[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-huggingface-huggingface-papers":3,"mdc--4if6y2-key":38,"related-org-huggingface-huggingface-papers":1921,"related-repo-huggingface-huggingface-papers":2092},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":27,"repoUrl":28,"updatedAt":29,"license":30,"forks":31,"topics":32,"repo":33,"sourceUrl":36,"mdContent":37},"huggingface-papers","research and read Hugging Face papers","Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models\u002Fdatasets\u002Fspaces, Github repo and project page. Use when the user shares a Hugging Face paper page URL, an arXiv URL or ID, or asks to summarize, explain, or analyze an AI research paper.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"huggingface","Hugging Face","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fhuggingface.png",[12,16,18,21,24],{"name":13,"slug":14,"type":15},"Research","research","tag",{"name":9,"slug":17,"type":15},"hugging-face",{"name":19,"slug":20,"type":15},"LLM","llm",{"name":22,"slug":23,"type":15},"Deep Learning","deep-learning",{"name":25,"slug":26,"type":15},"Metadata","metadata",10861,"https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fskills","2026-04-06T18:25:36.502396",null,721,[],{"repoUrl":28,"stars":27,"forks":31,"topics":34,"description":35},[],"Give your agents the power of the Hugging Face ecosystem","https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fskills\u002Ftree\u002FHEAD\u002Fskills\u002Fhuggingface-papers","---\nname: huggingface-papers\ndescription: Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models\u002Fdatasets\u002Fspaces, Github repo and project page. Use when the user shares a Hugging Face paper page URL, an arXiv URL or ID, or asks to summarize, explain, or analyze an AI research paper.\n---\n\n# Hugging Face Paper Pages\n\nHugging Face Paper pages (hf.co\u002Fpapers) is a platform built on top of arXiv (arxiv.org), specifically for research papers in the field of artificial intelligence (AI) and computer science. Hugging Face users can submit their paper at hf.co\u002Fpapers\u002Fsubmit, which features it on the Daily Papers feed (hf.co\u002Fpapers). Each day, users can upvote papers and comment on papers. Each paper page allows authors to:\n- claim their paper (by clicking their name on the `authors` field). This makes the paper page appear on their Hugging Face profile.\n- link the associated model checkpoints, datasets and Spaces by including the HF paper or arXiv URL in the model card, dataset card or README of the Space\n- link the Github repository and\u002For project page URLs\n- link the HF organization. This also makes the paper page appear on the Hugging Face organization page.\n\nWhenever someone mentions a HF paper or arXiv abstract\u002FPDF URL in a model card, dataset card or README of a Space repository, the paper will be automatically indexed. Note that not all papers indexed on Hugging Face are also submitted to daily papers. The latter is more a manner of promoting a research paper. Papers can only be submitted to daily papers up until 14 days after their publication date on arXiv.\n\nThe Hugging Face team has built an easy-to-use API to interact with paper pages. Content of the papers can be fetched as markdown, or structured metadata can be returned such as author names, linked models\u002Fdatasets\u002Fspaces, linked Github repo and project page.\n\n## When to Use\n\n- User shares a Hugging Face paper page URL (e.g. `https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F2602.08025`)\n- User shares a Hugging Face markdown paper page URL (e.g. `https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F2602.08025.md`)\n- User shares an arXiv URL (e.g. `https:\u002F\u002Farxiv.org\u002Fabs\u002F2602.08025` or  `https:\u002F\u002Farxiv.org\u002Fpdf\u002F2602.08025`)\n- User mentions a arXiv ID (e.g. `2602.08025`)\n- User asks you to summarize, explain, or analyze an AI research paper\n\n## Parsing the paper ID\n\nIt's recommended to parse the paper ID (arXiv ID) from whatever the user provides:\n\n| Input | Paper ID |\n| --- | --- |\n| `https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F2602.08025` | `2602.08025` |\n| `https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F2602.08025.md` | `2602.08025` |\n| `https:\u002F\u002Farxiv.org\u002Fabs\u002F2602.08025` | `2602.08025` |\n| `https:\u002F\u002Farxiv.org\u002Fpdf\u002F2602.08025` | `2602.08025` |\n| `2602.08025v1` | `2602.08025v1` |\n| `2602.08025` | `2602.08025` |\n\nThis allows you to provide the paper ID into any of the hub API endpoints mentioned below.