[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6P5HGE79PVRF8RXF2BF9NPF":3,"skill-same:01M6P5HGE79PVRF8RXF2BF9NPF":66},{"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":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":20,"commitSha":47,"skillDirectory":48,"forksCount":13,"repositoryHtmlUrl":49,"repositoryHomepageUrl":6,"ownerHtmlUrl":50,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":51,"install":52,"resources":65},"01M6P5HGE79PVRF8RXF2BF9NPF","huggingface-best",null,"Use when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores.","duc-duy-1612\u002FOnline-Delivery-Systems-Web","duc-duy-1612","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F192231384?v=4","01M6NSDKQQDKVTHZ8DZMZ1VYY6","01M6NSDKW8DDVTQHBT1HBDDJPT",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fduc-duy-1612\u002FOnline-Delivery-Systems-Web\u002Fblob\u002Fmain\u002F.agents\u002Fskills_archive\u002Fhuggingface-best\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"HuggingFace","huggingface best","main","for","tree","top","about","compare","compare models","critical","wed","github","task","models","use","model","https","license","benchmark","official","2026-11-10 10:12:30",false,"---\nname: huggingface-best\ndescription: \"Use when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores.\"\nrisk: critical\nsource: https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fskills\u002Ftree\u002Fmain\u002Fskills\u002Fhuggingface-best\nsource_repo: huggingface\u002Fskills\nsource_type: official\ndate_added: 2026-07-01\nlicense: Apache-2.0\nlicense_source: https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fskills\u002Fblob\u002Fmain\u002FLICENSE\n---\n\n# HuggingFace Best Model Finder\n## When to Use\n\nUse when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores. Triggers on: \"best model for X\", \"what model should I use for\", \"top models for [task]\", \"which model runs on my...\n\n\nFinds the best models for a task by querying official HF benchmark leaderboards, enriching\nresults with model size data, filtering for what fits on the user's device, and returning a\ncomparison table with benchmark scores.\n\n---\n\n## Step 1: Parse the request\n\nExtract from the user's message:\n- **Task**: what they want the model to do (coding, math\u002Freasoning, chat, OCR, RAG\u002Fretrieval, speech recognition, image classification, multimodal, agents, etc.)\n- **Device**: hardware constraints (MacBook M-series 8\u002F16\u002F32\u002F64GB unified memory, RTX GPU with VRAM amount, CPU-only, cloud\u002Fno constraint, etc.)\n\nIf device is not mentioned, skip filtering entirely and return the highest-performing models regardless of size. If the task is genuinely ambiguous, ask one clarifying question.\n\n### Device → max parameter budget\n\nWhen a device is specified, extract its available memory (unified RAM for Apple Silicon, VRAM for discrete GPUs) and apply:\n\n- **fp16 max params (B)** ≈ memory (GB) ÷ 2\n- **Q4 max params (B)** ≈ memory (GB) × 2\n\nExamples: 16GB → 8B fp16 \u002F 32B Q4 — 24GB VRAM → 12B fp16 \u002F 48B Q4 — 8GB → 4B fp16 \u002F 16B Q4\n\n---\n\n## Step 2: Find relevant benchmark datasets\n\nFetch the full list of official HF benchmarks:\n\n```bash\ncurl -s -H \"Authorization: Bearer $(cat ~\u002F.cache\u002Fhuggingface\u002Ftoken)\" \\\n  \"https:\u002F\u002Fhuggingface.co\u002Fapi\u002Fdatasets?filter=benchmark:official&limit=500\" | jq '[.