[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6EE6MF7RPWEPWMQDX8VDK4A":3,"skill-same:01M6EE6MF7RPWEPWMQDX8VDK4A":95},{"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":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":63,"commitSha":64,"skillDirectory":65,"forksCount":66,"repositoryHtmlUrl":67,"repositoryHomepageUrl":6,"ownerHtmlUrl":68,"ownerBlogUrl":6,"ownerDescription":69,"updatedAt":70,"install":71,"resources":84},"01M6EE6MF7RPWEPWMQDX8VDK4A","whisper",null,"OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.","luokai0\u002Fai-agent-skills-by-luo-kai","luokai0","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F266954973?v=4","01M6E16VNT42JB8B3X3254QWJ7","luo kai","01M6E16VTFVP6S5XJF1V1MPV90",14,"MIT","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F18-ai-agents-and-automation (by Luo Kai)\u002F16-other-agents\u002Fwhisper\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[5,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"Audio Processing","openai","processing","identification","english","translation","speech-to-text","transcription","multilingual","general","use","speech recognition","model","text","Audio","ASR","Transformers","multimodal","speech","2026-11-10 10:12:30",false,"---\nname: whisper\ndescription: OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.\nversion: 1.0.0\nauthor: luo-kai\nlicense: MIT\ntags: [Whisper, Speech Recognition, ASR, Multimodal, Multilingual, OpenAI, Speech-To-Text, Transcription, Translation, Audio Processing]\ndependencies: [openai-whisper, transformers, torch]\n---\n\n# Whisper - Robust Speech Recognition\n\nOpenAI's multilingual speech recognition model.\n\n## When to use Whisper\n\n**Use when:**\n- Speech-to-text transcription (99 languages)\n- Podcast\u002Fvideo transcription\n- Meeting notes automation\n- Translation to English\n- Noisy audio transcription\n- Multilingual audio processing\n\n**Metrics**:\n- **72,900+ GitHub stars**\n- 99 languages supported\n- Trained on 680,000 hours of audio\n- MIT License\n\n**Use alternatives instead**:\n- **AssemblyAI**: Managed API, speaker diarization\n- **Deepgram**: Real-time streaming ASR\n- **Google Speech-to-Text**: Cloud-based\n\n## Quick start\n\n### Installation\n\n```bash\n# Requires Python 3.8-3.11\npip install -U openai-whisper\n\n# Requires ffmpeg\n# macOS: brew install ffmpeg\n# Ubuntu: sudo apt install ffmpeg\n# Windows: choco install ffmpeg\n```\n\n### Basic transcription\n\n```python\nimport whisper\n\n# Load model\nmodel = whisper.load_model(\"base\")\n\n# Transcribe\nresult = model.transcribe(\"audio.mp3\")\n\n# Print text\nprint(result[\"text\"])\n\n# Access segments\nfor segment in result[\"segments\"]:\n    print(f\"[{segment['start']:.2f}s - {segment['end']:.2f}s] {segment['text']}\")\n```\n\n## Model sizes\n\n```python\n# Available models\nmodels = [\"tiny\", \"base\", \"small\", \"medium\", \"large\", \"turbo\"]\n\n# Load specific model\nmodel = whisper.load_model(\"turbo\")  # Fastest, good quality\n```\n\n| Model | Parameters | English-only | Multilingual | Speed | VRAM |\n|-------|------------|--------------|--------------|-------|------|\n| tiny | 39M | ✓ | ✓ | ~32x | ~1 GB |\n| base | 74M | ✓ | ✓ | ~16x | ~1 GB |\n| small | 244M | ✓ | ✓ | ~6x | ~2 GB |\n| medium | 769M | ✓ | ✓ | ~2x | ~5 GB |\n| large | 1550M | ✗ | ✓ | 1x | ~10 GB |\n| turbo | 809M | ✗ | ✓ | ~8x | ~6 GB |\n\n**Recommendation**: Use `turbo` for best speed\u002Fquality, `base` for prototyping\n\n## Transcription options\n\n### Language specification\n\n```python\n# Auto-detect language\nresult = model.transcribe(\"audio.mp3\")\n\n# Specify language (faster)\nresult = model.transcribe(\"audio.mp3\", language=\"en\")\n\n# Supported: en, es, fr, de, it, pt, ru, ja, ko, zh, and 89 more\n```\n\n### Task selection\n\n```python\n# Transcription (default)\nresult = model.transcribe(\"audio.mp3\", task=\"transcribe\")\n\n# Translation to English\nresult = model.transcribe(\"spanish.mp3\", task=\"translate\")\n# Input: Spanish audio → Output: English text\n```\n\n### Initial prompt\n\n```python\n# Improve accuracy with context\nresult = model.transcribe(\n    \"audio.mp3\",\n    initial_prompt=\"This is a technical podcast about machine learning and AI.