\n\n### Fetch the paper page as markdown\n\nThe content of a paper can be fetched as markdown like so:\n\n```bash\ncurl -s \"https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F{PAPER_ID}.md\"\n```\n\nThis should return the Hugging Face paper page as markdown. This relies on the HTML version of the paper at https:\u002F\u002Farxiv.org\u002Fhtml\u002F{PAPER_ID}.\n\nThere are 2 exceptions:\n- Not all arXiv papers have an HTML version. If the HTML version of the paper does not exist, then the content falls back to the HTML of the Hugging Face paper page.\n- If it results in a 404, it means the paper is not yet indexed on hf.co\u002Fpapers. See [Error handling](#error-handling) for info.\n\nAlternatively, you can request markdown from the normal paper page URL, like so:\n\n```bash\ncurl -s -H \"Accept: text\u002Fmarkdown\" \"https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F{PAPER_ID}\"\n```\n\n### Paper Pages API Endpoints\n\nAll endpoints use the base URL `https:\u002F\u002Fhuggingface.co`.\n\n#### Get structured metadata\n\nFetch the paper metadata as JSON using the Hugging Face REST API:\n\n```bash\ncurl -s \"https:\u002F\u002Fhuggingface.co\u002Fapi\u002Fpapers\u002F{PAPER_ID}\"\n```\n\nThis returns structured metadata that can include:\n\n- authors (names and Hugging Face usernames, in case they have claimed the paper)\n- media URLs (uploaded when submitting the paper to Daily Papers)\n- summary (abstract) and AI-generated summary\n- project page and GitHub repository\n- organization and engagement metadata (number of upvotes)\n\nTo find models linked to the paper, use:\n\n```bash\ncurl https:\u002F\u002Fhuggingface.co\u002Fapi\u002Fmodels?filter=arxiv:{PAPER_ID}\n```\n\nTo find datasets linked to the paper, use:\n\n```bash\ncurl https:\u002F\u002Fhuggingface.co\u002Fapi\u002Fdatasets?filter=arxiv:{PAPER_ID}\n```\n\nTo find spaces linked to the paper, use:\n\n```bash\ncurl https:\u002F\u002Fhuggingface.co\u002Fapi\u002Fspaces?filter=arxiv:{PAPER_ID}\n```\n\n#### Claim paper authorship\n\nClaim authorship of a paper for a Hugging Face user:\n\n```bash\ncurl \"https:\u002F\u002Fhuggingface.co\u002Fapi\u002Fsettings\u002Fpapers\u002Fclaim\" \\\n  --request POST \\\n  --header \"Content-Type: application\u002Fjson\" \\\n  --header \"Authorization: Bearer $HF_TOKEN\" \\\n  --data '{\n    \"paperId\": \"{PAPER_ID}\",\n    \"claimAuthorId\": \"{AUTHOR_ENTRY_ID}\",\n    \"targetUserId\": \"{USER_ID}\"\n  }'\n```\n\n- Endpoint: `POST \u002Fapi\u002Fsettings\u002Fpapers\u002Fclaim`\n- Body:\n  - `paperId` (string, required): arXiv paper identifier being claimed\n  - `claimAuthorId` (string): author entry on the paper being claimed, 24-char hex ID\n  - `targetUserId` (string): HF user who should receive the claim, 24-char hex ID\n- Response: paper authorship claim result, including the claimed paper ID\n\n#### Get daily papers\n\nFetch the Daily Papers feed:\n\n```bash\ncurl -s -H \"Authorization: Bearer $HF_TOKEN\" \\\n  \"https:\u002F\u002Fhuggingface.co\u002Fapi\u002Fdaily_papers?p=0&limit=20&date=2017-07-21&sort=publishedAt\"\n```\n\n- Endpoint: `GET \u002Fapi\u002Fdaily_papers`\n- Query parameters:\n  - `p` (integer): page number\n  - `limit` (integer): number of results, between 1 and 100\n  - `date` (string): RFC 3339 full-date, for example `2017-07-21`\n  - `week` (string): ISO week, for example `2024-W03`\n  - `month` (string): month value, for example `2024-01`\n  - `submitter` (string): filter by submitter\n  - `sort` (enum): `publishedAt` or `trending`\n- Response: list of daily papers\n\n#### List papers\n\nList arXiv papers sorted by published date:\n\n```bash\ncurl -s -H \"Authorization: Bearer $HF_TOKEN\" \\\n  \"https:\u002F\u002Fhuggingface.co\u002Fapi\u002Fpapers?cursor={CURSOR}&limit=20\"\n```\n\n- Endpoint: `GET \u002Fapi\u002Fpapers`\n- Query parameters:\n  - `cursor` (string): pagination cursor\n  - `limit` (integer): number of results, between 1 and 100\n- Response: list of papers\n\n#### Search papers\n\nPerform hybrid semantic and full-text search on papers:\n\n```bash\ncurl -s -H \"Authorization: Bearer $HF_TOKEN\" \\\n  \"https:\u002F\u002Fhuggingface.co\u002Fapi\u002Fpapers\u002Fsearch?q=vision+language&limit=20\"\n```\n\nThis searches over the paper title, authors, and content.