[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71MH3GRYFP06ASRK7G0CGR9":3,"skill-same:01M71MH3GRYFP06ASRK7G0CGR9":76},{"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":42,"linkOnly":43,"markdownContent":44,"markdownBody":45,"frontMatter":46,"refName":53,"commitSha":54,"skillDirectory":55,"forksCount":56,"repositoryHtmlUrl":57,"repositoryHomepageUrl":6,"ownerHtmlUrl":58,"ownerBlogUrl":59,"ownerDescription":60,"updatedAt":61,"install":62,"resources":75},"01M71MH3GRYFP06ASRK7G0CGR9","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.","iDorgham\u002FWorkspace-Factory","iDorgham","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F136521493?v=4","01M6CNGBDAHE4ARW3YJEF4X2FD","Dorgham","01M71E8H3CXZ7DJX59MTM9JEB2",1,"MIT","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_elevenlabs_speech_to_text\u002Fskill.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41],"speech","speech to text","text","content","meetings","subtitles","use","transcribe","elevenlabs","scribe","api","transcribe audio to text","when","access","internet","Audio","processing","Video","2026-11-10 10:56:23",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 - 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",{"name":5,"license":15,"metadata":47,"description":7,"compatibility":52},{"openclaw":48},{"requires":49,"primaryEnv":51},{"env":50},[51],"ELEVENLABS_API_KEY","Requires internet access and an ElevenLabs API key (ELEVENLABS_API_KEY).","master","c2e410e11df4d80d885e0ecf0583ef3c926df736","factory\u002Flibrary\u002Fskills\u002Fofficial_elevenlabs_speech_to_text",0,"https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory","https:\u002F\u002Fgithub.com\u002FiDorgham","Dorgham.me","hey I'm Dorgham \r\nI code, I create, and AI codes back. \r\nMaybe we can give AI the chance!! 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This skill should be used when writing, reviewing, or refactoring React\u002FNext.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_vercel_labs_react_best_practices\u002Fskill.md",[135],{"categoryNo":84,"categoryName":85,"taxonomyType":21,"taxonomyTypeDesc":22},[87,137,138,91,139,119,140,36,141,121,101,142,143,144,145,146,147,148,149,150],"react best practices","vercel react best practices","best practices","writing","code","data fetching","react components","next","refactoring","data","patterns","Optimization","guidelines","triggers",{"skillNo":152,"name":153,"nameZh":6,"description":154,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":155,"categories":156,"keywords":158,"publishedAt":105},"01M71P8HVEA70K5N6ZVJCG2ED3","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\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_vercel_labs_composition_patterns\u002Fskill.md",[157],{"categoryNo":84,"categoryName":85,"taxonomyType":21,"taxonomyTypeDesc":22},[159,91,160,147,161,162,163,164,150,165,166,30,36,167,121,101,168,169,34,87],"Composition","composition patterns","vercel composition patterns","react composition patterns","react 19","render","boolean","component architecture","reusable","architecture","react composition",{"skillNo":171,"name":172,"nameZh":6,"description":173,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":174,"categories":175,"keywords":179,"publishedAt":105},"01M71P60XXKKP222ZFJRG7R373","supabase-postgres-best-practices","Postgres performance optimization and best practices from Supabase. Use this skill when writing, reviewing, or optimizing Postgres queries, schema designs, or database configurations.","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_supabase_supabase_postgres_best_practices\u002Fskill.md",[176],{"categoryNo":177,"categoryName":178,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",[180,181,182,183,139,184,185,186,128,148,119,187,188,140,189,30,190,191,192,36],"postgres best practices","supabase","postgres","supabase postgres best practices","categories","postgres performance","advanced","correct","schema","impact","Rules","critical","database",{"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":42},"01M71P2ZCEJD0PKC458YTP1FJW","portable-text-serialization","Render and serialize Portable Text to React, Svelte, Vue, Astro, HTML, Markdown, and plain text. Use when implementing Portable Text rendering in any frontend framework, building custom serializers for non-standard block types, converting Portable Text to HTML strings server-side, converting Portable Text to Markdown, extracting plain text from Portable Text, or troubleshooting rendering issues with marks, blocks, lists, or custom types.","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_sanity_io_portable_text_serialization\u002Fskill.md",[199],{"categoryNo":84,"categoryName":85,"taxonomyType":21,"taxonomyTypeDesc":22},[26,201,202,203,204,97,205,164,206,100,207,208,36,87,209,210,211,212,213,214],"portable text serialization","portable","serialization","extracting","strings","issues","frontend","svelte","sanity","astro","server","markdown","rendering","vue"]