[] | {id, tags, description}]'\n```\n\nRead the returned list and select the datasets most relevant to the user's task — match on dataset id, tags, and description. Use your judgment; don't limit yourself to 2-3. Aim for comprehensive coverage: if 5 benchmarks clearly cover the task, use all 5.\n\n---\n\n## Step 3: Fetch top models from leaderboards\n\nFor each selected benchmark dataset:\n\n```bash\ncurl -s -H \"Authorization: Bearer $(cat ~\u002F.cache\u002Fhuggingface\u002Ftoken)\" \\\n  \"https:\u002F\u002Fhuggingface.co\u002Fapi\u002Fdatasets\u002F\u003Cnamespace>\u002F\u003Crepo>\u002Fleaderboard\" | jq '[.[:15] | .[] | {rank, modelId, value, verified}]'\n```\n\nCollect model IDs and scores across all benchmarks. If a leaderboard returns an error (404, 401, etc.), skip it and note it in the output.\n\n---\n\n## Step 4: Enrich with model metadata\n\nFor the top 10-15 candidate model IDs, get model infos.\n\n```bash\n# REST API\ncurl -s -H \"Authorization: Bearer $(cat ~\u002F.cache\u002Fhuggingface\u002Ftoken)\" \\\n  \"https:\u002F\u002Fhuggingface.co\u002Fapi\u002Fmodels\u002Forg\u002Fmodel1\" | jq '{safetensors, tags, cardData}'\n\n# CLI (hf-cli)\nhf models info org\u002Fmodel1 --json | jq '{safetensors, tags, cardData}'\n```\n\nExtract from each response:\n- **Parameters**: `safetensors.total` → convert to B (e.g., 7_241_748_480 → \"7.2B\")\n- **License**: from model card tags (look for `license:apache-2.0`, `license:mit`, etc.)\n- If `safetensors` is absent, parse size from the model name (look for \"7b\", \"8b\", \"13b\", \"70b\", \"72b\", etc.)\n\n---\n\n## Step 5: Filter and rank\n\n**If a device was specified:**\n1. Remove models exceeding the fp16 parameter budget for the device\n2. Flag models that fit only with Q4 quantization (multiply budget by ~4 for Q4 capacity)\n3. If a highly-ranked model is slightly over budget, keep it with a \"needs Q4\" note — don't silently drop it\n\n**If no device was mentioned:** skip all size filtering — just rank by benchmark score.\n\nThen: rank by benchmark score (descending), keep top 5-8 models.\n\nInclude proprietary models (GPT-4, Claude, Gemini) if they appear on leaderboards, but flag them as \"API only \u002F not self-hostable\". If the user explicitly asked for local\u002Fopen models only, exclude them.\n\n---\n\n## Step 6: Output\n\n### Comparison table\n\n```markdown\n| # | Model | Params | [Benchmark 1] | [Benchmark 2] | License | On device |\n|---|-------|--------|--------------|--------------|---------|-----------|\n| ⭐1 | [org\u002Fname](https:\u002F\u002Fhuggingface.co\u002Forg\u002Fname) | 7B | 85.2% | — | Apache 2.0 | Yes (fp16) |\n| 2 | [org\u002Fname](https:\u002F\u002Fhuggingface.co\u002Forg\u002Fname) | 13B | 83.1% | 71.5% | MIT | Q4 only |\n| 3 | [org\u002Fname](https:\u002F\u002Fhuggingface.co\u002Forg\u002Fname) | 70B | 90.0% | 81.0% | Llama | Too large |\n```\n\n- Link model names to `https:\u002F\u002Fhuggingface.co\u002F\u003Cmodel_id>`\n- Use `—` for benchmarks where the model wasn't evaluated\n- Star the top recommended pick with ⭐\n- \"On device\" values: `Yes (fp16)`, `Q4 only`, `Too large`, `API only`\n\n### Follow-up\n\nAfter presenting the table, ask the user: \"Would you like to run **[top recommended model]**?