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willamhou

HF Papers

by Will.hou · GitHub ↗ · v1.0.3 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install hf-papers
Description
Browse trending papers, search by keyword, and get paper details from Hugging Face Papers
README (SKILL.md)

hf-papers

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
Usage Guidance
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.
Capability Analysis
Type: OpenClaw Skill Name: hf-papers Version: 1.0.3 The skill bundle provides a standard interface for interacting with the Hugging Face Papers API to search and retrieve academic paper metadata. The tool definitions and instructions in SKILL.md are consistent with the stated purpose, and no indicators of data exfiltration, malicious execution, or prompt injection were found.
Capability Assessment
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.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install hf-papers
  3. After installation, invoke the skill by name or use /hf-papers
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.3
Remove executable code to resolve security flags
v1.0.2
- Initial skill release: browse, search, and get details for Hugging Face Papers. - Supports trending papers, keyword search, metadata lookup, and community comments. - No authentication required; results cached locally for fast repeat access. - Uses public Hugging Face Papers API; integrates with arxiv-reader for full paper text.
v1.0.1
- Removed core implementation and manifest files (`index.ts`, `manifest.json`). - The skill package now contains only documentation; no functionality is present in this version.
v1.0.0
Initial release of the hf-papers skill. - Browse trending papers, search by keyword, and get paper details from Hugging Face Papers. - Fetch metadata, upvote counts, author info, project/GitHub links, and community comments. - Results are cached locally for fast repeated access. - No authentication required; uses the public Hugging Face Papers API. - Integrates with arxiv-reader for full paper text using arXiv IDs.
Metadata
Slug hf-papers
Version 1.0.3
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 4
Frequently Asked Questions

What is HF Papers?

Browse trending papers, search by keyword, and get paper details from Hugging Face Papers. It is an AI Agent Skill for Claude Code / OpenClaw, with 374 downloads so far.

How do I install HF Papers?

Run "/install hf-papers" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is HF Papers free?

Yes, HF Papers is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does HF Papers support?

HF Papers is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created HF Papers?

It is built and maintained by Will.hou (@willamhou); the current version is v1.0.3.

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