[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M718XCJ2WAW9RM78512MESCK":3,"skill-same:01M718XCJ2WAW9RM78512MESCK":88},{"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":18,"publishedAt":37,"linkOnly":38,"markdownContent":39,"markdownBody":40,"frontMatter":41,"refName":24,"commitSha":43,"skillDirectory":44,"forksCount":14,"repositoryHtmlUrl":45,"repositoryHomepageUrl":6,"ownerHtmlUrl":46,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":47,"install":48,"resources":61},"01M718XCJ2WAW9RM78512MESCK","hugging-face-community-evals",null,"Run local evaluations for Hugging Face Hub models with inspect-ai or lighteval.","sapsapshen\u002Fai-memory-router","sapsapshen","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F20347959?v=4","01M6XDQEF1DP50DKF37H81J3J5","shenmian","01M6XDQEJPE41Y3DT2763X8ZJN",0,"MIT","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fglobal\u002Fhugging-face-community-evals\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36],"face","hugging face","evals","community","hugging face community evals","main","unknown","for","inspect","tree","with","local","github","hub","models","HuggingFace","https","run","2026-11-10 10:12:30",false,"---\nsource: \"https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fskills\u002Ftree\u002Fmain\u002Fskills\u002Fhuggingface-community-evals\"\nname: hugging-face-community-evals\ndescription: Run local evaluations for Hugging Face Hub models with inspect-ai or lighteval.\nrisk: unknown\n---\n\n# Overview\n\n## When to Use\n\nUse this skill for local model evaluation, backend selection, and GPU smoke tests outside the Hugging Face Jobs workflow.\n\nThis skill is for **running evaluations against models on the Hugging Face Hub on local hardware**.\n\nIt covers:\n- `inspect-ai` with local inference\n- `lighteval` with local inference\n- choosing between `vllm`, Hugging Face Transformers, and `accelerate`\n- smoke tests, task selection, and backend fallback strategy\n\nIt does **not** cover:\n- Hugging Face Jobs orchestration\n- model-card or `model-index` edits\n- README table extraction\n- Artificial Analysis imports\n- `.eval_results` generation or publishing\n- PR creation or community-evals automation\n\nIf the user wants to **run the same eval remotely on Hugging Face Jobs**, hand off to the `hugging-face-jobs` skill and pass it one of the local scripts in this skill.\n\nIf the user wants to **publish results into the community evals workflow**, stop after generating the evaluation run and hand off that publishing step to `~\u002Fcode\u002Fcommunity-evals`.\n\n> All paths below are relative to the directory containing this `SKILL.md`.\n\n# When To Use Which Script\n\n| Use case | Script |\n|---|---|\n| Local `inspect-ai` eval on a Hub model via inference providers | `scripts\u002Finspect_eval_uv.py` |\n| Local GPU eval with `inspect-ai` using `vllm` or Transformers | `scripts\u002Finspect_vllm_uv.py` |\n| Local GPU eval with `lighteval` using `vllm` or `accelerate` | `scripts\u002Flighteval_vllm_uv.py` |\n| Extra command patterns | `examples\u002FUSAGE_EXAMPLES.md` |\n\n# Prerequisites\n\n- Prefer `uv run` for local execution.\n- Set `HF_TOKEN` for gated\u002Fprivate models.\n- For local GPU runs, verify GPU access before starting:\n\n```bash\nuv --version\nprintenv HF_TOKEN >\u002Fdev\u002Fnull\nnvidia-smi\n```\n\nIf `nvidia-smi` is unavailable, either:\n- use `scripts\u002Finspect_eval_uv.py` for lighter provider-backed evaluation, or\n- hand off to the `hugging-face-jobs` skill if the user wants remote compute.\n\n# Core Workflow\n\n1. Choose the evaluation framework.\n   - Use `inspect-ai` when you want explicit task control and inspect-native flows.\n   - Use `lighteval` when the benchmark is naturally expressed as a lighteval task string, especially leaderboard-style tasks.\n2. Choose the inference backend.\n   - Prefer `vllm` for throughput on supported architectures.\n   - Use Hugging Face Transformers (`--backend hf`) or `accelerate` as compatibility fallbacks.\n3. Start with a smoke test.