\"\n\nIf they say yes, ask whether they'd prefer to:\n- **Run locally** — ask about their device if not already known, then give appropriate setup instructions\n- **Run on HF Jobs** — point them to the HF Jobs guide: https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fhuggingface_hub\u002Fen\u002Fguides\u002Fjobs\n\n---\n\n## Error handling\n\n- **Leaderboard not found**: skip, note \"leaderboard unavailable\" in output\n- **Model missing from hub_repo_details**: fall back to parsing size from model name\n- **No benchmarks found for task**: use the curated fallback table above, or try `hub_repo_search` with `filters=[\"\u003Ctask>\"]` sorted by `trendingScore`\n- **All leaderboards fail**: fall back to `hub_repo_search` for popular models tagged with the task, note that results are by popularity rather than benchmark score\n\n## Limitations\n\n- Use this skill only when the task clearly matches its upstream product or API scope.\n- Verify commands, API behavior, pricing, quotas, credentials, and deployment effects against current official documentation before making changes.\n- Do not treat generated examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.\n","# HuggingFace Best Model Finder\n## When to Use\n\nUse when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores. Triggers on: \"best model for X\", \"what model should I use for\", \"top models for [task]\", \"which model runs on my...\n\n\nFinds the best models for a task by querying official HF benchmark leaderboards, enriching\nresults with model size data, filtering for what fits on the user's device, and returning a\ncomparison table with benchmark scores.\n\n---\n\n## Step 1: Parse the request\n\nExtract from the user's message:\n- **Task**: what they want the model to do (coding, math\u002Freasoning, chat, OCR, RAG\u002Fretrieval, speech recognition, image classification, multimodal, agents, etc.)\n- **Device**: hardware constraints (MacBook M-series 8\u002F16\u002F32\u002F64GB unified memory, RTX GPU with VRAM amount, CPU-only, cloud\u002Fno constraint, etc.)\n\nIf device is not mentioned, skip filtering entirely and return the highest-performing models regardless of size. If the task is genuinely ambiguous, ask one clarifying question.\n\n### Device → max parameter budget\n\nWhen a device is specified, extract its available memory (unified RAM for Apple Silicon, VRAM for discrete GPUs) and apply:\n\n- **fp16 max params (B)** ≈ memory (GB) ÷ 2\n- **Q4 max params (B)** ≈ memory (GB) × 2\n\nExamples: 16GB → 8B fp16 \u002F 32B Q4 — 24GB VRAM → 12B fp16 \u002F 48B Q4 — 8GB → 4B fp16 \u002F 16B Q4\n\n---\n\n## Step 2: Find relevant benchmark datasets\n\nFetch the full list of official HF benchmarks:\n\n```bash\ncurl -s -H \"Authorization: Bearer $(cat ~\u002F.cache\u002Fhuggingface\u002Ftoken)\" \\\n  \"https:\u002F\u002Fhuggingface.co\u002Fapi\u002Fdatasets?filter=benchmark:official&limit=500\" | jq '[.[] | {id, tags, description}]'\n```\n\nRead the returned list and select the datasets most relevant to the user's task — match on dataset id, tags, and description. Use your judgment; don't limit yourself to 2-3. Aim for comprehensive coverage: if 5 benchmarks clearly cover the task, use all 5.\n\n---\n\n## Step 3: Fetch top models from leaderboards\n\nFor each selected benchmark dataset:\n\n```bash\ncurl -s -H \"Authorization: Bearer $(cat ~\u002F.cache\u002Fhuggingface\u002Ftoken)\" \\\n  \"https:\u002F\u002Fhuggingface.co\u002Fapi\u002Fdatasets\u002F\u003Cnamespace>\u002F\u003Crepo>\u002Fleaderboard\" | jq '[.[:15] | .[] | {rank, modelId, value, verified}]'\n```\n\nCollect model IDs and scores across all benchmarks. If a leaderboard returns an error (404, 401, etc.), skip it and note it in the output.