Agent Native

huggingface-papers

This skill retrieves and analyzes AI research papers from Hugging Face's paper index and arXiv by fetching paper content as markdown or structured metadata…

by Hugging FaceOfficial publisherNot reviewed

Identity proven by GitHub's verified organisation badge (huggingface)

Agents
Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode, claude.ai and API
License
Apache-2.0 (permissive)
Pin
c3ff942pinned Oct 9, 2026
Context cost
about 2,200 tokenswhen the skill loads
GitHub stars
11,150on huggingface/skills
Scripts
None found

What it does

“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. 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.”
The author's description, from SKILL.md

Generated summary from the skill's own text

This skill retrieves and analyzes AI research papers from Hugging Face's paper index and arXiv by fetching paper content as markdown or structured metadata including authors, linked models, datasets, and repositories. The agent can look up papers via Hugging Face URLs, arXiv links, or paper IDs, and summarize or explain their contents using the Hugging Face Papers API.

When to use it

  • User provides a Hugging Face paper URL or arXiv link to analyze
  • User asks to summarize or explain an AI research paper
  • User mentions an arXiv ID and wants paper details
  • User wants to find models, datasets, or code linked to a paper

When not to

  • User needs papers from non-AI domains or non-arXiv sources
  • Paper is not indexed on Hugging Face and no arXiv ID provided

Install

These commands install from the publisher's repository and need no account.

skills CLI

npx skills add huggingface/skills --skill huggingface-papers

Installs from the repository's default branch, which may have changed since the commit we checked.

GitHub CLI 2.90 or later

gh skill install huggingface/skills huggingface-papers@c3ff942be3e07116b475253f0a8fc4f54682c1a8

Installs the commit we checked (c3ff942).

Copy by hand

Download the folder at c3ff942 and put it in .claude/skills/huggingface-papers for Claude Code and in .agents/skills/huggingface-papers for Codex, Cursor, Gemini CLI, GitHub Copilot and OpenCode.

Skill folder on GitHub at c3ff942

Claude Code plugin

/plugin install huggingface-skills@claude-plugins-official

The publisher's plugin in Claude's official marketplace. It may hold more than this skill, and we have not pinned it.

Copy for your agent

A prompt for any coding agent. It installs this skill from GitHub at the pinned commit, copies the files as they are, and runs nothing unless you ask.

Prompt

Install the agent skill "huggingface-papers" by Hugging Face.

Source, pinned to commit c3ff942be3e07116b475253f0a8fc4f54682c1a8:
https://github.com/huggingface/skills/tree/c3ff942be3e07116b475253f0a8fc4f54682c1a8/skills/huggingface-papers

1. Download that folder at exactly this commit. Do not use the default branch.
2. Copy every file unchanged, keeping the folder structure, into .claude/skills/huggingface-papers/ for Claude Code and .agents/skills/huggingface-papers/ for Codex, Cursor, Gemini CLI, GitHub Copilot and OpenCode. Create the folders if they do not exist.
3. Do not edit the files, and do not run any script in the folder unless I ask you to.
4. When you are done, list the files you copied and where you put them.

Listing and checks: https://www.agentnative.dev/skills/hugging-face/huggingface-papers-skills

SKILL.md at the pin

---
name: huggingface-papers
description: 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. 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.
---

# 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 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](#error-handling) for info.

Alternatively, you can request markdown from the normal paper page URL, like so:

```bash
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:

```bash
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:

```bash
curl https://huggingface.co/api/models?filter=arxiv:{PAPER_ID}
```

To find datasets linked to the paper, use:

```bash
curl https://huggingface.co/api/datasets?filter=arxiv:{PAPER_ID}
```

To find spaces linked to the paper, use:

```bash
curl https://huggingface.co/api/spaces?filter=arxiv:{PAPER_ID}
```

#### Claim paper authorship

Claim authorship of a paper for a Hugging Face user:

```bash
curl "https://huggingface.co/api/settings/papers/claim" \
  --request POST \
  --header "Content-Type: application/json" \
  --header "Authorization: Bearer $HF_TOKEN" \
  --data '{
    "paperId": "{PAPER_ID}",
    "claimAuthorId": "{AUTHOR_ENTRY_ID}",
    "targetUserId": "{USER_ID}"
  }'
```

- Endpoint: `POST /api/settings/papers/claim`
- Body:
  - `paperId` (string, required): arXiv paper identifier being claimed
  - `claimAuthorId` (string): author entry on the paper being claimed, 24-char hex ID
  - `targetUserId` (string): HF user who should receive the claim, 24-char hex ID
- Response: paper authorship claim result, including the claimed paper ID

