[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M79DAM29AHY17V89CDYNTX8B":3,"skill-same:01M79DAM29AHY17V89CDYNTX8B":75},{"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":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":24,"commitSha":25,"skillDirectory":26,"forksCount":14,"repositoryHtmlUrl":27,"repositoryHomepageUrl":6,"ownerHtmlUrl":28,"ownerBlogUrl":6,"ownerDescription":29,"updatedAt":30,"install":31,"resources":44},"01M79DAM29AHY17V89CDYNTX8B","huggingface-community-evals",null,"Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM \u002F Transformers \u002F accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.","zfifteen\u002Fskills-catalog","zfifteen","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F221906715?v=4","01M79CQFXDZM5J5CJTCECPFA66","Dionisio Alberto Lopez III","01M79CQFY4C62PN8A2T5KDAHGA",1,"MIT","https:\u002F\u002Fgithub.com\u002Fzfifteen\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fcommunity-evals\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: huggingface-community-evals\ndescription: Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM \u002F Transformers \u002F accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.\n---\n\n# Overview\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\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,"description":7},"main","d9535210da9af28aa76bca80de80355197e1b6d6","skills\u002Fcommunity-evals","https:\u002F\u002Fgithub.com\u002Fzfifteen\u002Fskills-catalog","https:\u002F\u002Fgithub.com\u002Fzfifteen","I like to measure things.","2026-07-19 23:41:54",{"source":8,"skillName":5,"commands":32,"prompt":42,"zipAvailable":43},[33,36,39],{"tool":34,"command":35},"npx","npx skills add zfifteen\u002Fskills-catalog --skill huggingface-community-evals",{"tool":37,"command":38},"bunx","bunx skills add zfifteen\u002Fskills-catalog --skill huggingface-community-evals",{"tool":40,"command":41},"pnpm","pnpm dlx skills add zfifteen\u002Fskills-catalog --skill huggingface-community-evals","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fzfifteen\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fcommunity-evals\u002FSKILL.md",true,[45,51,57,61,67,71],{"path":46,"resourceType":47,"resourceTypeDesc":48,"sizeBytes":49,"sourceHtmlUrl":50},"agents\u002Fopenai.yaml","REFERENCE","参考文档",118,"https:\u002F\u002Fgithub.com\u002Fzfifteen\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fcommunity-evals\u002Fagents\u002Fopenai.yaml",{"path":52,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":55,"sourceHtmlUrl":56},"examples\u002F.env.example","OTHER","其他",159,"https:\u002F\u002Fgithub.com\u002Fzfifteen\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fcommunity-evals\u002Fexamples\u002F.env.example",{"path":58,"resourceType":47,"resourceTypeDesc":48,"sizeBytes":59,"sourceHtmlUrl":60},"examples\u002FUSAGE_EXAMPLES.md",2054,"https:\u002F\u002Fgithub.com\u002Fzfifteen\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fcommunity-evals\u002Fexamples\u002FUSAGE_EXAMPLES.md",{"path":62,"resourceType":63,"resourceTypeDesc":64,"sizeBytes":65,"sourceHtmlUrl":66},"scripts\u002Finspect_eval_uv.py","SCRIPT","脚本",3004,"https:\u002F\u002Fgithub.com\u002Fzfifteen\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fcommunity-evals\u002Fscripts\u002Finspect_eval_uv.py",{"path":68,"resourceType":63,"resourceTypeDesc":64,"sizeBytes":69,"sourceHtmlUrl":70},"scripts\u002Finspect_vllm_uv.py",9119,"https:\u002F\u002Fgithub.com\u002Fzfifteen\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fcommunity-evals\u002Fscripts\u002Finspect_vllm_uv.py",{"path":72,"resourceType":63,"resourceTypeDesc":64,"sizeBytes":73,"sourceHtmlUrl":74},"scripts\u002Flighteval_vllm_uv.py",9204,"https:\u002F\u002Fgithub.com\u002Fzfifteen\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fcommunity-evals\u002Fscripts\u002Flighteval_vllm_uv.py",[76,83,90,97,106,113],{"skillNo":77,"name":78,"nameZh":6,"description":79,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":80,"categories":81,"keywords":82,"publishedAt":19},"01M79GXQ2PWPT2E4CJYHXVJ0YE","Zotero","Use