Discover and Deep-Read Trending Hugging Face Papers with OpenClaw
Set up OpenClaw with two skills so you can discover, triage, and deep-read trending Hugging Face Papers conversationally without leaving your workspace.
Requirements
- The hf-papers skill, which provides four tools: hf_daily_papers, hf_search_papers, hf_paper_detail, and hf_paper_comments.
- The arxiv-source skill, which provides three tools: arxiv_fetch, arxiv_sections, and arxiv_abstract; it is used for full paper text.
- No Docker or authentication is required, since both skills use public APIs with local caching.
How to use
- Install both skills:
clawhub install hf-papers
clawhub install arxiv-source
- Prompt OpenClaw with your research workflow:
I want to stay on top of ML research. Here's my daily workflow:
1. Every morning, show me the top 10 trending papers on Hugging Face (sorted by upvotes)
- For each paper, show: title, upvotes, GitHub repo (if any), and 1-line AI summary
2. When I say "search [topic]":
- Search HF Papers and show the most relevant results
- Highlight papers with linked GitHub repos or high upvote counts
3. When I pick a paper (by ID):
- Show the full abstract, authors, and linked resources
- