القائمة
Browse trending papers, search by keyword, and get paper details from Hugging Face Papers
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Search, download, and summarize academic papers from arXiv. Built for AI/ML researchers.
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| name | hf-papers |
| description | Browse trending papers, search by keyword, and get paper details from Hugging Face Papers |
| metadata | {"openclaw":{"emoji":"🤗","tags":["huggingface","research","academic","papers","trending"],"requires":{"bins":[],"os":["darwin","linux","win32"]}}} |
Browse, search, and analyze papers from the Hugging Face Papers platform. Get trending papers, search by topic, and retrieve detailed metadata including community engagement and linked resources.
This skill wraps the Hugging Face Papers public API. It provides access to daily trending papers, keyword search, paper details (abstract, authors, upvotes, GitHub repos, project pages), and discussion comments. No authentication required.
For full paper text, use the returned arXiv ID with the arxiv-reader skill.
Results are cached locally (~/.cache/hf-papers/) for fast repeat access.
hf_daily_papers to see what's trending todayhf_search_papers to find papers on a topichf_paper_detail to get full metadata for a specific paperhf_paper_comments to read community discussionarxiv_fetch (from arxiv-reader) with the paper's arXiv ID for full textGet today's trending papers from Hugging Face.
Parameters:
limit (number, optional): Max papers to return (default: 20, max: 100)sort (string, optional): Sort by upvotes or date (default: upvotes)Returns: { papers: [{ id, title, summary, upvotes, authors, publishedAt, githubRepo?, projectPage?, ai_summary?, ai_keywords? }], count: number }
Example:
{ "limit": 10, "sort": "upvotes" }
Search Hugging Face Papers by keyword.
Parameters:
query (string, required): Search queryReturns: { papers: [{ id, title, summary, upvotes, authors, publishedAt, githubRepo?, projectPage?, ai_summary? }], query: string, count: number }
Example:
{ "query": "multimodal reasoning" }
Get detailed metadata for a specific paper.
Parameters:
paper_id (string, required): Paper ID (arXiv ID, e.g. 2401.12345)Returns: { id, title, summary, authors, publishedAt, upvotes, numComments, githubRepo?, githubStars?, projectPage?, ai_summary?, ai_keywords?, organization? }
Example:
{ "paper_id": "2401.12345" }
Get discussion comments for a paper.
Parameters:
paper_id (string, required): Paper ID (arXiv ID)Returns: { paper_id, comments: [{ author, content, createdAt }], count: number }
Example:
{ "paper_id": "2401.12345" }
arxiv-reader skill for full LaTeX text