\n\n---\n\n## Step 4: Enrich with model metadata\n\nFor the top 10-15 candidate model IDs, get model infos.\n\n```bash\n# REST API\ncurl -s -H \"Authorization: Bearer $(cat ~\u002F.cache\u002Fhuggingface\u002Ftoken)\" \\\n  \"https:\u002F\u002Fhuggingface.co\u002Fapi\u002Fmodels\u002Forg\u002Fmodel1\" | jq '{safetensors, tags, cardData}'\n\n# CLI (hf-cli)\nhf models info org\u002Fmodel1 --json | jq '{safetensors, tags, cardData}'\n```\n\nExtract from each response:\n- **Parameters**: `safetensors.total` → convert to B (e.g., 7_241_748_480 → \"7.2B\")\n- **License**: from model card tags (look for `license:apache-2.0`, `license:mit`, etc.)\n- If `safetensors` is absent, parse size from the model name (look for \"7b\", \"8b\", \"13b\", \"70b\", \"72b\", etc.)\n\n---\n\n## Step 5: Filter and rank\n\n**If a device was specified:**\n1. Remove models exceeding the fp16 parameter budget for the device\n2. Flag models that fit only with Q4 quantization (multiply budget by ~4 for Q4 capacity)\n3. If a highly-ranked model is slightly over budget, keep it with a \"needs Q4\" note — don't silently drop it\n\n**If no device was mentioned:** skip all size filtering — just rank by benchmark score.\n\nThen: rank by benchmark score (descending), keep top 5-8 models.\n\nInclude proprietary models (GPT-4, Claude, Gemini) if they appear on leaderboards, but flag them as \"API only \u002F not self-hostable\". If the user explicitly asked for local\u002Fopen models only, exclude them.\n\n---\n\n## Step 6: Output\n\n### Comparison table\n\n```markdown\n| # | Model | Params | [Benchmark 1] | [Benchmark 2] | License | On device |\n|---|-------|--------|--------------|--------------|---------|-----------|\n| ⭐1 | [org\u002Fname](https:\u002F\u002Fhuggingface.co\u002Forg\u002Fname) | 7B | 85.2% | — | Apache 2.0 | Yes (fp16) |\n| 2 | [org\u002Fname](https:\u002F\u002Fhuggingface.co\u002Forg\u002Fname) | 13B | 83.1% | 71.5% | MIT | Q4 only |\n| 3 | [org\u002Fname](https:\u002F\u002Fhuggingface.co\u002Forg\u002Fname) | 70B | 90.0% | 81.0% | Llama | Too large |\n```\n\n- Link model names to `https:\u002F\u002Fhuggingface.co\u002F\u003Cmodel_id>`\n- Use `—` for benchmarks where the model wasn't evaluated\n- Star the top recommended pick with ⭐\n- \"On device\" values: `Yes (fp16)`, `Q4 only`, `Too large`, `API only`\n\n### Follow-up\n\nAfter presenting the table, ask the user: \"Would you like to run **[top recommended model]**?\"\n\nIf they say yes, ask whether they'd prefer to:\n- **Run locally** — ask about their device if not already known, then give appropriate setup instructions\n- **Run on HF Jobs** — point them to the HF Jobs guide: https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fhuggingface_hub\u002Fen\u002Fguides\u002Fjobs\n\n---\n\n## Error handling\n\n- **Leaderboard not found**: skip, note \"leaderboard unavailable\" in output\n- **Model missing from hub_repo_details**: fall back to parsing size from model name\n- **No benchmarks found for task**: use the curated fallback table above, or try `hub_repo_search` with `filters=[\"\u003Ctask>\"]` sorted by `trendingScore`\n- **All leaderboards fail**: fall back to `hub_repo_search` for popular models tagged with the task, note that results are by popularity rather than benchmark score\n\n## Limitations\n\n- Use this skill only when the task clearly matches its upstream product or API scope.\n- Verify commands, API behavior, pricing, quotas, credentials, and deployment effects against current official documentation before making changes.