[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DCWSFWNKAWQEDGXH2PT1M4":3,"skill-same:01M6DCWSFWNKAWQEDGXH2PT1M4":68},{"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":48,"commitSha":49,"skillDirectory":50,"forksCount":14,"repositoryHtmlUrl":51,"repositoryHomepageUrl":6,"ownerHtmlUrl":52,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":53,"install":54,"resources":67},"01M6DCWSFWNKAWQEDGXH2PT1M4","reframe-transcribe",null,"Use when transcribing speech from audio\u002Fvideo to text with WORD-LEVEL timing (for captions, karaoke subtitles, clip-boundary snapping, or diarization). The proven local pipeline is faster-whisper (large-v3-turbo on GPU \u002F small on CPU) for the transcript, then a SEPARATE ctc-forced-aligner second.","Prekzursil\u002Fagent-skills-toolchain","Prekzursil","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F54636077?v=4","01M6DACZ1PCQHD99SCR1YXG28B","Andrei Mihai Visalon","01M6DACZ670WZF535V3WYYSS4Z",0,"MIT","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Fplatform\u002Frouting\u002Ffixtures\u002Fhome\u002F.agents\u002Fskills\u002Freframe-transcribe\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"reframe","transcribe","reframe transcribe","Captions","v3","local","whisper","clip","use","text","Word","transcript","Audio","Video","boundary","pipeline","subtitles","gpu","speech","2026-11-10 10:12:30",false,"---\nname: reframe-transcribe\ndescription: \"Use when transcribing speech from audio\u002Fvideo to text with WORD-LEVEL timing (for captions, karaoke subtitles, clip-boundary snapping, or diarization). The proven local pipeline is faster-whisper (large-v3-turbo on GPU \u002F small on CPU) for the transcript, then a SEPARATE ctc-forced-aligner second.\"\n---\n\n# Transcribe (speech to timed text)\n\nGoal: turn an audio\u002Fvideo file into a **Transcript** — language + segments +\n**per-word `{text, start, end}`** — that is accurate, correctly timed at the word\nlevel, and degrades loudly (never silently) when a GPU or model is missing.\n\nThis skill distills the **real, shipped** pipeline from Reframe\n(`sidecar\u002Fmedia_studio\u002Ffeatures\u002Ftranscribe.py`, `parakeet_asr.py`,\n`ctc_align.py` + their `_backend.py` siblings) plus current Hugging Face model\noptions. The architecture is deliberately **two passes**, and the single most\nimportant design decision is *why it is not WhisperX*.\n\n---\n\n## 0. The headline decision: two passes, NOT WhisperX\n\nWhisperX = faster-whisper + a bundled forced-alignment stage + optional pyannote.\nIts alignment stage loads a **language-specific wav2vec2** model keyed by the\ndetected language. That is the trap:\n\n- **Romanian (and many non-EN languages) have no good\u002Fany bundled wav2vec2\n  alignment model in WhisperX's map** → it either falls back to a wrong model,\n  produces garbage word times, or crashes outright on the missing key.\n- It couples ASR and alignment, so you cannot swap ASR engines independently.\n\n**Reframe's fix (adopt this):** keep ASR and alignment as **two independent\nseams**:\n\n1. **ASR pass** → faster-whisper (default) or Parakeet (opt-in) → segment + rough\n   word times in the `Transcript` schema.\n2. **Alignment pass** → a *separate* `ctc-forced-aligner` (MahmoudAshraf) call\n   that re-derives word boundaries. Its **default model is MMS-300M\n\n### 1. The Transcript schema (the contract everything speaks)\n### 2. The real recipe\n### 2.1 Dependencies\n### ASR (default engine) — CTranslate2 backend, fast + low VRAM\n### Word-level alignment second pass (the karaoke timing)\n### Opt-in alternative engine (multilingual transducer)\n### ffmpeg must be on PATH (audio decode for the aligner + duration probe)\n### 2.2 Pass 1 — ASR (faster-whisper)\n### Device\u002Fcompute policy (Reframe DEFAULT_*):\n### GPU present -> \"cuda\" + \"float16\" + model \"large-v3-turbo\"\n### CPU only    -> \"cpu\"  + \"int8\"    + model \"small\"  (turbo is impractically slow on CPU)\n### transcribe() returns a LAZY generator + info. Work happens as you iterate —\n","# Transcribe (speech to timed text)\n\nGoal: turn an audio\u002Fvideo file into a **Transcript** — language + segments +\n**per-word `{text, start, end}`** — that is accurate, correctly timed at the word\nlevel, and degrades loudly (never silently) when a GPU or model is missing.