[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7CZ2FAQMX1MXVS910B8HBM7":3,"skill-same:01M7CZ2FAQMX1MXVS910B8HBM7":76},{"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":17,"publishedAt":18,"linkOnly":19,"markdownContent":20,"markdownBody":21,"frontMatter":22,"refName":29,"commitSha":30,"skillDirectory":31,"forksCount":13,"repositoryHtmlUrl":32,"repositoryHomepageUrl":33,"ownerHtmlUrl":34,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":35,"install":36,"resources":49},"01M7CZ2FAQMX1MXVS910B8HBM7","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.","makemoney2023\u002Fstrongfoam","makemoney2023","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F124006256?v=4","01M79SG90P20MHWQ1HA5ZD90F0","01M7CWAB570G9BKA9NN3P1NZXS",0,"MIT","https:\u002F\u002Fgithub.com\u002Fmakemoney2023\u002Fstrongfoam\u002Fblob\u002Fmain\u002F.cursor\u002Fskills\u002Fclaude-skills\u002Fcommunity\u002Fopenmontage\u002F.agents\u002Fskills\u002Fspeech-to-text\u002FSKILL.md",[],[],"2026-11-15 14:19:17",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":14,"metadata":23,"description":7,"compatibility":28},{"openclaw":24},{"requires":25,"primaryEnv":27},{"env":26},[27],"ELEVENLABS_API_KEY","Requires internet access and an ElevenLabs API key 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beautiful, accessible user interfaces with shadcn\u002Fui components (built on Radix UI + Tailwind), Tailwind CSS utility-first styling, and canvas-based visual designs. Use when building user interfaces, implementing design systems, creating responsive layouts, adding accessible components (dialogs, dropdowns, forms, tables), customizing themes and colors, implementing dark mode, generating visual designs and posters, or establishing consistent styling patterns across applications.","https:\u002F\u002Fgithub.com\u002Fmakemoney2023\u002Fstrongfoam\u002Fblob\u002Fmain\u002F.cursor\u002Fskills\u002Fui-styling\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fmakemoney2023\u002Fstrongfoam\u002Fblob\u002Fmain\u002F.cursor\u002Fskills\u002Fui-styling\u002FSKILL.md",[],[],{"skillNo":86,"name":87,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":89,"sourceHtmlUrl":90,"categories":91,"keywords":92,"publishedAt":18},"01M7D1X7TWSR188NC5PC18RBWB","scroll-craft","Premium scroll-driven landing pages. Scroll is the timeline: video scrubs, sections pin, rails pan, headlines assemble, the ground shifts colour. Interviews first, then locks a page grammar, one feeling peak, a signature move, and a fingerprint so two builds cannot share a skeleton. Use when the user or seat wants scrollytelling, an Apple-style landing, a unique scroll site, or when a template-looking page must be rejected.","https:\u002F\u002Fgithub.com\u002Fmakemoney2023\u002Fstrongfoam\u002Fblob\u002Fmain\u002F.cursor\u002Fskills\u002Fscroll-craft\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fmakemoney2023\u002Fstrongfoam\u002Fblob\u002Fmain\u002F.cursor\u002Fskills\u002Fscroll-craft\u002FSKILL.md",[],[],{"skillNo":94,"name":95,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":97,"categories":98,"keywords":99,"publishedAt":18},"01M7D1KXG5YAW1S7M9HPPZEWYZ","design","Comprehensive design skill: brand identity, design tokens, UI styling, logo generation (55 styles, Gemini AI), corporate identity program (50 deliverables, CIP mockups), HTML presentations (Chart.js), banner design (22 styles, social\u002Fads\u002Fweb\u002Fprint), icon design (15 styles, SVG, Gemini 3.1 Pro), social photos (HTML→screenshot, multi-platform). Actions: design logo, create CIP, generate mockups, build slides, design banner, generate icon, create social photos, social media images, brand identity, design system. Platforms: Facebook, Twitter, LinkedIn, YouTube, Instagram, Pinterest, TikTok, Threads, Google Ads.","https:\u002F\u002Fgithub.com\u002Fmakemoney2023\u002Fstrongfoam\u002Fblob\u002Fmain\u002F.cursor\u002Fskills\u002Fdesign\u002FSKILL.md",[],[],{"skillNo":101,"name":102,"nameZh":6,"description":103,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":104,"categories":105,"keywords":106,"publishedAt":18},"01M7D1KC08QRK4YTQ3DZAB8HAF","design-system","Token architecture, component specifications, and slide generation. Three-layer tokens (primitive→semantic→component), CSS variables, spacing\u002Ftypography scales, component specs, strategic slide creation. Use for design tokens, systematic design, brand-compliant presentations.","https:\u002F\u002Fgithub.com\u002Fmakemoney2023\u002Fstrongfoam\u002Fblob\u002Fmain\u002F.cursor\u002Fskills\u002Fdesign-system\u002FSKILL.md",[],[],{"skillNo":108,"name":109,"nameZh":6,"description":110,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":111,"categories":112,"keywords":113,"publishedAt":18},"01M7D19YKR6AHSNDC5S48WP487","vercel-react-best-practices","React and Next.js performance optimization guidelines from Vercel Engineering. 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\u002Fmakemoney2023\u002Fstrongfoam\u002Fblob\u002Fmain\u002F.cursor\u002Fskills\u002Fclaude-skills\u002Fplugins\u002Fvercel\u002Freact-best-practices\u002Fupstream\u002FSKILL.md",[],[],{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":120,"publishedAt":18},"01M7D0XDR7W3QHTTT27T9DB56J","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\u002Fmakemoney2023\u002Fstrongfoam\u002Fblob\u002Fmain\u002F.cursor\u002Fskills\u002Fclaude-skills\u002Fplugins\u002Fsupabase\u002Fsupabase-postgres-best-practices\u002FSKILL.md",[],[]]