\n- Do not treat generated examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.\n",{"name":5,"risk":27,"source":43,"license":14,"date_added":44,"description":7,"source_repo":45,"source_type":37,"license_source":46},"https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fskills\u002Ftree\u002Fmain\u002Fskills\u002Fhuggingface-best",1782864000000,"huggingface\u002Fskills","https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fskills\u002Fblob\u002Fmain\u002FLICENSE","f3312ba749561b6159f477dbfc9313c875d40a56",".agents\u002Fskills_archive\u002Fhuggingface-best","https:\u002F\u002Fgithub.com\u002Fduc-duy-1612\u002FOnline-Delivery-Systems-Web","https:\u002F\u002Fgithub.com\u002Fduc-duy-1612","2026-10-25 23:50:58",{"source":8,"skillName":5,"commands":53,"prompt":63,"zipAvailable":64},[54,57,60],{"tool":55,"command":56},"npx","npx skills add duc-duy-1612\u002FOnline-Delivery-Systems-Web --skill huggingface-best",{"tool":58,"command":59},"bunx","bunx skills add duc-duy-1612\u002FOnline-Delivery-Systems-Web --skill huggingface-best",{"tool":61,"command":62},"pnpm","pnpm dlx skills add duc-duy-1612\u002FOnline-Delivery-Systems-Web --skill huggingface-best","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fduc-duy-1612\u002FOnline-Delivery-Systems-Web\u002Fblob\u002Fmain\u002F.agents\u002Fskills_archive\u002Fhuggingface-best\u002FSKILL.md",true,[],[67,97,120,142,170,193],{"skillNo":68,"name":69,"nameZh":6,"description":70,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":71,"licenseFileUrl":6,"sourceHtmlUrl":72,"categories":73,"keywords":79,"publishedAt":38},"01M6Q0FA5FZ410Z33Z3TFQ4DBA","zero-trust","Implement zero-trust network architecture. Configure identity-based access, micro-segmentation, and continuous verification. Use when implementing modern security architectures.","MIT","https:\u002F\u002Fgithub.com\u002Fduc-duy-1612\u002FOnline-Delivery-Systems-Web\u002Fblob\u002Fmain\u002F.agents\u002Fskills_archive\u002Fzero-trust\u002FSKILL.md",[74],{"categoryNo":75,"categoryName":76,"taxonomyType":77,"taxonomyTypeDesc":78},"01JBSEEDCAT000000000000011","运维与部署","SCENARIO","使用场景",[80,81,82,83,84,85,69,86,87,88,89,90,91,20,92,27,93,94,95,96],"ZeRO","trust","zero trust","architecture","assessment","verification","segmentation","devops-security","access","configure","only","community","active","scope","scripts","relevant","implementing",{"skillNo":98,"name":99,"nameZh":6,"description":100,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":71,"licenseFileUrl":6,"sourceHtmlUrl":101,"categories":102,"keywords":103,"publishedAt":38},"01M6Q0F1YZJW3KX5FBKEVZ8CV4","zcode-delegate","Delegate coding tasks to the Z.AI ZCode CLI only when the user explicitly requests it, while the orchestrator retains review and landing responsibility.","https:\u002F\u002Fgithub.com\u002Fduc-duy-1612\u002FOnline-Delivery-Systems-Web\u002Fblob\u002Fmain\u002F.agents\u002Fskills_archive\u002Fzcode-delegate\u002FSKILL.md",[],[104,105,106,107,108,109,110,111,112,33,113,114,115,90,116,91,117,118,27,119],"delegate","zcode","zcode delegate","review","cli","orchestration","tasks","run","Bash","node","read","orchestrator","files","desktop app","app","agent-orchestration",{"skillNo":121,"name":122,"nameZh":6,"description":123,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":71,"licenseFileUrl":6,"sourceHtmlUrl":124,"categories":125,"keywords":126,"publishedAt":38},"01M6Q0ETK41VGNMYEXW0T00AJN","youtube-transcript","Fetch