Zotero Desktop from Codex to enable\u002Fprobe the local API, search a local Zotero library, list items\u002Fcollections\u002Ftags, export BibTeX, insert citation keys into LaTeX or Markdown drafts, read indexed full text when requested, and import BibTeX\u002FRIS records into Zotero through the connector server. Use when the user mentions Zotero, citations, references.bib, BibTeX export, local Zotero API, localhost:23119, or adding citations from a Zotero library.","https:\u002F\u002Fgithub.com\u002Fzfifteen\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fzotero\u002FSKILL.md",[],[],{"skillNo":84,"name":85,"nameZh":6,"description":86,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":87,"categories":88,"keywords":89,"publishedAt":19},"01M79GXN85NETKBE06865CWK6Z","zoom-apps-sdk","Use when using Apps SDK.","https:\u002F\u002Fgithub.com\u002Fzfifteen\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fzoom-apps-sdk\u002FSKILL.md",[],[],{"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":96,"publishedAt":19},"01M79GXCYD7FGYS932J9N9RFFY","youtube","Obtain accurate, timestamped YouTube video transcripts (preferring manual captions over auto-generated) as the primary deliverable, then derive summaries, key claims, or notes from that transcript.  Use when the user provides a YouTube URL (or video ID) and wants the transcript, a faithful summary, key claims with attribution, timestamped notes, or \"what does this video actually say\".  The skill is transcript-first: never substitute metadata, title, description, or search snippets for the spoken content.  Triggers: \"youtube transcript\", \"get the transcript of this video\", \"summarize this youtube video\", \"what did they say in https:\u002F\u002Fyoutube.com\u002F...\", \"key claims from this talk\".","https:\u002F\u002Fgithub.com\u002Fzfifteen\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fyoutube\u002FSKILL.md",[],[],{"skillNo":98,"name":99,"nameZh":6,"description":100,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":101,"licenseFileUrl":102,"sourceHtmlUrl":103,"categories":104,"keywords":105,"publishedAt":19},"01M79GX8EXHHYM566BKR7MS778","yeet","Publish local changes to GitHub by confirming scope, committing intentionally, pushing the branch, and opening a draft PR through the GitHub app from this plugin, with `gh` used only as a fallback where connector coverage is insufficient.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fzfifteen\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fyeet\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fzfifteen\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fyeet\u002FSKILL.md",[],[],{"skillNo":107,"name":108,"nameZh":6,"description":109,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":110,"categories":111,"keywords":112,"publishedAt":19},"01M79GX2HFJPX84NMRE5VBTYX9","x-post-creator","Create X-ready post threads and co-located story packages from the active conversation or referenced project material. Use when the user says turn this into an X-post, \u002Fx-post, make this into an X thread, or wants plain-language social posts with supporting images saved to docs\u002Fx-posts\u002F[story-name]\u002F.","https:\u002F\u002Fgithub.com\u002Fzfifteen\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fx-post-creator\u002FSKILL.md",[],[],{"skillNo":114,"name":115,"nameZh":6,"description":116,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":117,"categories":118,"keywords":119,"publishedAt":19},"01M79GWXQT4HBT6QME6QSBZJFA","x-api","Search, read, and write X posts through the local X API MCP server (the same backend used by the Codex x-api skill). Use for searching X\u002Fx.com, reading threads and replies, creating lists, staging\u002Fpublishing posts for @alltheputs, finding high-visibility reply targets for research outreach, ingesting public context, or any request that mentions \u002Fx-api or \"search X\".","https:\u002F\u002Fgithub.com\u002Fzfifteen\u002Fskills-catalog\u002Fblob\u002Fmain\u002Fskills\u002Fx-api\u002FSKILL.md",[],[]]