\n   - `inspect-ai`: add `--limit 10` or similar.\n   - `lighteval`: add `--max-samples 10`.\n4. Scale up only after the smoke test passes.\n5. If the user wants remote execution, hand off to `hugging-face-jobs` with the same script + args.\n\n# Quick Start\n\n## Option A: inspect-ai with local inference providers path\n\nBest when the model is already supported by Hugging Face Inference Providers and you want the lowest local setup overhead.\n\n```bash\nuv run scripts\u002Finspect_eval_uv.py \\\n  --model meta-llama\u002FLlama-3.2-1B \\\n  --task mmlu \\\n  --limit 20\n```\n\nUse this path when:\n- you want a quick local smoke test\n- you do not need direct GPU control\n- the task already exists in `inspect-evals`\n\n## Option B: inspect-ai on Local GPU\n\nBest when you need to load the Hub model directly, use `vllm`, or fall back to Transformers for unsupported architectures.\n\nLocal GPU:\n\n```bash\nuv run scripts\u002Finspect_vllm_uv.py \\\n  --model meta-llama\u002FLlama-3.2-1B \\\n  --task gsm8k \\\n  --limit 20\n```\n\nTransformers fallback:\n\n```bash\nuv run scripts\u002Finspect_vllm_uv.py \\\n  --model microsoft\u002Fphi-2 \\\n  --task mmlu \\\n  --backend hf \\\n  --trust-remote-code \\\n  --limit 20\n```\n\n## Option C: lighteval on Local GPU\n\nBest when the task is naturally expressed as a `lighteval` task string, especially Open LLM Leaderboard style benchmarks.\n\nLocal GPU:\n\n```bash\nuv run scripts\u002Flighteval_vllm_uv.py \\\n  --model meta-llama\u002FLlama-3.2-3B-Instruct \\\n  --tasks \"leaderboard|mmlu|5,leaderboard|gsm8k|5\" \\\n  --max-samples 20 \\\n  --use-chat-template\n```\n\n`accelerate` fallback:\n\n```bash\nuv run scripts\u002Flighteval_vllm_uv.py \\\n  --model microsoft\u002Fphi-2 \\\n  --tasks \"leaderboard|mmlu|5\" \\\n  --backend accelerate \\\n  --trust-remote-code \\\n  --max-samples 20\n```\n\n# Remote Execution Boundary\n\nThis skill intentionally stops at **local execution and backend selection**.\n\nIf the user wants to:\n- run these scripts on Hugging Face Jobs\n- pick remote hardware\n- pass secrets to remote jobs\n- schedule recurring runs\n- inspect \u002F cancel \u002F monitor jobs\n\nthen switch to the **`hugging-face-jobs`** skill and pass it one of these scripts plus the chosen arguments.\n\n# Task Selection\n\n`inspect-ai` examples:\n- `mmlu`\n- `gsm8k`\n- `hellaswag`\n- `arc_challenge`\n- `truthfulqa`\n- `winogrande`\n- `humaneval`\n\n`lighteval` task strings use `suite|task|num_fewshot`:\n- `leaderboard|mmlu|5`\n- `leaderboard|gsm8k|5`\n- `leaderboard|arc_challenge|25`\n- `lighteval|hellaswag|0`\n\nMultiple `lighteval` tasks can be comma-separated in `--tasks`.\n\n# Backend Selection\n\n- Prefer `inspect_vllm_uv.py --backend vllm` for fast GPU inference on supported architectures.\n- Use `inspect_vllm_uv.py --backend hf` when `vllm` does not support the model.\n- Prefer `lighteval_vllm_uv.py --backend vllm` for throughput on supported models.\n- Use `lighteval_vllm_uv.py --backend accelerate` as the compatibility fallback.\n- Use `inspect_eval_uv.py` when Inference Providers already cover the model and you do not need direct GPU control.\n\n# Hardware Guidance\n\n| Model size | Suggested local hardware |\n|---|---|\n| `\u003C 3B` | consumer GPU \u002F Apple Silicon \u002F small dev GPU |\n| `3B - 13B` | stronger local GPU |\n| `13B+` | high-memory local GPU or hand off to `hugging-face-jobs` |\n\nFor smoke tests, prefer cheaper local runs plus `--limit` or `--max-samples`.\n\n# Troubleshooting\n\n- CUDA or vLLM OOM:\n  - reduce `--batch-size`\n  - reduce `--gpu-memory-utilization`\n  - switch to a smaller model for the smoke test\n  - if necessary, hand off to `hugging-face-jobs`\n- Model unsupported by `vllm`:\n  - switch to `--backend hf` for `inspect-ai`\n  - switch to `--backend accelerate` for `lighteval`\n- Gated\u002Fprivate repo access fails:\n  - verify `HF_TOKEN`\n- Custom model code required:\n  - add `--trust-remote-code`\n\n# Examples\n\nSee:\n- `examples\u002FUSAGE_EXAMPLES.md` for local command patterns\n- `scripts\u002Finspect_eval_uv.py`\n- `scripts\u002Finspect_vllm_uv.py`\n- `scripts\u002Flighteval_vllm_uv.py`\n","# Overview\n\n## When to Use\n\nUse this skill for local model evaluation, backend selection, and GPU smoke tests outside the Hugging Face Jobs workflow.