[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XG6H6EM1RJEFBVN8TDEPRR":3,"skill-same:01M6XG6H6EM1RJEFBVN8TDEPRR":90},{"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":64,"commitSha":65,"skillDirectory":66,"forksCount":14,"repositoryHtmlUrl":67,"repositoryHomepageUrl":6,"ownerHtmlUrl":68,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":69,"install":70,"resources":83},"01M6XG6H6EM1RJEFBVN8TDEPRR","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.","fadrienne\u002FMyGitHub","fadrienne","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F197874571?v=4","01M6X64V8VWCRSMFZ4EPS2BWXG","Francesca Adrienne","01M6X64VD5MW4F9PHDWQKS7ZAF",0,"MIT","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\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],"for","openai","Audio Processing","processing","identification","english","synthetic","translation","multilingual","general","speech recognition","model","text","Audio","Tools","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.\ncategory: llm-tools\nversion: 1.0.0\nauthor: Synthetic Sciences\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,"category":59,"description":7,"dependencies":60},[49,50,39,51,52,53,54,55,56,26],"Whisper","Speech Recognition","Multimodal","Multilingual","OpenAI","Speech-To-Text","Transcription","Translation","Synthetic Sciences","1.0.0","llm-tools",[61,62,63],"openai-whisper","transformers","torch","main","4aa39efd1cdab0b4657ecee16c92a92011a5b10f","openscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fwhisper","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub","https:\u002F\u002Fgithub.com\u002Ffadrienne","2026-10-28 00:43:09",{"source":8,"skillName":5,"commands":71,"prompt":81,"zipAvailable":82},[72,75,78],{"tool":73,"command":74},"npx","npx skills add fadrienne\u002FMyGitHub --skill whisper",{"tool":76,"command":77},"bunx","bunx skills add fadrienne\u002FMyGitHub --skill whisper",{"tool":79,"command":80},"pnpm","pnpm dlx skills add fadrienne\u002FMyGitHub --skill whisper","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fwhisper\u002FSKILL.md",true,[84],{"path":85,"resourceType":86,"resourceTypeDesc":87,"sizeBytes":88,"sourceHtmlUrl":89},"references\u002Flanguages.md","REFERENCE","参考文档",4786,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fwhisper\u002Freferences\u002Flanguages.md",[91,121,147,181,208,235],{"skillNo":92,"name":93,"nameZh":6,"description":94,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":95,"categories":96,"keywords":100,"publishedAt":43},"01M7167B28PK20HP7R7ZED2DY0","statistical-power","Sample size calculation, power analysis, and effect size conversion for scientific studies. Use BEFORE data collection (a priori) to determine required N, or after (post-hoc) to compute achieved power. Covers parametric tests (t-test, ANOVA, chi-square, proportions, correlation, regression), simulation-based power for complex\u002Fclustered\u002Fnested designs, and power curves. Use this skill when someone asks 'how many samples do I need', 'what is my power', 'what effect size can I detect', or any variant. Pairs with \u002Fexperimental-design (choose design first, then compute power) and \u002Fstatistical-analysis (analyze data after collection).","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-power\u002FSKILL.md",[97],{"categoryNo":98,"categoryName":99,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120],"power","statistical power","effect","scipy","regression","choose","design","uv","when","Bash","collection","analysis","calculation","ANOVA","conversion","analyze","t-test","chi-square","simulation","sample",{"skillNo":122,"name":123,"nameZh":6,"description":124,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":125,"categories":126,"keywords":130,"publishedAt":43},"01M71677EBCM8EPCE9BZCK8505","statistical-analysis","Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-analysis\u002FSKILL.md",[127],{"categoryNo":128,"categoryName":129,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[112,131,101,132,133,134,109,135,136,137,24,138,139,140,141,142,143,144,145,146],"statistical analysis","academic research","implementing","selection","you","reporting","data","tests","use","with","test","research","results","statsmodels","models","help",{"skillNo":148,"name":149,"nameZh":6,"description":150,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":151,"categories":152,"keywords":162,"publishedAt":43},"01M71673R93XAHFH81DBNVNG7H","scientific-writing","Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA\u002FAMA\u002FVancouver), figures\u002Ftables, reporting guidelines (CONSORT\u002FSTROBE\u002FPRISMA), for research papers and journal submissions.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-writing\u002FSKILL.md",[153,156,157],{"categoryNo":154,"categoryName":155,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":98,"categoryName":99,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":158,"categoryName":159,"taxonomyType":160,"taxonomyTypeDesc":161},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[163,164,165,166,167,168,169,170,171,172,110,173,174,175,176,177,178,179,136,180],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","api","prisma","structure","openrouter","Outlines","core skill","read","features",{"skillNo":182,"name":183,"nameZh":6,"description":184,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":185,"categories":186,"keywords":191,"publishedAt":43},"01M7166ZPKXBF4EBPHVTJPQBB1","scientific-visualization","Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib\u002Fseaborn\u002Fplotly with publication styles. For quick exploration use seaborn or plotly directly.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-visualization\u002FSKILL.md",[187,190],{"categoryNo":188,"categoryName":189,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":98,"categoryName":99,"taxonomyType":21,"taxonomyTypeDesc":22},[192,164,193,194,195,196,197,198,199,171,109,200,201,202,203,204,205,206,24,207],"visualization","scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"skillNo":209,"name":210,"nameZh":6,"description":211,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":212,"categories":213,"keywords":220,"publishedAt":43},"01M7166VXJWQSSC3RQZP60WBGR","research-code","Debug, profile, review, and restructure Python research and analysis code. Use when analysis scripts crash, produce wrong results, run too slowly, or need a correctness review. Covers: reading tracebacks and diagnosing root causes, hypothesis-driven debugging, reviewing code for common statistical\u002Fnumerical errors, profiling data pipelines, improving reproducibility (seeds, environments, determinism), and structuring analysis scripts for sharing. Distinct from \u002Fexploratory-data-analysis (which understands data) and \u002Fstatistical-analysis (which chooses and runs tests) — this skill focuses on the code itself.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fresearch-code\u002FSKILL.md",[214,217],{"categoryNo":215,"categoryName":216,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":218,"categoryName":219,"taxonomyType":160,"taxonomyTypeDesc":161},"01JBSEEDCAT000000000000030","开发工程师",[221,222,142,223,224,225,226,108,227,109,110,112,228,229,230,231,232,233,179,234],"code","research code","which","memory","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line","write",{"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":239,"licenseFileUrl":6,"sourceHtmlUrl":240,"categories":241,"keywords":248,"publishedAt":43},"01M7166CF5HSR7SVZYZFWTDX55","markdown-mermaid-writing","Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fmarkdown-mermaid-writing\u002FSKILL.md",[242,245,246,247],{"categoryNo":243,"categoryName":244,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":154,"categoryName":155,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":98,"categoryName":99,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":158,"categoryName":159,"taxonomyType":160,"taxonomyTypeDesc":161},[163,249,250,251,252,253,192,254,255,109,110,112,256,257,36,258,259,260,164,179],"markdown mermaid writing","mermaid","markdown","community","diagrams","project","default","team","guides","documentation","references","diagram"]