\n\nThis skill distills the **real, shipped** pipeline from Reframe\n(`sidecar\u002Fmedia_studio\u002Ffeatures\u002Ftranscribe.py`, `parakeet_asr.py`,\n`ctc_align.py` + their `_backend.py` siblings) plus current Hugging Face model\noptions. The architecture is deliberately **two passes**, and the single most\nimportant design decision is *why it is not WhisperX*.\n\n---\n\n## 0. The headline decision: two passes, NOT WhisperX\n\nWhisperX = faster-whisper + a bundled forced-alignment stage + optional pyannote.\nIts alignment stage loads a **language-specific wav2vec2** model keyed by the\ndetected language. That is the trap:\n\n- **Romanian (and many non-EN languages) have no good\u002Fany bundled wav2vec2\n  alignment model in WhisperX's map** → it either falls back to a wrong model,\n  produces garbage word times, or crashes outright on the missing key.\n- It couples ASR and alignment, so you cannot swap ASR engines independently.\n\n**Reframe's fix (adopt this):** keep ASR and alignment as **two independent\nseams**:\n\n1. **ASR pass** → faster-whisper (default) or Parakeet (opt-in) → segment + rough\n   word times in the `Transcript` schema.\n2. **Alignment pass** → a *separate* `ctc-forced-aligner` (MahmoudAshraf) call\n   that re-derives word boundaries. Its **default model is MMS-300M\n\n### 1. The Transcript schema (the contract everything speaks)\n### 2. The real recipe\n### 2.1 Dependencies\n### ASR (default engine) — CTranslate2 backend, fast + low VRAM\n### Word-level alignment second pass (the karaoke timing)\n### Opt-in alternative engine (multilingual transducer)\n### ffmpeg must be on PATH (audio decode for the aligner + duration probe)\n### 2.2 Pass 1 — ASR (faster-whisper)\n### Device\u002Fcompute policy (Reframe DEFAULT_*):\n### GPU present -> \"cuda\" + \"float16\" + model \"large-v3-turbo\"\n### CPU only    -> \"cpu\"  + \"int8\"    + model \"small\"  (turbo is impractically slow on CPU)\n### transcribe() returns a LAZY generator + info. Work happens as you iterate —\n",{"name":5,"description":7},"main","a298c929b6cbd662e1fce54302ce520d5ceea1a5","platform\u002Frouting\u002Ffixtures\u002Fhome\u002F.agents\u002Fskills\u002Freframe-transcribe","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain","https:\u002F\u002Fgithub.com\u002FPrekzursil","2026-11-02 14:30:57",{"source":8,"skillName":5,"commands":55,"prompt":65,"zipAvailable":66},[56,59,62],{"tool":57,"command":58},"npx","npx skills add Prekzursil\u002Fagent-skills-toolchain --skill reframe-transcribe",{"tool":60,"command":61},"bunx","bunx skills add Prekzursil\u002Fagent-skills-toolchain --skill reframe-transcribe",{"tool":63,"command":64},"pnpm","pnpm dlx skills add Prekzursil\u002Fagent-skills-toolchain --skill reframe-transcribe","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Fplatform\u002Frouting\u002Ffixtures\u002Fhome\u002F.agents\u002Fskills\u002Freframe-transcribe\u002FSKILL.md",true,[],[69,90,117,144,158,174],{"skillNo":70,"name":71,"nameZh":6,"description":72,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":73,"categories":74,"keywords":75,"publishedAt":43},"01M6DF7D7478R55P46REYH4QAC","clarify-requirements","Resolve genuine pre-execution ambiguity by scanning repo context, generating 1-6 grounded options, and asking 1-3 focused questions via AskUserQuestion. NOT for mid-task use (injection risk); NOT when the goal is already unambiguous; use to disambiguate vague prompts before any file