\n\n- Endpoint: `GET \u002Fapi\u002Fpapers\u002Fsearch`\n- Query parameters:\n  - `q` (string): search query, max length 250\n  - `limit` (integer): number of results, between 1 and 120\n- Response: matching papers\n\n#### Index a paper\n\nInsert a paper from arXiv by ID. If the paper is already indexed, only its authors can re-index it:\n\n```bash\ncurl \"https:\u002F\u002Fhuggingface.co\u002Fapi\u002Fpapers\u002Findex\" \\\n  --request POST \\\n  --header \"Content-Type: application\u002Fjson\" \\\n  --header \"Authorization: Bearer $HF_TOKEN\" \\\n  --data '{\n    \"arxivId\": \"{ARXIV_ID}\"\n  }'\n```\n\n- Endpoint: `POST \u002Fapi\u002Fpapers\u002Findex`\n- Body:\n  - `arxivId` (string, required): arXiv ID to index, for example `2301.00001`\n- Pattern: `^\\d{4}\\.\\d{4,5}$`\n- Response: empty JSON object on success\n\n#### Update paper links\n\nUpdate the project page, GitHub repository, or submitting organization for a paper. The requester must be the paper author, the Daily Papers submitter, or a papers admin:\n\n```bash\ncurl \"https:\u002F\u002Fhuggingface.co\u002Fapi\u002Fpapers\u002F{PAPER_OBJECT_ID}\u002Flinks\" \\\n  --request POST \\\n  --header \"Content-Type: application\u002Fjson\" \\\n  --header \"Authorization: Bearer $HF_TOKEN\" \\\n  --data '{\n    \"projectPage\": \"https:\u002F\u002Fexample.com\",\n    \"githubRepo\": \"https:\u002F\u002Fgithub.com\u002Forg\u002Frepo\",\n    \"organizationId\": \"{ORGANIZATION_ID}\"\n  }'\n```\n\n- Endpoint: `POST \u002Fapi\u002Fpapers\u002F{paperId}\u002Flinks`\n- Path parameters:\n  - `paperId` (string, required): Hugging Face paper object ID\n- Body:\n  - `githubRepo` (string, nullable): GitHub repository URL\n  - `organizationId` (string, nullable): organization ID, 24-char hex ID\n  - `projectPage` (string, nullable): project page URL\n- Response: empty JSON object on success\n\n## Error Handling\n\n- **404 on `https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F{PAPER_ID}` or `md` endpoint**: the paper is not indexed on Hugging Face paper pages yet.\n- **404 on `\u002Fapi\u002Fpapers\u002F{PAPER_ID}`**: the paper may not be indexed on Hugging Face paper pages yet.\n- **Paper ID not found**: verify the extracted arXiv ID, including any version suffix\n\n### Fallbacks\n\nIf the Hugging Face paper page does not contain enough detail for the user's question:\n\n- Check the regular paper page at `https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F{PAPER_ID}`\n- Fall back to the arXiv page or PDF for the original source:\n  - `https:\u002F\u002Farxiv.org\u002Fabs\u002F{PAPER_ID}`\n  - `https:\u002F\u002Farxiv.org\u002Fpdf\u002F{PAPER_ID}`\n\n## Notes\n\n- No authentication is required for public paper pages.\n- Write endpoints such as claim authorship, index paper, and update paper links require `Authorization: Bearer $HF_TOKEN`.\n- Prefer the `.md` endpoint for reliable machine-readable output.\n- Prefer `\u002Fapi\u002Fpapers\u002F{PAPER_ID}` when you need structured JSON fields instead of page markdown.",{"data":39,"body":40},{"name":4,"description":6},{"type":41,"children":42},"root",[43,52,58,92,97,102,109,174,180,185,328,333,340,345,390,406,411,432,437,487,493,505,512,517,548,553,581,586,605,610,629,634,653,659,664,831,891,897,902,961,1095,1101,1106,1164,1209,1215,1220,1278,1283,1329,1335,1340,1470,1523,1529,1534,1680,1755,1761,1814,1820,1825,1864,1870,1915],{"type":44,"tag":45,"props":46,"children":48},"element","h1",{"id":47},"hugging-face-paper-pages",[49],{"type":50,"value":51},"text","Hugging Face Paper Pages",{"type":44,"tag":53,"props":54,"children":55},"p",{},[56],{"type":50,"value":57},"Hugging Face Paper pages (hf.co\u002Fpapers) is a platform built on top of arXiv (arxiv.org), specifically for research papers in the field of artificial intelligence (AI) and computer science. Hugging Face users can submit their paper at hf.co\u002Fpapers\u002Fsubmit, which features it on the Daily Papers feed (hf.co\u002Fpapers). Each day, users can upvote papers and comment on papers. Each paper page allows authors to:",{"type":44,"tag":59,"props":60,"children":61},"ul",{},[62,77,82,87],{"type":44,"tag":63,"props":64,"children":65},"li",{},[66,68,75],{"type":50,"value":67},"claim their paper (by clicking their name on the ",{"type":44,"tag":69,"props":70,"children":72},"code",{"className":71},[],[73],{"type":50,"value":74},"authors",{"type":50,"value":76}," field). This makes the paper page appear on their Hugging Face profile.",{"type":44,"tag":63,"props":78,"children":79},{},[80],{"type":50,"value":81},"link the associated model checkpoints, datasets and Spaces by