hugging-face-paper-pages — official hugging-face-paper-pages, official, ide skills

Verified
v1.0.0

À propos de ce Skill

Parfait pour les agents de recherche en IA nécessitant une analyse approfondie des pages de documents Hugging Face et du contenu arXiv. 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.

huggingface huggingface
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Updated: 3/21/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 10/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution Quality floor passed for review
Review Score
10/11
Quality Score
89
Canonical Locale
en
Detected Body Locale
en

Parfait pour les agents de recherche en IA nécessitant une analyse approfondie des pages de documents Hugging Face et du contenu arXiv. 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.

Pourquoi utiliser cette compétence

Permet aux agents de récupérer des métadonnées structurées telles que les auteurs, les modèles liés, les ensembles de données et les espaces en utilisant l'API de documents Hugging Face, et d'analyser les documents de recherche en IA à partir d'URL ou d'ID arXiv, en fournissant des résumés et des explications au format markdown.

Meilleur pour

Parfait pour les agents de recherche en IA nécessitant une analyse approfondie des pages de documents Hugging Face et du contenu arXiv.

Cas d'utilisation exploitables for hugging-face-paper-pages

Analyser les pages de documents Hugging Face pour obtenir des informations de recherche
Récupérer des métadonnées à partir d'URL ou d'ID arXiv pour les documents de recherche en IA
Résumer et expliquer les documents de recherche en IA en utilisant du contenu markdown

! Sécurité et Limitations

  • Nécessite une URL de page de document Hugging Face, une URL arXiv ou un ID
  • Limité aux documents de recherche en IA et au contenu de sciences informatiques

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Curated Collection Review

Reviewed In Curated Collections

This section shows how Killer-Skills has already collected, reviewed, and maintained this skill inside first-party curated paths. For operators and crawlers alike, this is a stronger signal than treating the upstream README as the primary story.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Labs Demo

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FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is hugging-face-paper-pages?

Parfait pour les agents de recherche en IA nécessitant une analyse approfondie des pages de documents Hugging Face et du contenu arXiv. 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.

How do I install hugging-face-paper-pages?

Run the command: npx killer-skills add huggingface/skills/hugging-face-paper-pages. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for hugging-face-paper-pages?

Key use cases include: Analyser les pages de documents Hugging Face pour obtenir des informations de recherche, Récupérer des métadonnées à partir d'URL ou d'ID arXiv pour les documents de recherche en IA, Résumer et expliquer les documents de recherche en IA en utilisant du contenu markdown.

Which IDEs are compatible with hugging-face-paper-pages?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for hugging-face-paper-pages?

Nécessite une URL de page de document Hugging Face, une URL arXiv ou un ID. Limité aux documents de recherche en IA et au contenu de sciences informatiques.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add huggingface/skills/hugging-face-paper-pages. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use hugging-face-paper-pages immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

hugging-face-paper-pages

Install hugging-face-paper-pages, an AI agent skill for AI agent workflows and automation. Review the use cases, limitations, and setup path before rollout.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

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:

InputPaper ID
https://huggingface.co/papers/2602.080252602.08025
https://huggingface.co/papers/2602.08025.md2602.08025
https://arxiv.org/abs/2602.080252602.08025
https://arxiv.org/pdf/2602.080252602.08025
2602.08025v12602.08025v1
2602.080252602.08025

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
1curl -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:

bash
1curl -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
1curl -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
1curl https://huggingface.co/api/models?filter=arxiv:{PAPER_ID}

To find datasets linked to the paper, use:

bash
1curl https://huggingface.co/api/datasets?filter=arxiv:{PAPER_ID}

To find spaces linked to the paper, use:

bash
1curl https://huggingface.co/api/spaces?filter=arxiv:{PAPER_ID}

Claim paper authorship

Claim authorship of a paper for a Hugging Face user:

bash
1curl "https://huggingface.co/api/settings/papers/claim" \ 2 --request POST \ 3 --header "Content-Type: application/json" \ 4 --header "Authorization: Bearer $HF_TOKEN" \ 5 --data '{ 6 "paperId": "{PAPER_ID}", 7 "claimAuthorId": "{AUTHOR_ENTRY_ID}", 8 "targetUserId": "{USER_ID}" 9 }'
  • 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
1curl -s -H "Authorization: Bearer $HF_TOKEN" \ 2 "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
1curl -s -H "Authorization: Bearer $HF_TOKEN" \ 2 "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
1curl -s -H "Authorization: Bearer $HF_TOKEN" \ 2 "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
1curl "https://huggingface.co/api/papers/index" \ 2 --request POST \ 3 --header "Content-Type: application/json" \ 4 --header "Authorization: Bearer $HF_TOKEN" \ 5 --data '{ 6 "arxivId": "{ARXIV_ID}" 7 }'
  • Endpoint: POST /api/papers/index
  • Body:
    • arxivId (string, required): arXiv ID to index, for example 2301.00001
  • Pattern: ^\d{4}\.\d{4,5}$
  • Response: empty JSON object on success

Update 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
1curl "https://huggingface.co/api/papers/{PAPER_OBJECT_ID}/links" \ 2 --request POST \ 3 --header "Content-Type: application/json" \ 4 --header "Authorization: Bearer $HF_TOKEN" \ 5 --data '{ 6 "projectPage": "https://example.com", 7 "githubRepo": "https://github.com/org/repo", 8 "organizationId": "{ORGANIZATION_ID}" 9 }'
  • 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.

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