hugging-face-papers
Has your agent look up and read research papers on Hugging Face. It accesses structured metadata like authors, models, and Github repositories.
What it does
This tool looks up and reads AI research papers from the Hugging Face Papers platform. It can extract information like authors, summaries, linked models, and code.
When to use it
- ✓When you find a link to an AI research paper.
- ✓When asking for a summary or explanation of a scientific paper.
- ✓When looking for models or code related to a specific paper.
Example of what to ask your AI
“Summarize the research paper with ID 2602.08025.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool uses the paper's ID (like its arXiv number) to fetch information from a special database. It can display the paper's text or organized data like authors and links.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill hugging-face-papersOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/hugging-face-papers/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Hugging Face Paper Pages
Hugging Face Paper pages (hf.co/papers) 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/papers/submit, which features it on the Daily Papers feed (hf.co/papers). Each day, users can upvote papers and comment on papers. Each paper page allows authors to:
- claim their paper (by clicking their name on the
authorsfield). This makes the paper page appear on their Hugging Face profile. - 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
- link the Github repository and/or project page URLs
- link the HF organization. This also makes the paper page appear on the Hugging Face organization page.
Whenever someone mentions a HF paper or arXiv abstract/PDF 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.
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/datasets/spaces, linked Github repo and project page.
When to Use
- User shares a Hugging Face paper page URL (e.g.
https://huggingface.co/papers/2602.08025) - User shares a Hugging Face markdown paper page URL (e.g.
https://huggingface.co/papers/2602.08025.md) - User shares an arXiv URL (e.g.
https://arxiv.org/abs/2602.08025orhttps://arxiv.org/pdf/2602.08025) - User mentions a arXiv ID (e.g.
2602.08025) - User asks you to summarize, explain, or analyze an AI research paper
Parsing the paper ID
It's recommended to parse the paper ID (arXiv ID) from whatever the user provides:
| Input | Paper ID |
|---|---|
https://huggingface.co/papers/2602.08025 |
2602.08025 |
https://huggingface.co/papers/2602.08025.md |
2602.08025 |
https://arxiv.org/abs/2602.08025 |
2602.08025 |
https://arxiv.org/pdf/2602.08025 |
2602.08025 |
2602.08025v1 |
2602.08025v1 |
2602.08025 |
2602.08025 |
This allows you to provide the paper ID into any of the hub API endpoints mentioned below.
Fetch the paper page as markdown
The content of a paper can be fetched as markdown like so:
curl -s "https://huggingface.co/papers/{PAPER_ID}.md"
This should return the Hugging Face paper page as markdown. This relies on the HTML version of the paper at https://arxiv.org/html/{PAPER_ID}.
There are 2 exceptions:
- 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.
- If it results in a 404, it means the paper is not yet indexed on hf.co/papers. See Error handling for info.
Alternatively, you can request markdown from the normal paper page URL, like so:
curl -s -H "Accept: text/markdown" "https://huggingface.co/papers/{PAPER_ID}"
Paper Pages API Endpoints
All endpoints use the base URL https://huggingface.co.
Get structured metadata
Fetch the paper metadata as JSON using the Hugging Face REST API:
curl -s "https://huggingface.co/api/papers/{PAPER_ID}"
This returns structured metadata that can include:
- authors (names and Hugging Face usernames, in case they have claimed the paper)
- media URLs (uploaded when submitting the paper to Daily Papers)
- summary (abstract) and AI-generated summary
- project page and GitHub repository
- organization and engagement metadata (number of upvotes)
To find models linked to the paper, use:
curl https://huggingface.co/api/models?filter=arxiv:{PAPER_ID}
To find datasets linked to the paper, use:
curl https://huggingface.co/api/datasets?filter=arxiv:{PAPER_ID}
To find spaces linked to the paper, use:
curl https://huggingface.co/api/spaces?filter=arxiv:{PAPER_ID}
Claim paper authorship
Claim authorship of a paper for a Hugging Face user:
curl "https://huggingface.co/api/settings/papers/claim" \
--request POST \
…
FAQ
What is Hugging Face Papers?
It's a website where AI researchers publish and discuss scientific papers, built on top of arXiv.
How does the tool find papers?
It uses the paper's identification number (arXiv ID) that you provide or find in a link.
What information can I get about a paper?
You can get the full text in markdown format, author names, the abstract, links to models, datasets, GitHub code, and project pages.
Details
Original description by the author
Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page.
Popularity
- GitHub stars
- 48k
- Ranking position
- #3227
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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