\"\n)\n\n# Helps with:\n# - Technical terms\n# - Proper nouns\n# - Domain-specific vocabulary\n```\n\n### Timestamps\n\n```python\n# Word-level timestamps\nresult = model.transcribe(\"audio.mp3\", word_timestamps=True)\n\nfor segment in result[\"segments\"]:\n    for word in segment[\"words\"]:\n        print(f\"{word['word']} ({word['start']:.2f}s - {word['end']:.2f}s)\")\n```\n\n### Temperature fallback\n\n```python\n# Retry with different temperatures if confidence low\nresult = model.transcribe(\n    \"audio.mp3\",\n    temperature=(0.0, 0.2, 0.4, 0.6, 0.8, 1.0)\n)\n```\n\n## Command line usage\n\n```bash\n# Basic transcription\nwhisper audio.mp3\n\n# Specify model\nwhisper audio.mp3 --model turbo\n\n# Output formats\nwhisper audio.mp3 --output_format txt     # Plain text\nwhisper audio.mp3 --output_format srt     # Subtitles\nwhisper audio.mp3 --output_format vtt     # WebVTT\nwhisper audio.mp3 --output_format json    # JSON with timestamps\n\n# Language\nwhisper audio.mp3 --language Spanish\n\n# Translation\nwhisper spanish.mp3 --task translate\n```\n\n## Batch processing\n\n```python\nimport os\n\naudio_files = [\"file1.mp3\", \"file2.mp3\", \"file3.mp3\"]\n\nfor audio_file in audio_files:\n    print(f\"Transcribing {audio_file}...\")\n    result = model.transcribe(audio_file)\n\n    # Save to file\n    output_file = audio_file.replace(\".mp3\", \".txt\")\n    with open(output_file, \"w\") as f:\n        f.write(result[\"text\"])\n```\n\n## Real-time transcription\n\n```python\n# For streaming audio, use faster-whisper\n# pip install faster-whisper\n\nfrom faster_whisper import WhisperModel\n\nmodel = WhisperModel(\"base\", device=\"cuda\", compute_type=\"float16\")\n\n# Transcribe with streaming\nsegments, info = model.transcribe(\"audio.mp3\", beam_size=5)\n\nfor segment in segments:\n    print(f\"[{segment.start:.2f}s -> {segment.end:.2f}s] {segment.text}\")\n```\n\n## GPU acceleration\n\n```python\nimport whisper\n\n# Automatically uses GPU if available\nmodel = whisper.load_model(\"turbo\")\n\n# Force CPU\nmodel = whisper.load_model(\"turbo\", device=\"cpu\")\n\n# Force GPU\nmodel = whisper.load_model(\"turbo\", device=\"cuda\")\n\n# 10-20× faster on GPU\n```\n\n## Integration with other tools\n\n### Subtitle generation\n\n```bash\n# Generate SRT subtitles\nwhisper video.mp4 --output_format srt --language English\n\n# Output: video.srt\n```\n\n### With LangChain\n\n```python\nfrom langchain.document_loaders import WhisperTranscriptionLoader\n\nloader = WhisperTranscriptionLoader(file_path=\"audio.mp3\")\ndocs = loader.load()\n\n# Use transcription in RAG\nfrom langchain_chroma import Chroma\nfrom langchain_openai import OpenAIEmbeddings\n\nvectorstore = Chroma.from_documents(docs, OpenAIEmbeddings())\n```\n\n### Extract audio from video\n\n```bash\n# Use ffmpeg to extract audio\nffmpeg -i video.mp4 -vn -acodec pcm_s16le audio.wav\n\n# Then transcribe\nwhisper audio.wav\n```\n\n## Best practices\n\n1. **Use turbo model** - Best speed\u002Fquality for English\n2. **Specify language** - Faster than auto-detect\n3. **Add initial prompt** - Improves technical terms\n4. **Use GPU** - 10-20× faster\n5. **Batch process** - More efficient\n6. **Convert to WAV** - Better compatibility\n7. **Split long audio** - \u003C30 min chunks\n8. **Check language support** - Quality varies by language\n9. **Use faster-whisper** - 4× faster than openai-whisper\n10. **Monitor VRAM** - Scale model size to hardware\n\n## Performance\n\n| Model | Real-time factor (CPU) | Real-time factor (GPU) |\n|-------|------------------------|------------------------|\n| tiny | ~0.32 | ~0.01 |\n| base | ~0.16 | ~0.01 |\n| turbo | ~0.08 | ~0.01 |\n| large | ~1.0 | ~0.05 |\n\n*Real-time factor: 0.1 = 10× faster than real-time*\n\n## Language support\n\nTop-supported languages:\n- English (en)\n- Spanish (es)\n- French (fr)\n- German (de)\n- Italian (it)\n- Portuguese (pt)\n- Russian (ru)\n- Japanese (ja)\n- Korean (ko)\n- Chinese (zh)\n\nFull list: 99 languages total\n\n## Limitations\n\n1. **Hallucinations** - May repeat or invent text\n2. **Long-form accuracy** - Degrades on >30 min audio\n3. **Speaker identification** - No diarization\n4. **Accents** - Quality varies\n5. **Background noise** - Can affect accuracy\n6. **Real-time latency** - Not suitable for live captioning\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fopenai\u002Fwhisper ⭐ 72,900+\n- **Paper**: https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.04356\n- **Model Card**: https:\u002F\u002Fgithub.com\u002Fopenai\u002Fwhisper\u002Fblob\u002Fmain\u002Fmodel-card.md\n- **Colab**: Available in repo\n- **License**: MIT\n\n\n","# Whisper - Robust Speech Recognition\n\nOpenAI's multilingual speech recognition model.\n\n## When to use Whisper\n\n**Use when:**\n- Speech-to-text transcription (99 languages)\n- Podcast\u002Fvideo transcription\n- Meeting notes automation\n- Translation to English\n- Noisy audio transcription\n- Multilingual audio processing\n\n**Metrics**:\n- **72,900+ GitHub stars**\n- 99 languages supported\n- Trained on 680,000 hours of audio\n- MIT License\n\n**Use alternatives instead**:\n- **AssemblyAI**: Managed API, speaker diarization\n- **Deepgram**: Real-time streaming ASR\n- **Google Speech-to-Text**: Cloud-based\n\n## Quick start\n\n### Installation\n\n```bash\n# Requires Python 3.8-3.11\npip install -U openai-whisper\n\n# Requires ffmpeg\n# macOS: brew install ffmpeg\n# Ubuntu: sudo apt install ffmpeg\n# Windows: choco install ffmpeg\n```\n\n### Basic transcription\n\n```python\nimport whisper\n\n# Load model\nmodel = whisper.load_model(\"base\")\n\n# Transcribe\nresult = model.transcribe(\"audio.mp3\")\n\n# Print text\nprint(result[\"text\"])\n\n# Access segments\nfor segment in result[\"segments\"]:\n    print(f\"[{segment['start']:.2f}s - {segment['end']:.2f}s] {segment['text']}\")\n```\n\n## Model sizes\n\n```python\n# Available models\nmodels = [\"tiny\", \"base\", \"small\", \"medium\", \"large\", \"turbo\"]\n\n# Load specific model\nmodel = whisper.load_model(\"turbo\")  # Fastest, good quality\n```\n\n| Model | Parameters | English-only | Multilingual | Speed | VRAM |\n|-------|------------|--------------|--------------|-------|------|\n| tiny | 39M | ✓ | ✓ | ~32x | ~1 GB |\n| base | 74M | ✓ | ✓ | ~16x | ~1 GB |\n| small | 244M | ✓ | ✓ | ~6x | ~2 GB |\n| medium | 769M | ✓ | ✓ | ~2x | ~5 GB |\n| large | 1550M | ✗ | ✓ | 1x | ~10 GB |\n| turbo | 809M | ✗ | ✓ | ~8x | ~6 GB |\n\n**Recommendation**: Use `turbo` for best speed\u002Fquality, `base` for prototyping\n\n## Transcription options\n\n### Language specification\n\n```python\n# Auto-detect language\nresult = model.transcribe(\"audio.mp3\")\n\n# Specify language (faster)\nresult = model.transcribe(\"audio.mp3\", language=\"en\")\n\n# Supported: en, es, fr, de, it, pt, ru, ja, ko, zh, and 89 more\n```\n\n### Task selection\n\n```python\n# Transcription (default)\nresult = model.transcribe(\"audio.mp3\", task=\"transcribe\")\n\n# Translation to English\nresult = model.transcribe(\"spanish.mp3\", task=\"translate\")\n# Input: Spanish audio → Output: English text\n```\n\n### Initial prompt\n\n```python\n# Improve accuracy with context\nresult = model.transcribe(\n    \"audio.mp3\",\n    initial_prompt=\"This is a technical podcast about machine learning and AI.\"\n)\n\n# Helps with:\n# - Technical terms\n# - Proper nouns\n# - Domain-specific vocabulary\n```\n\n### Timestamps\n\n```python\n# Word-level timestamps\nresult = model.transcribe(\"audio.mp3\", word_timestamps=True)\n\nfor segment in result[\"segments\"]:\n    for word in segment[\"words\"]:\n        print(f\"{word['word']} ({word['start']:.2f}s - {word['end']:.2f}s)\")\n```\n\n### Temperature fallback\n\n```python\n# Retry with different temperatures if confidence low\nresult = model.transcribe(\n    \"audio.mp3\",\n    temperature=(0.0, 0.2, 0.4, 0.6, 0.8, 1.0)\n)\n```\n\n## Command line usage\n\n```bash\n# Basic transcription\nwhisper audio.mp3\n\n# Specify model\nwhisper audio.mp3 --model turbo\n\n# Output formats\nwhisper audio.mp3 --output_format txt     # Plain text\nwhisper audio.mp3 --output_format srt     # Subtitles\nwhisper audio.mp3 --output_format vtt     # WebVTT\nwhisper audio.mp3 --output_format json    # JSON with timestamps\n\n# Language\nwhisper audio.mp3 --language Spanish\n\n# Translation\nwhisper spanish.mp3 --task translate\n```\n\n## Batch processing\n\n```python\nimport os\n\naudio_files = [\"file1.mp3\", \"file2.mp3\", \"file3.mp3\"]\n\nfor audio_file in audio_files:\n    print(f\"Transcribing {audio_file}...\")\n    result = model.transcribe(audio_file)\n\n    # Save to file\n    output_file = audio_file.replace(\".mp3\", \".txt\")\n    with open(output_file, \"w\") as f:\n        f.write(result[\"text\"])\n```\n\n## Real-time transcription\n\n```python\n# For streaming audio, use faster-whisper\n# pip install faster-whisper\n\nfrom faster_whisper import WhisperModel\n\nmodel = WhisperModel(\"base\", device=\"cuda\", compute_type=\"float16\")\n\n# Transcribe with streaming\nsegments, info = model.transcribe(\"audio.mp3\", beam_size=5)\n\nfor segment in segments:\n    print(f\"[{segment.start:.2f}s -> {segment.end:.2f}s] {segment.text}\")\n```\n\n## GPU acceleration\n\n```python\nimport whisper\n\n# Automatically uses GPU if available\nmodel = whisper.load_model(\"turbo\")\n\n# Force CPU\nmodel = whisper.load_model(\"turbo\", device=\"cpu\")\n\n# Force GPU\nmodel = whisper.load_model(\"turbo\", device=\"cuda\")\n\n# 10-20× faster on GPU\n```\n\n## Integration with other tools\n\n### Subtitle generation\n\n```bash\n# Generate SRT subtitles\nwhisper video.mp4 --output_format srt --language English\n\n# Output: video.srt\n```\n\n### With LangChain\n\n```python\nfrom langchain.document_loaders import WhisperTranscriptionLoader\n\nloader = WhisperTranscriptionLoader(file_path=\"audio.mp3\")\ndocs = loader.load()\n\n# Use transcription in RAG\nfrom langchain_chroma import Chroma\nfrom langchain_openai import OpenAIEmbeddings\n\nvectorstore = Chroma.from_documents(docs, OpenAIEmbeddings())\n```\n\n### Extract audio from video\n\n```bash\n# Use ffmpeg to extract audio\nffmpeg -i video.mp4 -vn -acodec pcm_s16le audio.wav\n\n# Then transcribe\nwhisper audio.wav\n```\n\n## Best practices\n\n1. **Use turbo model** - Best speed\u002Fquality for English\n2. **Specify language** - Faster than auto-detect\n3. **Add initial prompt** - Improves technical terms\n4. **Use GPU** - 10-20× faster\n5. **Batch process** - More efficient\n6. **Convert to WAV** - Better compatibility\n7. **Split long audio** - \u003C30 min chunks\n8. **Check language support** - Quality varies by language\n9. **Use faster-whisper** - 4× faster than openai-whisper\n10. **Monitor VRAM** - Scale model size to hardware\n\n## Performance\n\n| Model | Real-time factor (CPU) | Real-time factor (GPU) |\n|-------|------------------------|------------------------|\n| tiny | ~0.32 | ~0.01 |\n| base | ~0.16 | ~0.01 |\n| turbo | ~0.08 | ~0.01 |\n| large | ~1.0 | ~0.05 |\n\n*Real-time factor: 0.1 = 10× faster than real-time*\n\n## Language support\n\nTop-supported languages:\n- English (en)\n- Spanish (es)\n- French (fr)\n- German (de)\n- Italian (it)\n- Portuguese (pt)\n- Russian (ru)\n- Japanese (ja)\n- Korean (ko)\n- Chinese (zh)\n\nFull list: 99 languages total\n\n## Limitations\n\n1. **Hallucinations** - May repeat or invent text\n2. **Long-form accuracy** - Degrades on >30 min audio\n3. **Speaker identification** - No diarization\n4. **Accents** - Quality varies\n5. **Background noise** - Can affect accuracy\n6. **Real-time latency** - Not suitable for live captioning\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fopenai\u002Fwhisper ⭐ 72,900+\n- **Paper**: https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.04356\n- **Model Card**: https:\u002F\u002Fgithub.com\u002Fopenai\u002Fwhisper\u002Fblob\u002Fmain\u002Fmodel-card.md\n- **Colab**: Available in repo\n- **License**: MIT\n\n\n",{"name":5,"tags":48,"author":57,"license":15,"version":58,"description":7,"dependencies":59},[49,50,39,51,52,53,54,55,56,24],"Whisper","Speech Recognition","Multimodal","Multilingual","OpenAI","Speech-To-Text","Transcription","Translation","luo-kai","1.0.0",[60,61,62],"openai-whisper","transformers","torch","main","4d2fb89cdfb75f3f6fd3c6c5ec9ad90ea129eb3c","ai-agent-skills\u002F18-ai-agents-and-automation (by Luo Kai)\u002F16-other-agents\u002Fwhisper",1,"https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai","https:\u002F\u002Fgithub.com\u002Fluokai0","a solo builder.","2026-05-06 04:29:04",{"source":8,"skillName":5,"commands":72,"prompt":82,"zipAvailable":83},[73,76,79],{"tool":74,"command":75},"npx","npx skills add luokai0\u002Fai-agent-skills-by-luo-kai --skill whisper",{"tool":77,"command":78},"bunx","bunx skills add luokai0\u002Fai-agent-skills-by-luo-kai --skill whisper",{"tool":80,"command":81},"pnpm","pnpm dlx skills add luokai0\u002Fai-agent-skills-by-luo-kai --skill whisper","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F18-ai-agents-and-automation (by Luo Kai)\u002F16-other-agents\u002Fwhisper\u002FSKILL.md",true,[85,91],{"path":86,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":89,"sourceHtmlUrl":90},"agents\u002Fopenai.yaml","REFERENCE","参考文档",90,"https:\u002F\u002Fgithub.com\u002FThamielis\u002F.agents\u002Fblob\u002Fmain\u002Fplugins\u002Fvercel\u002Fskills\u002Fvercel-cli\u002Fagents\u002Fopenai.yaml",{"path":92,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":93,"sourceHtmlUrl":94},"references\u002Flanguages.md",4786,"https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F18-ai-agents-and-automation (by Luo Kai)\u002F16-other-agents\u002Fwhisper\u002Freferences\u002Flanguages.md",[96,122,139,167,199,235],{"skillNo":97,"name":98,"nameZh":6,"description":99,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":100,"categories":101,"keywords":102,"publishedAt":43},"01M6F2SKTE208174C7S7JJCZC7","write-contracts","Generates secure Aptos Move V2 smart contracts with Object model, Digital Asset integration, security patterns, and storage type guidance. 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Ask storage questions when: 'store', 'track', 'registry', 'mapping', 'list', 'collection'.","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Fwrite-contracts\u002FSKILL.md",[],[103,104,105,106,107,108,109,110,36,111,112,113,114,115,116,117,118,119,120,121],"write","contracts","write contracts","storage","secure","create","generate","collection","decision","ask","smart-contracts","selection","objects","structure","nft","token","aptos","critical","move",{"skillNo":123,"name":124,"nameZh":6,"description":125,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":126,"categories":127,"keywords":128,"publishedAt":43},"01M6F2RFXJ6T9MKW0A7N7GM45B","web-search","通用网络搜索技能，支持多引擎搜索（百度、必应、DuckDuckGo），无需API密钥即可获取实时信息","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Fweb-search-ex-skill\u002FSKILL.md",[],[129,130,131,132,133,134,135,136,137,138],"web","search","web