YouTube transcripts through DeepAPI or local fallback tooling and save clean text output.","https:\u002F\u002Fgithub.com\u002Fduc-duy-1612\u002FOnline-Delivery-Systems-Web\u002Fblob\u002Fmain\u002F.agents\u002Fskills_archive\u002Fyoutube-transcript\u002FSKILL.md",[],[127,128,129,130,29,131,132,35,34,133,134,135,136,91,20,137,138,139,140,141],"transcript","youtube transcript","youtube","safe","codex","research","text","fetch","tooling","clean","local","output","deepapi","youtube transcripts","blob",{"skillNo":143,"name":144,"nameZh":6,"description":145,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":71,"licenseFileUrl":6,"sourceHtmlUrl":146,"categories":147,"keywords":159,"publishedAt":38},"01M6Q0EPPH2PVMDW40017F8WX0","youtube-transcript-skills","Fetch YouTube video transcripts, search videos\u002Fchannels, browse channels, and extract playlists via the getyoutubetranscript.com API - free tier, no card required.","https:\u002F\u002Fgithub.com\u002Fduc-duy-1612\u002FOnline-Delivery-Systems-Web\u002Fblob\u002Fmain\u002F.agents\u002Fskills_archive\u002Fyoutube-transcript-skills\u002FSKILL.md",[148,151,154],{"categoryNo":149,"categoryName":150,"taxonomyType":77,"taxonomyTypeDesc":78},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":152,"categoryName":153,"taxonomyType":77,"taxonomyTypeDesc":78},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":155,"categoryName":156,"taxonomyType":157,"taxonomyTypeDesc":158},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[127,128,129,160,130,161,29,131,35,162,163,164,165,134,166,91,20,167,168,169],"youtube transcript skills","gemini","search","api","Video","browse","api-integration","free","card","channels",{"skillNo":171,"name":172,"nameZh":6,"description":173,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":71,"licenseFileUrl":6,"sourceHtmlUrl":174,"categories":175,"keywords":180,"publishedAt":38},"01M6Q0EAB96PX83W5PTE8A0R8D","youtube-notetaker","Turn YouTube talks into local study notes with slides, transcripts, editable annotations, and a markdown-backed viewer.","https:\u002F\u002Fgithub.com\u002Fduc-duy-1612\u002FOnline-Delivery-Systems-Web\u002Fblob\u002Fmain\u002F.agents\u002Fskills_archive\u002Fyoutube-notetaker\u002FSKILL.md",[176,179],{"categoryNo":177,"categoryName":178,"taxonomyType":77,"taxonomyTypeDesc":78},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":152,"categoryName":153,"taxonomyType":77,"taxonomyTypeDesc":78},[129,181,182,108,130,183,37,184,185,35,164,186,187,188,189,20,190,137,191,192],"youtube notetaker","notetaker","workflow","notes","code","markdown","slides","annotations","README","study","dair-academy","viewer",{"skillNo":194,"name":195,"nameZh":6,"description":196,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":71,"licenseFileUrl":6,"sourceHtmlUrl":197,"categories":198,"keywords":200,"publishedAt":38},"01M6Q0E55F0GW5MX48V8HXS8B5","youtube-full","Fetch YouTube transcripts, search videos, browse channels, and extract playlists via TranscriptAPI — no yt-dlp, no Google API key, works from any cloud server.","https:\u002F\u002Fgithub.com\u002Fduc-duy-1612\u002FOnline-Delivery-Systems-Web\u002Fblob\u002Fmain\u002F.agents\u002Fskills_archive\u002Fyoutube-full\u002FSKILL.md",[199],{"categoryNo":149,"categoryName":150,"taxonomyType":77,"taxonomyTypeDesc":78},[201,129,202,203,130,161,162,204,205,163,164,165,134,206,166,91,20,167,207,140],"youtube full","full","google","server","dlp","credits","automatic"]