebM, MPEG, 3GPP\n\n**Limits:** Up to 3GB file size, 10 hours duration\n\n## Response Format\n\n```json\n{\n  \"text\": \"The full transcription text\",\n  \"language_code\": \"eng\",\n  \"language_probability\": 0.98,\n  \"words\": [\n    {\"text\": \"The\", \"start\": 0.0, \"end\": 0.15, \"type\": \"word\", \"speaker_id\": \"speaker_0\"},\n    {\"text\": \" \", \"start\": 0.15, \"end\": 0.16, \"type\": \"spacing\", \"speaker_id\": \"speaker_0\"}\n  ]\n}\n```\n\n**Word types:**\n- `word` - An actual spoken word\n- `spacing` - Whitespace between words (useful for precise timing)\n- `audio_event` - Non-speech sounds the model detected (laughter, applause, music, etc.)\n\n## Error Handling\n\n```python\ntry:\n    result = client.speech_to_text.convert(file=audio_file, model_id=\"scribe_v2\")\nexcept Exception as e:\n    print(f\"Transcription failed: {e}\")\n```\n\nCommon errors:\n- **401**: Invalid API key\n- **422**: Invalid parameters\n- **429**: Rate limit exceeded\n\n## Tracking Costs\n\nMonitor usage via `request-id` response header:\n\n```python\nresponse = client.speech_to_text.convert.with_raw_response(file=audio_file, model_id=\"scribe_v2\")\nresult = response.parse()\nprint(f\"Request ID: {response.headers.get('request-id')}\")\n```\n\n## Real-Time Streaming\n\nFor live transcription with ultra-low latency (~150ms), use the real-time API. The real-time API produces two types of transcripts:\n\n- **Partial transcripts**: Interim results that update frequently as audio is processed - use these for live feedback (e.g., showing text as the user speaks)\n- **Committed transcripts**: Final, stable results after you \"commit\" - use these as the source of truth for your application\n\nA \"commit\" tells the model to finalize the current segment. You can commit manually (e.g., when the user pauses) or use Voice Activity Detection (VAD) to auto-commit on silence.\n\n### Python (Server-Side)\n\n```python\nimport asyncio\nfrom elevenlabs import ElevenLabs\n\nclient = ElevenLabs()\n\nasync def transcribe_realtime():\n    async with client.speech_to_text.realtime.connect(\n        model_id=\"scribe_v2_realtime\",\n        include_timestamps=True,\n    ) as connection:\n        await connection.stream_url(\"https:\u002F\u002Fexample.com\u002Faudio.mp3\")\n\n        async for event in connection:\n            if event.type == \"partial_transcript\":\n                print(f\"Partial: {event.text}\")\n            elif event.type == \"committed_transcript\":\n                print(f\"Final: {event.text}\")\n\nasyncio.run(transcribe_realtime())\n```\n\n### JavaScript (Client-Side with React)\n\n```typescript\nimport { useScribe, CommitStrategy } from \"@elevenlabs\u002Freact\";\n\nfunction TranscriptionComponent() {\n  const [transcript, setTranscript] = useState(\"\");\n\n  const scribe = useScribe({\n    modelId: \"scribe_v2_realtime\",\n    commitStrategy: CommitStrategy.VAD, \u002F\u002F Auto-commit on silence for mic input\n    onPartialTranscript: (data) => console.log(\"Partial:\", data.text),\n    onCommittedTranscript: (data) => setTranscript((prev) => prev + data.text),\n  });\n\n  const start = async () => {\n    \u002F\u002F Get token from your backend (never expose API key to client)\n    const { token } = await fetch(\"\u002Fscribe-token\").then((r) => r.json());\n\n    await scribe.connect({\n      token,\n      microphone: { echoCancellation: true, noiseSuppression: true },\n    });\n  };\n\n  return \u003Cbutton onClick={start}>Start Recording\u003C\u002Fbutton>;\n}\n```\n\n### Commit Strategies\n\n| Strategy | Description |\n|----------|-------------|\n| **Manual** | You call `commit()` when ready - 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Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.","https:\u002F\u002Fgithub.com\u002Fgyalamanch001a\u002Fpur-new\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fvercel-react-best-practices\u002FSKILL.md",[104],{"categoryNo":105,"categoryName":106,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000009","前端开发",[108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127],"react","vercel react best practices","best practices","react best practices","vercel","writing","performance","components","react components","guidelines","tasks","code","Optimization","data","refactoring","patterns","next","pages","triggers","data