\n\nThis skill is for **running evaluations against models on the Hugging Face Hub on local hardware**.\n\nIt covers:\n- `inspect-ai` with local inference\n- `lighteval` with local inference\n- choosing between `vllm`, Hugging Face Transformers, and `accelerate`\n- smoke tests, task selection, and backend fallback strategy\n\nIt does **not** cover:\n- Hugging Face Jobs orchestration\n- model-card or `model-index` edits\n- README table extraction\n- Artificial Analysis imports\n- `.eval_results` generation or publishing\n- PR creation or community-evals automation\n\nIf the user wants to **run the same eval remotely on Hugging Face Jobs**, hand off to the `hugging-face-jobs` skill and pass it one of the local scripts in this skill.\n\nIf the user wants to **publish results into the community evals workflow**, stop after generating the evaluation run and hand off that publishing step to `~\u002Fcode\u002Fcommunity-evals`.\n\n> All paths below are relative to the directory containing this `SKILL.md`.\n\n# When To Use Which Script\n\n| Use case | Script |\n|---|---|\n| Local `inspect-ai` eval on a Hub model via inference providers | `scripts\u002Finspect_eval_uv.py` |\n| Local GPU eval with `inspect-ai` using `vllm` or Transformers | `scripts\u002Finspect_vllm_uv.py` |\n| Local GPU eval with `lighteval` using `vllm` or `accelerate` | `scripts\u002Flighteval_vllm_uv.py` |\n| Extra command patterns | `examples\u002FUSAGE_EXAMPLES.md` |\n\n# Prerequisites\n\n- Prefer `uv run` for local execution.\n- Set `HF_TOKEN` for gated\u002Fprivate models.\n- For local GPU runs, verify GPU access before starting:\n\n```bash\nuv --version\nprintenv HF_TOKEN >\u002Fdev\u002Fnull\nnvidia-smi\n```\n\nIf `nvidia-smi` is unavailable, either:\n- use `scripts\u002Finspect_eval_uv.py` for lighter provider-backed evaluation, or\n- hand off to the `hugging-face-jobs` skill if the user wants remote compute.\n\n# Core Workflow\n\n1. Choose the evaluation framework.\n   - Use `inspect-ai` when you want explicit task control and inspect-native flows.\n   - Use `lighteval` when the benchmark is naturally expressed as a lighteval task string, especially leaderboard-style tasks.\n2. Choose the inference backend.\n   - Prefer `vllm` for throughput on supported architectures.\n   - Use Hugging Face Transformers (`--backend hf`) or `accelerate` as compatibility fallbacks.\n3. Start with a smoke test.\n   - `inspect-ai`: add `--limit 10` or similar.\n   - `lighteval`: add `--max-samples 10`.\n4. Scale up only after the smoke test passes.\n5. If the user wants remote execution, hand off to `hugging-face-jobs` with the same script + args.\n\n# Quick Start\n\n## Option A: inspect-ai with local inference providers path\n\nBest when the model is already supported by Hugging Face Inference Providers and you want the lowest local setup overhead.\n\n```bash\nuv run scripts\u002Finspect_eval_uv.py \\\n  --model meta-llama\u002FLlama-3.2-1B \\\n  --task mmlu \\\n  --limit 20\n```\n\nUse this path when:\n- you want a quick local smoke test\n- you do not need direct GPU control\n- the task already exists in `inspect-evals`\n\n## Option B: inspect-ai on Local GPU\n\nBest when you need to load the Hub model directly, use `vllm`, or fall back to Transformers for unsupported architectures.\n\nLocal GPU:\n\n```bash\nuv run scripts\u002Finspect_vllm_uv.py \\\n  --model meta-llama\u002FLlama-3.2-1B \\\n  --task gsm8k \\\n  --limit 20\n```\n\nTransformers fallback:\n\n```bash\nuv run scripts\u002Finspect_vllm_uv.py \\\n  --model microsoft\u002Fphi-2 \\\n  --task mmlu \\\n  --backend hf \\\n  --trust-remote-code \\\n  --limit 20\n```\n\n## Option C: lighteval on Local GPU\n\nBest when the task is naturally expressed as a `lighteval` task string, especially Open LLM Leaderboard style benchmarks.