including the HF paper or arXiv URL in the model card, dataset card or README of the Space",{"type":44,"tag":63,"props":83,"children":84},{},[85],{"type":50,"value":86},"link the Github repository and\u002For project page URLs",{"type":44,"tag":63,"props":88,"children":89},{},[90],{"type":50,"value":91},"link the HF organization. This also makes the paper page appear on the Hugging Face organization page.",{"type":44,"tag":53,"props":93,"children":94},{},[95],{"type":50,"value":96},"Whenever someone mentions a HF paper or arXiv abstract\u002FPDF URL in a model card, dataset card or README of a Space repository, the paper will be automatically indexed. Note that not all papers indexed on Hugging Face are also submitted to daily papers. The latter is more a manner of promoting a research paper. Papers can only be submitted to daily papers up until 14 days after their publication date on arXiv.",{"type":44,"tag":53,"props":98,"children":99},{},[100],{"type":50,"value":101},"The Hugging Face team has built an easy-to-use API to interact with paper pages. Content of the papers can be fetched as markdown, or structured metadata can be returned such as author names, linked models\u002Fdatasets\u002Fspaces, linked Github repo and project page.",{"type":44,"tag":103,"props":104,"children":106},"h2",{"id":105},"when-to-use",[107],{"type":50,"value":108},"When to Use",{"type":44,"tag":59,"props":110,"children":111},{},[112,125,137,157,169],{"type":44,"tag":63,"props":113,"children":114},{},[115,117,123],{"type":50,"value":116},"User shares a Hugging Face paper page URL (e.g. ",{"type":44,"tag":69,"props":118,"children":120},{"className":119},[],[121],{"type":50,"value":122},"https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F2602.08025",{"type":50,"value":124},")",{"type":44,"tag":63,"props":126,"children":127},{},[128,130,136],{"type":50,"value":129},"User shares a Hugging Face markdown paper page URL (e.g. ",{"type":44,"tag":69,"props":131,"children":133},{"className":132},[],[134],{"type":50,"value":135},"https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F2602.08025.md",{"type":50,"value":124},{"type":44,"tag":63,"props":138,"children":139},{},[140,142,148,150,156],{"type":50,"value":141},"User shares an arXiv URL (e.g. ",{"type":44,"tag":69,"props":143,"children":145},{"className":144},[],[146],{"type":50,"value":147},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2602.08025",{"type":50,"value":149}," or  ",{"type":44,"tag":69,"props":151,"children":153},{"className":152},[],[154],{"type":50,"value":155},"https:\u002F\u002Farxiv.org\u002Fpdf\u002F2602.08025",{"type":50,"value":124},{"type":44,"tag":63,"props":158,"children":159},{},[160,162,168],{"type":50,"value":161},"User mentions a arXiv ID (e.g. ",{"type":44,"tag":69,"props":163,"children":165},{"className":164},[],[166],{"type":50,"value":167},"2602.08025",{"type":50,"value":124},{"type":44,"tag":63,"props":170,"children":171},{},[172],{"type":50,"value":173},"User asks you to summarize, explain, or analyze an AI research paper",{"type":44,"tag":103,"props":175,"children":177},{"id":176},"parsing-the-paper-id",[178],{"type":50,"value":179},"Parsing the paper ID",{"type":44,"tag":53,"props":181,"children":182},{},[183],{"type":50,"value":184},"It's recommended to parse the paper ID (arXiv ID) from whatever the user provides:",{"type":44,"tag":186,"props":187,"children":188},"table",{},[189,208],{"type":44,"tag":190,"props":191,"children":192},"thead",{},[193],{"type":44,"tag":194,"props":195,"children":196},"tr",{},[197,203],{"type":44,"tag":198,"props":199,"children":200},"th",{},[201],{"type":50,"value":202},"Input",{"type":44,"tag":198,"props":204,"children":205},{},[206],{"type":50,"value":207},"Paper ID",{"type":44,"tag":209,"props":210,"children":211},"tbody",{},[212,232,251,270,289,309],{"type":44,"tag":194,"props":213,"children":214},{},[215,224],{"type":44,"tag":216,"props":217,"children":218},"td",{},[219],{"type":44,"tag":69,"props":220,"children":222},{"className":221},[],[223],{"type":50,"value":122},{"type":44,"tag":216,"props":225,"children":226},{},[227],{"type":44,"tag":69,"props":228,"children":230},{"className":229},[],[231],{"type":50,"value":167},{"type":44,"tag":194,"props":233,"children":234},{},[235,243],{"type":44,"tag":216,"props":236,"children":237},{},[238],{"type":44,"tag":69,"props":239,"children":241},{"className":240},[],[242],{"type":50,"value":135},{"type":44,"tag":216,"props":244,"children":245},{},[246],{"type":44,"tag":69