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Browse trending papers, search by keyword, and get paper details from Hugging Face Papers. Use when the user wants to find ML research, asks about recent AI papers, trending models, or mentions Hugging Face Papers.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
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用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/Prismer-AI/Prismer --skill hf-papers命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
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| name | hf-papers |
| description | Browse trending papers, search by keyword, and get paper details from Hugging Face Papers. Use when the user wants to find ML research, asks about recent AI papers, trending models, or mentions Hugging Face Papers. |
| metadata | {"openclaw":{"emoji":"🤗","tags":["huggingface","research","academic","papers","trending"],"requires":{"bins":"[Truncated]","os":"[Truncated]"}}} |
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 textCore academic workspace tools: LaTeX compilation, Jupyter execution, PDF loading, UI control, content updates, and arXiv paper conversion. Use when the user asks to compile LaTeX, run Python code, load PDFs, display data, or control workspace editors.
Read and analyze arXiv papers by fetching LaTeX source, listing sections, or extracting abstracts. Use when the user mentions arXiv, research papers, preprints, paper IDs like 2301.xxxxx, or wants to read academic publications.
List installed skills, search the skill registry, install new skills, and manage workspace extensions. Use when the user asks to find, install, list, or configure skills, or mentions skill registry, extensions, or workspace capabilities.