\n\nLocal GPU:\n\n```bash\nuv run scripts\u002Flighteval_vllm_uv.py \\\n  --model meta-llama\u002FLlama-3.2-3B-Instruct \\\n  --tasks \"leaderboard|mmlu|5,leaderboard|gsm8k|5\" \\\n  --max-samples 20 \\\n  --use-chat-template\n```\n\n`accelerate` fallback:\n\n```bash\nuv run scripts\u002Flighteval_vllm_uv.py \\\n  --model microsoft\u002Fphi-2 \\\n  --tasks \"leaderboard|mmlu|5\" \\\n  --backend accelerate \\\n  --trust-remote-code \\\n  --max-samples 20\n```\n\n# Remote Execution Boundary\n\nThis skill intentionally stops at **local execution and backend selection**.\n\nIf the user wants to:\n- run these scripts on Hugging Face Jobs\n- pick remote hardware\n- pass secrets to remote jobs\n- schedule recurring runs\n- inspect \u002F cancel \u002F monitor jobs\n\nthen switch to the **`hugging-face-jobs`** skill and pass it one of these scripts plus the chosen arguments.\n\n# Task Selection\n\n`inspect-ai` examples:\n- `mmlu`\n- `gsm8k`\n- `hellaswag`\n- `arc_challenge`\n- `truthfulqa`\n- `winogrande`\n- `humaneval`\n\n`lighteval` task strings use `suite|task|num_fewshot`:\n- `leaderboard|mmlu|5`\n- `leaderboard|gsm8k|5`\n- `leaderboard|arc_challenge|25`\n- `lighteval|hellaswag|0`\n\nMultiple `lighteval` tasks can be comma-separated in `--tasks`.\n\n# Backend Selection\n\n- Prefer `inspect_vllm_uv.py --backend vllm` for fast GPU inference on supported architectures.\n- Use `inspect_vllm_uv.py --backend hf` when `vllm` does not support the model.\n- Prefer `lighteval_vllm_uv.py --backend vllm` for throughput on supported models.\n- Use `lighteval_vllm_uv.py --backend accelerate` as the compatibility fallback.\n- Use `inspect_eval_uv.py` when Inference Providers already cover the model and you do not need direct GPU control.\n\n# Hardware Guidance\n\n| Model size | Suggested local hardware |\n|---|---|\n| `\u003C 3B` | consumer GPU \u002F Apple Silicon \u002F small dev GPU |\n| `3B - 13B` | stronger local GPU |\n| `13B+` | high-memory local GPU or hand off to `hugging-face-jobs` |\n\nFor smoke tests, prefer cheaper local runs plus `--limit` or `--max-samples`.\n\n# Troubleshooting\n\n- CUDA or vLLM OOM:\n  - reduce `--batch-size`\n  - reduce `--gpu-memory-utilization`\n  - switch to a smaller model for the smoke test\n  - if necessary, hand off to `hugging-face-jobs`\n- Model unsupported by `vllm`:\n  - switch to `--backend hf` for `inspect-ai`\n  - switch to `--backend accelerate` for `lighteval`\n- Gated\u002Fprivate repo access fails:\n  - verify `HF_TOKEN`\n- Custom model code required:\n  - add `--trust-remote-code`\n\n# Examples\n\nSee:\n- `examples\u002FUSAGE_EXAMPLES.md` for local command patterns\n- `scripts\u002Finspect_eval_uv.py`\n- `scripts\u002Finspect_vllm_uv.py`\n- `scripts\u002Flighteval_vllm_uv.py`\n",{"name":5,"risk":25,"source":42,"description":7},"https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fskills\u002Ftree\u002Fmain\u002Fskills\u002Fhuggingface-community-evals","7a38d8e7073e61c16eda85681cb32fb68a8657a5","skills-store\u002Fglobal\u002Fhugging-face-community-evals","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router","https:\u002F\u002Fgithub.com\u002Fsapsapshen","2026-04-14 21:12:12",{"source":8,"skillName":5,"commands":49,"prompt":59,"zipAvailable":60},[50,53,56],{"tool":51,"command":52},"npx","npx skills add sapsapshen\u002Fai-memory-router --skill hugging-face-community-evals",{"tool":54,"command":55},"bunx","bunx skills add sapsapshen\u002Fai-memory-router --skill hugging-face-community-evals",{"tool":57,"command":58},"pnpm","pnpm dlx skills add sapsapshen\u002Fai-memory-router --skill