edits.","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Fworkspace-hooks\u002Fskills\u002Fclarify-requirements\u002FSKILL.md",[],[76,77,78,79,80,81,82,83,84,32,85,86,87,88,89],"clarify requirements","clarify","requirements","risk","options","task","prompts","goal","scanning","context","repo","injection","execution","file",{"skillNo":91,"name":92,"nameZh":6,"description":93,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":94,"categories":95,"keywords":99,"publishedAt":43},"01M6DF79CA4BHJS66J8EZDBPWJ","openmm-md","Run FREE, open-source, Windows-native GPU molecular dynamics via the openmm-md MCP (OpenMM + CUDA on the local NVIDIA GPU). Use for protein \u002F protein-ligand MD when you want a free stack or are on Windows without a Schrodinger license. Covers the full workflow (prepare, solvate, minimize, equilibrate, production, analyze, mmgbsa) AND an honest decision guide vs Schrodinger Desmond, including how to run Desmond legitimately on Linux or a cloud GPU with a real license. This is a deliberately LESSER free alternative and does NOT replace the schrodinger MCP tools. Do NOT use it to pick a free tool when a validated FEP+, WaterMap, IFD-MD, or Prime MM-GBSA result is actually required.","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Fmcp\u002Fopenmm-md-mcp\u002Fskill\u002Fopenmm-md\u002FSKILL.md",[96],{"categoryNo":97,"categoryName":98,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[100,101,102,103,104,105,106,107,108,109,41,110,111,112,32,113,114,29,115,116],"openmm md","how","decision","pick","linux","analyze","tool","stack","open","NVIDIA","license","workflow","windows","free","molecular dynamics","do","MCP",{"skillNo":118,"name":119,"nameZh":6,"description":120,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":121,"categories":122,"keywords":129,"publishedAt":43},"01M6DF76C1MG85VEWP8VZCBNMN","visual-review","Take screenshots of web pages and UI using Playwright for visual review and iteration","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fvisual-review\u002FSKILL.md",[123,126],{"categoryNo":124,"categoryName":125,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":127,"categoryName":128,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[130,131,132,133,134,135,136,137,138,139,101,140,141,142,143],"review","visual","visual review","pages","web","false","iteration","take","check","slides","ui","screenshot","playwright","screenshots",{"skillNo":145,"name":146,"nameZh":6,"description":147,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":148,"categories":149,"keywords":150,"publishedAt":43},"01M6DF73AQVCBZKEN0YXEX9FXP","status","Diagnostic status report — shows metaswarm installation state, project setup, and potential issues","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fstatus\u002FSKILL.md",[],[146,151,152,153,154,155,156,157],"setup","report","project","installation","project setup","issues","state",{"skillNo":159,"name":160,"nameZh":6,"description":161,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":162,"categories":163,"keywords":165,"publishedAt":43},"01M6DF6X6C2XB0P6GX76P7TTYV","start","Use when starting work on any task, when the user mentions metaswarm, or when the user wants to begin tracked development work","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fstart\u002FSKILL.md",[164],{"categoryNo":97,"categoryName":98,"taxonomyType":21,"taxonomyTypeDesc":22},[160,166,81,167,168,169,32,170,171,172,173],"start issue","issue","ready","work on issue","user","work","work on","development",{"skillNo":175,"name":151,"nameZh":6,"description":176,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":177,"categories":178,"keywords":179,"publishedAt":43},"01M6DF6QMJ9AJKFKHHKRT005PQ","Interactive project setup — detects your project, configures metaswarm, writes project-local files","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fsetup\u002FSKILL.md",[],[151,153,29,180,155,181],"files","interactive"]