,"props":247,"children":249},{"className":248},[],[250],{"type":50,"value":167},{"type":44,"tag":194,"props":252,"children":253},{},[254,262],{"type":44,"tag":216,"props":255,"children":256},{},[257],{"type":44,"tag":69,"props":258,"children":260},{"className":259},[],[261],{"type":50,"value":147},{"type":44,"tag":216,"props":263,"children":264},{},[265],{"type":44,"tag":69,"props":266,"children":268},{"className":267},[],[269],{"type":50,"value":167},{"type":44,"tag":194,"props":271,"children":272},{},[273,281],{"type":44,"tag":216,"props":274,"children":275},{},[276],{"type":44,"tag":69,"props":277,"children":279},{"className":278},[],[280],{"type":50,"value":155},{"type":44,"tag":216,"props":282,"children":283},{},[284],{"type":44,"tag":69,"props":285,"children":287},{"className":286},[],[288],{"type":50,"value":167},{"type":44,"tag":194,"props":290,"children":291},{},[292,301],{"type":44,"tag":216,"props":293,"children":294},{},[295],{"type":44,"tag":69,"props":296,"children":298},{"className":297},[],[299],{"type":50,"value":300},"2602.08025v1",{"type":44,"tag":216,"props":302,"children":303},{},[304],{"type":44,"tag":69,"props":305,"children":307},{"className":306},[],[308],{"type":50,"value":300},{"type":44,"tag":194,"props":310,"children":311},{},[312,320],{"type":44,"tag":216,"props":313,"children":314},{},[315],{"type":44,"tag":69,"props":316,"children":318},{"className":317},[],[319],{"type":50,"value":167},{"type":44,"tag":216,"props":321,"children":322},{},[323],{"type":44,"tag":69,"props":324,"children":326},{"className":325},[],[327],{"type":50,"value":167},{"type":44,"tag":53,"props":329,"children":330},{},[331],{"type":50,"value":332},"This allows you to provide the paper ID into any of the hub API endpoints mentioned below.",{"type":44,"tag":334,"props":335,"children":337},"h3",{"id":336},"fetch-the-paper-page-as-markdown",[338],{"type":50,"value":339},"Fetch the paper page as markdown",{"type":44,"tag":53,"props":341,"children":342},{},[343],{"type":50,"value":344},"The content of a paper can be fetched as markdown like so:",{"type":44,"tag":346,"props":347,"children":352},"pre",{"className":348,"code":349,"language":350,"meta":351,"style":351},"language-bash shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","curl -s \"https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F{PAPER_ID}.md\"\n","bash","",[353],{"type":44,"tag":69,"props":354,"children":355},{"__ignoreMap":351},[356],{"type":44,"tag":357,"props":358,"children":361},"span",{"class":359,"line":360},"line",1,[362,368,374,380,385],{"type":44,"tag":357,"props":363,"children":365},{"style":364},"--shiki-light:#E2931D;--shiki-default:#FFCB6B;--shiki-dark:#FFCB6B",[366],{"type":50,"value":367},"curl",{"type":44,"tag":357,"props":369,"children":371},{"style":370},"--shiki-light:#91B859;--shiki-default:#C3E88D;--shiki-dark:#C3E88D",[372],{"type":50,"value":373}," -s",{"type":44,"tag":357,"props":375,"children":377},{"style":376},"--shiki-light:#39ADB5;--shiki-default:#89DDFF;--shiki-dark:#89DDFF",[378],{"type":50,"value":379}," \"",{"type":44,"tag":357,"props":381,"children":382},{"style":370},[383],{"type":50,"value":384},"https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F{PAPER_ID}.md",{"type":44,"tag":357,"props":386,"children":387},{"style":376},[388],{"type":50,"value":389},"\"\n",{"type":44,"tag":53,"props":391,"children":392},{},[393,395,404],{"type":50,"value":394},"This should return the Hugging Face paper page as markdown. 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update paper links require ",{"type":44,"tag":69,"props":1884,"children":1886},{"className":1885},[],[1887],{"type":50,"value":1888},"Authorization: Bearer $HF_TOKEN",{"type":50,"value":405},{"type":44,"tag":63,"props":1891,"children":1892},{},[1893,1895,1901],{"type":50,"value":1894},"Prefer the ",{"type":44,"tag":69,"props":1896,"children":1898},{"className":1897},[],[1899],{"type":50,"value":1900},".md",{"type":50,"value":1902}," endpoint for reliable machine-readable output.",{"type":44,"tag":63,"props":1904,"children":1905},{},[1906,1908,1913],{"type":50,"value":1907},"Prefer ",{"type":44,"tag":69,"props":1909,"children":1911},{"className":1910},[],[1912],{"type":50,"value":1801},{"type":50,"value":1914}," when you need structured JSON fields instead of page markdown.",{"type":44,"tag":1916,"props":1917,"children":1918},"style",{},[1919],{"type":50,"value":1920},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":1922,"total":2091},[1923,1941,1957,1972,1986,1999,2012,2027,2041,2051,2064,2078],{"slug":1924,"name":1924,"fn":1925,"description":1926,"org":1927,"tags":1928,"stars":1938,"repoUrl":1939,"updatedAt":1940},"train-sentence-transformers","train sentence-transformers models","Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder; dense or static embedding model; for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker; pair scoring for two-stage retrieval \u002F pair classification), and `SparseEncoder` (SPLADE, sparse embedding model; for learned-sparse retrieval). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1929,1930,1931,1932,1935],{"name":22,"slug":23,"type":15},{"name":9,"slug":17,"type":15},{"name":19,"slug":20,"type":15},{"name":1933,"slug":1934,"type":15},"Python","python",{"name":1936,"slug":1937,"type":15},"Search","search",18914,"https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fsentence-transformers","2026-05-08T05:09:16.820066",{"slug":1942,"name":1942,"fn":1943,"description":1944,"org":1945,"tags":1946,"stars":1954,"repoUrl":1955,"updatedAt":1956},"trl-training","train and fine-tune LLMs with TRL","Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). Supports SFT, DPO, GRPO, KTO, RLOO and Reward Model training via CLI commands.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1947,1950,1951,1952,1953],{"name":1948,"slug":1949,"type":15},"AI Infrastructure","ai-infrastructure",{"name":22,"slug":23,"type":15},{"name":9,"slug":17,"type":15},{"name":19,"slug":20,"type":15},{"name":1933,"slug":1934,"type":15},18850,"https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Ftrl","2026-04-06T18:25:32.746828",{"slug":1958,"name":1958,"fn":1959,"description":1960,"org":1961,"tags":1962,"stars":27,"repoUrl":28,"updatedAt":1971},"hf-cli","manage Hugging Face Hub resources via CLI","Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or scheduling jobs on Hugging Face infrastructure; managing Hugging Face repos; discussions and pull requests; browsing models, datasets and spaces; reading, searching, or browsing academic papers; managing collections; querying datasets; configuring spaces; setting up webhooks; or deploying and managing HF Inference Endpoints. Make sure to use this skill whenever the user mentions 'hf', 'huggingface', 'Hugging Face', 'huggingface-cli', or 'hugging face cli', or wants to do anything related to the Hugging Face ecosystem and to AI and ML in general. Also use for cloud storage needs like training checkpoints, data pipelines, or agent traces. Use even if the user doesn't explicitly ask for a CLI command. Replaces the deprecated `huggingface-cli`.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1963,1966,1969,1970],{"name":1964,"slug":1965,"type":15},"CLI","cli",{"name":1967,"slug":1968,"type":15},"Datasets","datasets",{"name":9,"slug":17,"type":15},{"name":19,"slug":20,"type":15},"2026-04-06T18:25:34.020855",{"slug":1973,"name":1973,"fn":1974,"description":1975,"org":1976,"tags":1977,"stars":27,"repoUrl":28,"updatedAt":1985},"hf-cloud-aws-context-discovery","discover local AWS environment context","Discover the user's local AWS context (active profile, region, account ID, caller identity) at the start of any AWS task. Use this skill before any other AWS work — deploying to SageMaker, creating resources, calling AWS APIs, or anything that touches an AWS account. Use it especially when the user has not specified a region or profile explicitly, when they say things like \"use my AWS account\", \"deploy to AWS\", \"use my profile\", or when about to make any AWS CLI or SDK call. Never guess the region or account ID — always use this skill to read it from the local configuration first.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1978,1981,1982],{"name":1979,"slug":1980,"type":15},"AWS","aws",{"name":1964,"slug":1965,"type":15},{"name":1983,"slug":1984,"type":15},"Configuration","configuration","2026-07-08T05:55:33.716099",{"slug":1987,"name":1987,"fn":1988,"description":1989,"org":1990,"tags":1991,"stars":27,"repoUrl":28,"updatedAt":1998},"hf-cloud-python-env-setup","set up Python environments for AWS","Set up an isolated Python environment for SageMaker \u002F AWS work, with the right Python version and current boto3. Use this skill whenever Python code will be executed for a SageMaker deployment, training job, or any AWS automation — including when about to run `pip install`, when about to invoke `boto3`, when creating or activating a virtualenv, or when the user asks to \"set up the environment\". Never use system Python and never `pip install` into it. Always isolate. This skill prevents the most common failure modes: wrong Python version, dependency conflicts, and stale SDKs.