fetching",{"skillNo":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":132,"categories":133,"keywords":135,"publishedAt":40},"01M6MM84W6NP0KV00EV26AZHBX","vercel-composition-patterns","React composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API changes.","https:\u002F\u002Fgithub.com\u002Fgyalamanch001a\u002Fpur-new\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fvercel-composition-patterns\u002FSKILL.md",[134],{"categoryNo":105,"categoryName":106,"taxonomyType":20,"taxonomyTypeDesc":21},[136,137,112,123,138,115,108,139,118,140,141,35,142,31,143,126,144,145,146,147],"Composition","vercel composition patterns","composition patterns","architecture","libraries","component architecture","react composition","reusable","react 19","render","with","react composition patterns",{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":152,"categories":153,"keywords":154,"publishedAt":40},"01M6MM6FJ9FHP529Y2S1F85C5D","sound-effects","Generate sound effects from text descriptions using ElevenLabs. Use when creating sound effects, generating audio textures, producing ambient sounds, cinematic impacts, UI sounds, or any audio that isn't speech. Supports looping, duration control, and prompt influence tuning.","https:\u002F\u002Fgithub.com\u002Fgyalamanch001a\u002Fpur-new\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fsound-effects\u002FSKILL.md",[],[155,156,157,158,24,159,160,35,33,26,25,161,31,30,162,163,28],"sound effects","sound","effects","generate","ui","tuning","prompt","control","ambient",{"skillNo":165,"name":166,"nameZh":6,"description":167,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":168,"categories":169,"keywords":181,"publishedAt":40},"01M6MM5SWAS258ZTP8T4KY5GNR","setup-api-key","Guides users through setting up an ElevenLabs API key for ElevenLabs MCP tools. Use when the user needs to configure an ElevenLabs API key, when ElevenLabs tools fail due to missing API key, or when the user mentions needing access to ElevenLabs. First checks whether ELEVENLABS_API_KEY is already configured and valid, and only runs full setup when needed.","https:\u002F\u002Fgithub.com\u002Fgyalamanch001a\u002Fpur-new\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fsetup-api-key\u002FSKILL.md",[170,173,176],{"categoryNo":171,"categoryName":172,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":174,"categoryName":175,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":177,"categoryName":178,"taxonomyType":179,"taxonomyTypeDesc":180},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[182,35,183,184,185,186,187,188,26,189,31,190,191,192,193,194,195,196,28,197],"setup","setup api key","MCP","valid","mcp tools","Tools","first","configure","only","up","full","io","missing","guides","users","for",{"skillNo":199,"name":200,"nameZh":6,"description":201,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":202,"categories":203,"keywords":205,"publishedAt":40},"01M6MM2MVP7A02HVFB658T3Y39","music","Generate music using ElevenLabs Music API. Use when creating instrumental tracks, songs with lyrics, background music, jingles, or any AI-generated music composition. Supports prompt-based generation, composition plans for granular control, and detailed output with metadata.","https:\u002F\u002Fgithub.com\u002Fgyalamanch001a\u002Fpur-new\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fmusic\u002FSKILL.md",[204],{"categoryNo":171,"categoryName":172,"taxonomyType":20,"taxonomyTypeDesc":21},[200,158,35,136,26,161,206,31,207,208,30,209,162,210,211,146,28,212,197,213],"generated","lyrics","elevenlabs music","generation","plans","output","tracks","background music",{"skillNo":215,"name":216,"nameZh":6,"description":217,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":218,"categories":219,"keywords":220,"publishedAt":40},"01M6MKV0S9MXDE1NJGNZWZ3GXK","agents","Build voice AI agents with ElevenLabs. Use when creating voice assistants, customer service bots, interactive voice characters, or any real-time voice conversation experience.","https:\u002F\u002Fgithub.com\u002Fgyalamanch001a\u002Fpur-new\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fagents\u002FSKILL.md",[],[221,222,223,35,26,224,225,31,226,30,227,228,229,230,146,28,231,232],"ai agents","customer","real-time","voice","interactive","build","conversation","bots","time","service","voice ai agents","characters"]