hugging-face-papers

Read and analyze Hugging Face paper pages or arXiv papers with markdown and papers API metadata.

31,392 stars
Complexity: medium

About this skill

This skill empowers an AI agent to programmatically access and process research papers hosted on Hugging Face Papers (hf.co/papers) or directly from arXiv (arxiv.org). It allows the agent to extract and analyze paper content, including its markdown representation, and leverage associated API metadata. This facilitates tasks such as understanding paper abstracts, methodologies, results, and author information, making it easier for the agent to synthesize information from the latest AI research. It bridges the gap between raw research papers and an agent's ability to comprehend and utilize their content.

Best use case

Automated literature reviews, summarization of recent AI advancements, extracting key findings from academic papers, answering specific questions about research methodologies, tracking daily paper submissions on Hugging Face, and assisting with academic writing by providing contextual paper data.

Read and analyze Hugging Face paper pages or arXiv papers with markdown and papers API metadata.

The agent will return analyzed content, concise summaries, key findings, or specific metadata extracted from the requested research papers. This could manifest as a summary of a paper's core contribution, answers to targeted questions about its content, or a curated list of relevant papers with their titles and abstracts.

Practical example

Example input

Summarize the methodology and main results of the latest paper on 'transformer architectures for multimodal learning' from Hugging Face Papers.

Example output

Based on the paper titled ' [Paper Title] ' by [Author Name] from Hugging Face Papers, the core methodology involves [brief description of methodology, e.g., 'a novel multi-head attention mechanism combining visual and textual embeddings']. Key results demonstrate [brief description of results, e.g., 'a 15% improvement in cross-modal retrieval tasks compared to previous state-of-the-art models'] and suggest [implications or future work].

When to use this skill

  • When an AI agent needs to gain a deep understanding of a specific AI research paper, when conducting a comprehensive literature review on a given topic, when the agent needs to extract structured data (e.g., authors, abstract, publication date) from a paper, or when staying updated on daily AI paper submissions and community discussions.

When not to use this skill

  • When the required information is not in a research paper format (e.g., blog posts, news articles, general web pages), when only a high-level overview without detailed textual analysis is sufficient (a simple search engine or general web browsing tool might be more efficient), or when the agent lacks the inherent contextual understanding to interpret highly specialized scientific language effectively.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/hugging-face-papers/SKILL.md --create-dirs "https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/plugins/antigravity-awesome-skills-claude/skills/hugging-face-papers/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/hugging-face-papers/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How hugging-face-papers Compares

Feature / Agenthugging-face-papersStandard Approach
Platform SupportClaudeLimited / Varies
Context Awareness High Baseline
Installation ComplexitymediumN/A

Frequently Asked Questions

What does this skill do?

Read and analyze Hugging Face paper pages or arXiv papers with markdown and papers API metadata.

Which AI agents support this skill?

This skill is designed for Claude.

How difficult is it to install?

The installation complexity is rated as medium. You can find the installation instructions above.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# 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.

Related Skills

keyword-extractor

31392
from sickn33/antigravity-awesome-skills

Extracts up to 50 highly relevant SEO keywords from text. Use when user wants to generate or extract keywords for given text.

Text AnalysisClaude

flutter-expert

31392
from sickn33/antigravity-awesome-skills

Master Flutter development with Dart 3, advanced widgets, and multi-platform deployment.

Text AnalysisClaude

docs-architect

31392
from sickn33/antigravity-awesome-skills

Creates comprehensive technical documentation from existing codebases. Analyzes architecture, design patterns, and implementation details to produce long-form technical manuals and ebooks.

Text AnalysisClaude

data-storytelling

31392
from sickn33/antigravity-awesome-skills

Transform raw data into compelling narratives that drive decisions and inspire action.

Text AnalysisClaude

data-engineering-data-pipeline

31392
from sickn33/antigravity-awesome-skills

You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.

Text AnalysisClaude

behavioral-modes

31392
from sickn33/antigravity-awesome-skills

AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.

Text AnalysisClaude

azure-search-documents-py

31392
from sickn33/antigravity-awesome-skills

Azure AI Search SDK for Python. Use for vector search, hybrid search, semantic ranking, indexing, and skillsets.

Text AnalysisClaude

azure-ai-textanalytics-py

31392
from sickn33/antigravity-awesome-skills

Azure AI Text Analytics SDK for sentiment analysis, entity recognition, key phrases, language detection, PII, and healthcare NLP. Use for natural language processing on text.

Text AnalysisClaude

hugging-face-vision-trainer

31392
from sickn33/antigravity-awesome-skills

Train or fine-tune vision models on Hugging Face Jobs for detection, classification, and SAM or SAM2 segmentation.

Computer VisionClaude

hugging-face-trackio

31392
from sickn33/antigravity-awesome-skills

Track ML experiments with Trackio using Python logging, alerts, and CLI metric retrieval.

Machine LearningClaude

hugging-face-tool-builder

31392
from sickn33/antigravity-awesome-skills

Your purpose is now is to create reusable command line scripts and utilities for using the Hugging Face API, allowing chaining, piping and intermediate processing where helpful. You can access the API directly, as well as use the hf command line tool.

Developer ToolsClaude

hugging-face-paper-publisher

31392
from sickn33/antigravity-awesome-skills

Publish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles.

AI Research PublishingClaude