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1992,1993,1994,1997],{"name":1948,"slug":1949,"type":15},{"name":1979,"slug":1980,"type":15},{"name":1995,"slug":1996,"type":15},"Engineering","engineering",{"name":1933,"slug":1934,"type":15},"2026-07-08T05:55:32.505017",{"slug":2000,"name":2000,"fn":2001,"description":2002,"org":2003,"tags":2004,"stars":27,"repoUrl":28,"updatedAt":2011},"hf-cloud-sagemaker-deployment-planner","plan model deployments to Amazon SageMaker","Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like \"deploy a model\", \"host this LLM on AWS\", \"serve this embedding model\", \"deploy a reranker\", \"deploy a text-to-image \u002F diffusion model\", \"host this for async inference\", \"create an endpoint\", \"serve my fine-tuned model\", or any request that involves making a model available for inference on AWS. Use this even when the user is vague (e.g. \"I just want to get this running on AWS, you figure it out\"). Works for text-generation LLMs, embedding models, rerankers, classifiers, text-to-image \u002F diffusion models — picks the right serving stack and chooses between real-time and async inference. This is the entry-point skill for SageMaker deployment work — it asks clarifying questions, picks a deployment pathway, and coordinates the other deployment skills.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2005,2006,2007,2010],{"name":1948,"slug":1949,"type":15},{"name":1979,"slug":1980,"type":15},{"name":2008,"slug":2009,"type":15},"Deployment","deployment",{"name":9,"slug":17,"type":15},"2026-07-08T05:55:37.387689",{"slug":2013,"name":2013,"fn":2014,"description":2015,"org":2016,"tags":2017,"stars":27,"repoUrl":28,"updatedAt":2026},"hf-cloud-sagemaker-iam-preflight","configure SageMaker IAM roles","Ensure a usable SageMaker execution role exists before deploying or training. Use this skill whenever about to create a SageMaker endpoint, model, training job, or any resource that requires an execution role. Use it especially when the user has not provided a role ARN explicitly, when scripts are about to call `iam:CreateRole`, or when an AccessDenied error mentions an IAM action. Never blindly call `iam:CreateRole` — always check for existing roles first. This skill prevents the most common SageMaker deployment failure: trying to create IAM resources from an SSO principal that has no IAM write permissions.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2018,2019,2020,2023],{"name":1948,"slug":1949,"type":15},{"name":1979,"slug":1980,"type":15},{"name":2021,"slug":2022,"type":15},"Permissions","permissions",{"name":2024,"slug":2025,"type":15},"Security","security","2026-07-08T05:55:34.948771",{"slug":2028,"name":2028,"fn":2029,"description":2030,"org":2031,"tags":2032,"stars":27,"repoUrl":28,"updatedAt":2040},"hf-cloud-sagemaker-production-defaults","create production-ready SageMaker endpoints","Create a SageMaker endpoint (real-time or async) with autoscaling, CloudWatch alarms, and tagging enabled by default. Use this skill whenever about to create a SageMaker endpoint, write deployment code that calls `create_endpoint`, or finalize a deployment after the image URI and IAM role are known. Provides deploy.py for real-time endpoints and deploy_async.py for async endpoints (with genuine scale-to-zero support). This is the last step in the SageMaker deployment workflow. Never generate a bare `create_endpoint` call without these defaults — endpoints without autoscaling or alarms are demos, not deployments.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2033,2034,2035,2036,2037],{"name":1948,"slug":1949,"type":15},{"name":1979,"slug":1980,"type":15},{"name":2008,"slug":2009,"type":15},{"name":9,"slug":17,"type":15},{"name":2038,"slug":2039,"type":15},"Monitoring","monitoring","2026-07-08T05:55:38.664702",{"slug":2042,"name":2042,"fn":2043,"description":2044,"org":2045,"tags":2046,"stars":27,"repoUrl":28,"updatedAt":2050},"hf-cloud-serving-image-selection","select SageMaker serving containers","Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says \"deploy this LLM\", \"host this HuggingFace model\", \"serve this fine-tuned model\", \"deploy this embedding model\", \"host a reranker\", \"serve a sentence-transformers model\", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings\u002Fcross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2047,2048,2049],{"name":1948,"slug":1949,"type":15},{"name":1979,"slug":1980,"type":15},{"name":2008,"slug":2009,"type":15},"2026-07-08T05:55:36.173465",{"slug":2052,"name":2052,"fn":2053,"description":2054,"org":2055,"tags":2056,"stars":27,"repoUrl":28,"updatedAt":2063},"hf-mcp","access Hugging Face Hub via MCP","Use Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF MCP server.