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Agent skill · Audited from source

huggingface-papers

skill · huggingface/skillsApache-2.0 licenceAudited 31 Oct 2026 · source 33087d8174de

★ 11,241pushed today
✓SAFEGrade B · 89/100 · 31 Oct 2026 · 33087d8
11,241repo stars · whole huggingface/skills

Is it safe? Yes — SAFE, grade B (89/100). We read this skill's source at commit 33087d8174de and found no filesystem, network or shell access and no hard-coded secrets. 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…

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Cite this page

ModelsAtlas. “huggingface-papers (huggingface/skills)”. Retrieved 31 Oct 2026. https://modelsatlas.com/skills/huggingface/huggingface-papers

Trust audit

Audit 0.4.1 · source read 31 Oct 2026

Trust score89/100✓SAFE B
0 critical0 high0 medium0 low0 info

SAFE means nothing in the source contradicts what the skill says it does. Grade B is shared by 16,667 of 17,602 audited skills.

What the source does

  • FilesystemNone observedNo reads or writes in the source
  • NetworkNone observedNo outbound calls in the source
  • ShellNone observedNo commands run in the source
  • DependenciesPinnedNothing floats
  • SecretsNone foundNo keys in source

Five audit layers

LayerWhat we checkResultFindings
L0 ProvenanceRepo owner, licence, archive status, activityPASS0
L1 Static sourceSecrets, path traversal, supply chain, hidden codeNA0
L2 InstructionsPrompt injection, authority framing, hidden directivesPASS0
L3 InventoryThe files and declarations match the sourcePASS0
L4 SandboxRun in isolation and watch behaviourSKIPPED why0

Gates applied: no behavioural pass.

Findings (0)

No findings at any severity. We re-audit when the source changes.

Audit 0.4.1 · source sha 33087d8174de · 31 Oct 2026Report an issue or request a re-scan

Provenance

Who published it, and what the verdict is pinned to

  • Publisherhuggingface GitHub organizationOwner of the repository this skill is read from
  • Source folderhuggingface/skills/skills/huggingface-papersThe exact folder we audited
  • LicenceApache-2.0As the repository states it
  • Commit pinned for this verdict33087d8174deA new audit runs when the source changes
  • Repository statusNot archivedPushed today
  • Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

What it tells the agent

Full source on GitHub ↗

Its 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…

---
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`

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

Install

Pinned to the audited commit 33087d8

Download folder ↗
git clone https://github.com/huggingface/skills
git -C skills checkout 33087d8174de

The author documents no install command, so this uses the repository directly. Our verdict covers commit 33087d8174de only.

Works in which agents?

What its own text says, not a test

AgentStatusMore
CursorMentioned in its text, not testedSkills for Cursor · About Cursor
“Mentioned” means the skill’s text names the agent. We have not run it there.

Audit history

Every audit, every source change

  1. ✓SAFE B 89 / 100

    first audit · source 33087d8174de

README badge

For maintainers · shows the latest audit

ModelsAtlas: SAFE BMODELSATLASSAFE · B
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Add this badge to your README

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Questions about huggingface-papers

What does the huggingface-papers skill do?
Give your agents the power of the Hugging Face ecosystem
Is huggingface-papers safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can huggingface-papers access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
Which assistants does huggingface-papers work with?
Its documentation mentions cursor. That is what the text claims, not a compatibility test we ran.
How current is this page?
The grade is for one exact copy of the source (33087d8174de), read on 2026-10-31. The repository is watched, and a new audit runs when it changes — this is the first audit.

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