huggingface-papers
🤖 AI Summary
This skill retrieves and interacts with AI research papers from the Hugging Face Papers platform (hf.co/papers), allowing an agent to fetch daily paper feeds, submit papers, and manage author claims or link associated models, datasets, and repositories.
How to Install
Claude Code:
git clone --depth 1 https://github.com/huggingface/skills.git && cp skills/skills/huggingface-papers ~/.claude/skills/huggingface-papers -r# 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 `authors` field). 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/2602.08025` or `https://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:
```bash
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
Details
| Category | AI/ML → ml |
| Source | huggingface/skills |
| SKILL.md | View on GitHub → |
| Repo Stars | ★ 10.7K |
| Est. per Skill | 357 (shared across 30 skills from this repo) |
| Difficulty | Intermediate |
| Risk Level | N/A |
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