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2057,2058,2059,2060],{"name":1967,"slug":1968,"type":15},{"name":9,"slug":17,"type":15},{"name":19,"slug":20,"type":15},{"name":2061,"slug":2062,"type":15},"MCP","mcp","2026-04-06T18:25:50.364185",{"slug":2065,"name":2065,"fn":2066,"description":2067,"org":2068,"tags":2069,"stars":27,"repoUrl":28,"updatedAt":2077},"hf-mem","estimate memory for Hugging Face models","Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2070,2071,2072,2073,2074],{"name":1948,"slug":1949,"type":15},{"name":1964,"slug":1965,"type":15},{"name":9,"slug":17,"type":15},{"name":19,"slug":20,"type":15},{"name":2075,"slug":2076,"type":15},"Performance","performance","2026-06-13T07:23:57.101435",{"slug":2079,"name":2079,"fn":2080,"description":2081,"org":2082,"tags":2083,"stars":27,"repoUrl":28,"updatedAt":2090},"huggingface-best","find and compare Hugging Face models","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. Triggers on: \"best model for X\", \"what model should I use for\", \"top models for [task]\", \"which model runs on my laptop\u002Fmachine\u002Fdevice\", \"recommend a model for\", \"what LLM should I use for\", \"compare models for\", \"what's state of the art for\", or any question about choosing an AI model for a specific use case. Always use this skill when the user wants model recommendations or comparisons, even if they don't explicitly mention HuggingFace or benchmarks.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2084,2087,2088,2089],{"name":2085,"slug":2086,"type":15},"Analytics","analytics",{"name":9,"slug":17,"type":15},{"name":19,"slug":20,"type":15},{"name":13,"slug":14,"type":15},"2026-04-24T05:09:45.870658",37,{"items":2093,"total":2142},[2094,2101,2107,2114,2121,2128,2136],{"slug":1958,"name":1958,"fn":1959,"description":1960,"org":2095,"tags":2096,"stars":27,"repoUrl":28,"updatedAt":1971},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2097,2098,2099,2100],{"name":1964,"slug":1965,"type":15},{"name":1967,"slug":1968,"type":15},{"name":9,"slug":17,"type":15},{"name":19,"slug":20,"type":15},{"slug":1973,"name":1973,"fn":1974,"description":1975,"org":2102,"tags":2103,"stars":27,"repoUrl":28,"updatedAt":1985},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2104,2105,2106],{"name":1979,"slug":1980,"type":15},{"name":1964,"slug":1965,"type":15},{"name":1983,"slug":1984,"type":15},{"slug":1987,"name":1987,"fn":1988,"description":1989,"org":2108,"tags":2109,"stars":27,"repoUrl":28,"updatedAt":1998},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2110,2111,2112,2113],{"name":1948,"slug":1949,"type":15},{"name":1979,"slug":1980,"type":15},{"name":1995,"slug":1996,"type":15},{"name":1933,"slug":1934,"type":15},{"slug":2000,"name":2000,"fn":2001,"description":2002,"org":2115,"tags":2116,"stars":27,"repoUrl":28,"updatedAt":2011},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2117,2118,2119,2120],{"name":1948,"slug":1949,"type":15},{"name":1979,"slug":1980,"type":15},{"name":2008,"slug":2009,"type":15},{"name":9,"slug":17,"type":15},{"slug":2013,"name":2013,"fn":2014,"description":2015,"org":2122,"tags":2123,"stars":27,"repoUrl":28,"updatedAt":2026},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2124,2125,2126,2127],{"name":1948,"slug":1949,"type":15},{"name":1979,"slug":1980,"type":15},{"name":2021,"slug":2022,"type":15},{"name":2024,"slug":2025,"type":15},{"slug":2028,"name":2028,"fn":2029,"description":2030,"org":2129,"tags":2130,"stars":27,"repoUrl":28,"updatedAt":2040},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2131,2132,2133,2134,2135],{"name":1948,"slug":1949,"type":15},{"name":1979,"slug":1980,"type":15},{"name":2008,"slug":2009,"type":15},{"name":9,"slug":17,"type":15},{"name":2038,"slug":2039,"type":15},{"slug":2042,"name":2042,"fn":2043,"description":2044,"org":2137,"tags":2138,"stars":27,"repoUrl":28,"updatedAt":2050},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2139,2140,2141],{"name":1948,"slug":1949,"type":15},{"name":1979,"slug":1980,"type":15},{"name":2008,"slug":2009,"type":15},24]