HF Papers

Browse trending papers, search by keyword, and get paper details from Hugging Face Papers

Security Scan

The skill's declared purpose (browse/search Hugging Face Papers) aligns with its instructions and it requests no unusual installs or credentials; behavior is coherent and proportional.

Purpose & Capability

Name/description match the actions documented in SKILL.md (trending, search, details, comments). No unrelated credentials, binaries, or config paths are requested.

Instruction Scope

Instructions describe calling the Hugging Face Papers public API and optionally using an external arxiv-reader skill for full text. The only local I/O mentioned is caching under ~/.cache/hf-papers/ with specified TTLs; nothing instructs reading unrelated files or secrets.

Install Mechanism

No install spec or code files are present (instruction-only). This minimizes risk because nothing is downloaded or written by the skill itself during install.

Credentials

The skill requires no environment variables, credentials, or config paths. That is appropriate for a read-only public-API browsing/searching capability.

Persistence & Privilege

always is false and the skill does not request elevated or cross-skill configuration changes. Local caching is limited to a per-user cache directory (~/.cache/hf-papers/) and TTLs are defined.

Assessment

This skill appears coherent and low-risk: it queries the public Hugging Face Papers API and caches results under ~/.cache/hf-papers/ (15-minute/1-hour TTLs). There are no install steps, no downloads, and no credentials requested. If you are concerned about local data, you can remove that cache directory after use. Note that because this is an instruction-only skill (no code files), its runtime network behavior depends on the platform implementing the described tools — if you need stronger assurance, ask the maintainer or platform for details on the actual HTTP endpoints and caching implementation before installing.

Browse, search, and analyze papers from the Hugging Face Papers platform. Get trending papers, search by topic, and retrieve detailed metadata including community engagement and linked resources.

Description

This skill wraps the Hugging Face Papers public API. It provides access to daily trending papers, keyword search, paper details (abstract, authors, upvotes, GitHub repos, project pages), and discussion comments. No authentication required.

For full paper text, use the returned arXiv ID with the arxiv-reader skill.

Results are cached locally (~/.cache/hf-papers/) for fast repeat access.

Usage Examples

• "What are today's trending papers on Hugging Face?"
• "Search Hugging Face Papers for diffusion models"
• "Get details for paper 2401.12345 on HF"
• "Show me comments on HF paper 2405.67890"

Process

1. Discover — Use hf_daily_papers to see what's trending today
2. Search — Use hf_search_papers to find papers on a topic
3. Inspect — Use hf_paper_detail to get full metadata for a specific paper
4. Discuss — Use hf_paper_comments to read community discussion
5. Deep read — Use arxiv_fetch (from arxiv-reader) with the paper's arXiv ID for full text

Tools

hf_daily_papers

Get today's trending papers from Hugging Face.

Parameters:

• limit (number, optional): Max papers to return (default: 20, max: 100)
• sort (string, optional): Sort by upvotes or date (default: upvotes)

Returns: { papers: [{ id, title, summary, upvotes, authors, publishedAt, githubRepo?, projectPage?, ai_summary?, ai_keywords? }], count: number }

Example:

{ "limit": 10, "sort": "upvotes" }

hf_search_papers

Search Hugging Face Papers by keyword.

Parameters:

• query (string, required): Search query

Returns: { papers: [{ id, title, summary, upvotes, authors, publishedAt, githubRepo?, projectPage?, ai_summary? }], query: string, count: number }

Example:

{ "query": "multimodal reasoning" }

hf_paper_detail

Get detailed metadata for a specific paper.

Parameters:

• paper_id (string, required): Paper ID (arXiv ID, e.g. 2401.12345)

Returns: { id, title, summary, authors, publishedAt, upvotes, numComments, githubRepo?, githubStars?, projectPage?, ai_summary?, ai_keywords?, organization? }

Example:

{ "paper_id": "2401.12345" }

hf_paper_comments

Get discussion comments for a paper.

Parameters:

• paper_id (string, required): Paper ID (arXiv ID)

Returns: { paper_id, comments: [{ author, content, createdAt }], count: number }

Example:

{ "paper_id": "2401.12345" }

Notes

• All results are cached locally — repeat requests are instant (15-minute TTL for daily/search, 1-hour for details)
• Paper IDs are arXiv IDs — use with arxiv-reader skill for full LaTeX text
• No authentication required; uses HF public API
• Daily papers update throughout the day as the community submits and upvotes