search","搜索","baidu","bing","duckduckgo","百度","多引擎","internet",{"skillNo":140,"name":141,"nameZh":6,"description":142,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":143,"categories":144,"keywords":148,"publishedAt":43},"01M6F2K968G1FZEZFPW2E6B222","solo-build","Execute implementation plan tasks with TDD workflow, auto-commit, and phase gates. Use when user says \"build it\", \"start building\", \"execute plan\", \"implement tasks\", \"ship it\", or references a track ID. Do NOT use for planning (use \u002Fplan) or scaffolding (use \u002Fscaffold).","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Fsolo-build\u002FSKILL.md",[145],{"categoryNo":146,"categoryName":147,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[149,150,151,152,153,154,155,156,130,157,158,159,103,160,161,162,163,164,165,166],"build","solo","solo build","MCP","workflow","commit","tasks","code","Bash","plan","read","auto","references","context7","query","scaffolding","ship it","project",{"skillNo":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":171,"categories":172,"keywords":179,"publishedAt":43},"01M6F2GKN39SGHSPWYB3662YC8","react-email","Use when building HTML email templates with React components, adding a visual email editor to an application using the React Email visual editor, rendering emails to HTML, or sending emails with Resend. Covers welcome emails, password resets, notifications, order confirmations, newsletters, transactional emails, and the embeddable email editor component.","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Freact-email-skills\u002FSKILL.md",[173,176],{"categoryNo":174,"categoryName":175,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000005","邮件与沟通",{"categoryNo":177,"categoryName":178,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[180,181,182,183,184,185,186,187,188,189,190,191,192,34,193,194,195,196,197,198],"react","email","react email","github","editor","components","react components","component","welcome","https","notifications","password","node","html","resend","visual","emails","rendering","with",{"skillNo":200,"name":201,"nameZh":6,"description":202,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":203,"categories":204,"keywords":217,"publishedAt":43},"01M6F2FBVGVDFSG8SSFJGXA40C","APITester Agent-Driven API Testing","Test API endpoints and document responses. Define tests in plain English, run them, get formatted results. Agent-driven Postman alternative.","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Fopenclaw-api-tester\u002FSKILL.md",[205,208,211,212],{"categoryNo":206,"categoryName":207,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":209,"categoryName":210,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":146,"categoryName":147,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":213,"categoryName":214,"taxonomyType":215,"taxonomyTypeDesc":216},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,28,234],"testing","api","api testing","driven","APITester Agent Driven API Testing","test","run","postman","results","plain english","tester","endpoints","document","get","define","tests","api-tester",{"skillNo":236,"name":237,"nameZh":6,"description":238,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":239,"categories":240,"keywords":241,"publishedAt":43},"01M6F273Y3NGAPVJZ608BC0G9G","hubspot-implementation-plan","Generate a phased implementation plan from a HubSpot audit report. Creates prioritized, sequenced cleanup processes with effort estimates, dependencies, and automation feasibility. Use after running \u002Fhubspot-audit.","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Fhubspot-implementation-plan\u002FSKILL.md",[],[158,242,243,244,245,246,247,109,248,249,250,251,252,34,253,254,198,255],"implementation","hubspot","hubspot implementation plan","automation","feasibility","cleanup","planning","report","audit","audit report","phased implementation","dependencies","processes","hubspot audit"]