hugging-face-community-evals","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fglobal\u002Fhugging-face-community-evals\u002FSKILL.md",true,[62,68,74,80,84],{"path":63,"resourceType":64,"resourceTypeDesc":65,"sizeBytes":66,"sourceHtmlUrl":67},"examples\u002F.env.example","OTHER","其他",159,"https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fglobal\u002Fhugging-face-community-evals\u002Fexamples\u002F.env.example",{"path":69,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":72,"sourceHtmlUrl":73},"examples\u002FUSAGE_EXAMPLES.md","REFERENCE","参考文档",2062,"https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fglobal\u002Fhugging-face-community-evals\u002Fexamples\u002FUSAGE_EXAMPLES.md",{"path":75,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":78,"sourceHtmlUrl":79},"scripts\u002Finspect_eval_uv.py","SCRIPT","脚本",3004,"https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fglobal\u002Fhugging-face-community-evals\u002Fscripts\u002Finspect_eval_uv.py",{"path":81,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":82,"sourceHtmlUrl":83},"scripts\u002Finspect_vllm_uv.py",9119,"https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fglobal\u002Fhugging-face-community-evals\u002Fscripts\u002Finspect_vllm_uv.py",{"path":85,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":86,"sourceHtmlUrl":87},"scripts\u002Flighteval_vllm_uv.py",9204,"https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fglobal\u002Fhugging-face-community-evals\u002Fscripts\u002Flighteval_vllm_uv.py",[89,112,138,148,161,179],{"skillNo":90,"name":91,"nameZh":6,"description":92,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":93,"categories":94,"keywords":100,"publishedAt":37},"01M71ATQRKG48SH06FQ697T5G9","wechat-search","Search WeChat Official Account articles using OpenClaw's web search and fetch capabilities with compliance-focused design.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fwechat-search-release\u002FSKILL.md",[95],{"categoryNo":96,"categoryName":97,"taxonomyType":98,"taxonomyTypeDesc":99},"01JBSEEDCAT000000000000037","律师与法务","PROFESSION","适用职业",[101,102,103,104,105,106,107,108,109,110,111,29],"wechat search","wechat","search","capabilities","openclaw","design","web search","compliance","web","official","fetch",{"skillNo":113,"name":114,"nameZh":6,"description":115,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":116,"categories":117,"keywords":118,"publishedAt":37},"01M71ATNAVGZB35PFYM2GC5N00","trend-tap","Real-time trending topics aggregator across 7 platforms (X\u002FTwitter, Reddit, Google Trends, Hacker News, Zhihu, Bilibili, Weibo). 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Trigger: when user says 'trend', 'trends', 'trend pulse', '热点', '热搜', or asks what is trending\u002Fhot. Do NOT use web_search — this skill already fetches live data from all platforms with zero API keys.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Ftrend-radar\u002FSKILL.md",[],[145,146,119,130,131,132,133,123,103,135,147,136,137,129,127,126,125,134,124,122],"trend radar","radar","pulse",{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":152,"categories":153,"keywords":154,"publishedAt":37},"01M71ATGF095N3EJM9G8SD0BB5","tech-news-daily","每日科技新闻汇——从全球多个 RSS 源抓取最新科技新闻，翻译（英→中）、去重、排版成每日简报。当用户询问科技新闻、科技新鲜事、每日科技、技术动态、Daily Tech News 或类似表达时触发此技能。","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Ftech-news-daily\u002FSKILL.md",[],[155,156,157,158,159,160],"news","tech","tech news daily","daily","rss","翻译",{"skillNo":162,"name":163,"nameZh":6,"description":164,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":165,"categories":166,"keywords":172,"publishedAt":37},"01M71ATFAEAMQ17M70NQNY7SR8","summarize","Summarize URLs or files with the summarize CLI (web, PDFs, images, audio, YouTube).","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fsummarize\u002FSKILL.md",[167],{"categoryNo":168,"categoryName":169,"taxonomyType":170,"taxonomyTypeDesc":171},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",[163,109,173,174,175,29,130,176,35,177,121,178,134],"Audio","cli","youtube","images","files","pdfs",{"skillNo":180,"name":181,"nameZh":6,"description":182,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":183,"categories":184,"keywords":185,"publishedAt":37},"01M71ATDNS3R0M4H936PACQAWZ","sonoscli","Control Sonos speakers (discover\u002Fstatus\u002Fplay\u002Fvolume\u002Fgroup).","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fsonoscli\u002FSKILL.md",[],[181,186,187,188,134,189,35,31,190,191,192,193],"status","play","go","latest","discover","control","cmd","volume"]