#### Get daily papers

Fetch the Daily Papers feed:

```bash
curl -s -H "Authorization: Bearer $HF_TOKEN" \
  "https://huggingface.co/api/daily_papers?p=0&limit=20&date=2017-07-21&sort=publishedAt"
```

- Endpoint: `GET /api/daily_papers`
- Query parameters:
  - `p` (integer): page number
  - `limit` (integer): number of results, between 1 and 100
  - `date` (string): RFC 3339 full-date, for example `2017-07-21`
  - `week` (string): ISO week, for example `2024-W03`
  - `month` (string): month value, for example `2024-01`
  - `submitter` (string): filter by submitter
  - `sort` (enum): `publishedAt` or `trending`
- Response: list of daily papers

#### List papers

List arXiv papers sorted by published date:

```bash
curl -s -H "Authorization: Bearer $HF_TOKEN" \
  "https://huggingface.co/api/papers?cursor={CURSOR}&limit=20"
```

- Endpoint: `GET /api/papers`
- Query parameters:
  - `cursor` (string): pagination cursor
  - `limit` (integer): number of results, between 1 and 100
- Response: list of papers

#### Search papers

Perform hybrid semantic and full-text search on papers:

```bash
curl -s -H "Authorization: Bearer $HF_TOKEN" \
  "https://huggingface.co/api/papers/search?q=vision+language&limit=20"
```

This searches over the paper title, authors, and content.

- Endpoint: `GET /api/papers/search`
- Query parameters:
  - `q` (string): search query, max length 250
  - `limit` (integer): number of results, between 1 and 120
- Response: matching papers

#### Index a paper

Insert a paper from arXiv by ID. If the paper is already indexed, only its authors can re-index it:

```bash
curl "https://huggingface.co/api/papers/index" \
  --request POST \
  --header "Content-Type: application/json" \
  --header "Authorization: Bearer $HF_TOKEN" \
  --data '{
    "arxivId": "{ARXIV_ID}"
  }'
```

- Endpoint: `POST /api/papers/index`
- Body:
  - `arxivId` (string, required): arXiv ID to index, for example `2301.00001`
- Pattern: `^\d{4}\.\d{4,5}$`
- Response: empty JSON object on success

#### Update paper links

Update 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:

```bash
curl "https://huggingface.co/api/papers/{PAPER_OBJECT_ID}/links" \
  --request POST \
  --header "Content-Type: application/json" \
  --header "Authorization: Bearer $HF_TOKEN" \
  --data '{
    "projectPage": "https://example.com",
    "githubRepo": "https://github.com/org/repo",
    "organizationId": "{ORGANIZATION_ID}"
  }'
```

- Endpoint: `POST /api/papers/{paperId}/links`
- Path parameters:
  - `paperId` (string, required): Hugging Face paper object ID
- Body:
  - `githubRepo` (string, nullable): GitHub repository URL
  - `organizationId` (string, nullable): organization ID, 24-char hex ID
  - `projectPage` (string, nullable): project page URL
- Response: empty JSON object on success

## Error Handling

- **404 on `https://huggingface.co/papers/{PAPER_ID}` or `md` endpoint**: the paper is not indexed on Hugging Face paper pages yet.
- **404 on `/api/papers/{PAPER_ID}`**: the paper may not be indexed on Hugging Face paper pages yet.
- **Paper ID not found**: verify the extracted arXiv ID, including any version suffix

### Fallbacks

If the Hugging Face paper page does not contain enough detail for the user's question:

- Check the regular paper page at `https://huggingface.co/papers/{PAPER_ID}`
- Fall back to the arXiv page or PDF for the original source:
  - `https://arxiv.org/abs/{PAPER_ID}`
  - `https://arxiv.org/pdf/{PAPER_ID}`

## Notes

- No authentication is required for public paper pages.
- Write endpoints such as claim authorship, index paper, and update paper links require `Authorization: Bearer $HF_TOKEN`.
- Prefer the `.md` endpoint for reliable machine-readable output.
- Prefer `/api/papers/{PAPER_ID}` when you need structured JSON fields instead of page markdown.

From huggingface/skills at c3ff942, shown as plain text under its license: Apache-2.0 (permissive). The license text is in LICENSE.

View SKILL.md on GitHub at c3ff942

Files

1 file, 9.3 KB in total. Each file has a sha256 in the pin's manifest; the first 12 characters are shown.

Skill folder on GitHub at c3ff942

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The same SKILL.md content appears in these places too. We list it once and credit the oldest public source.

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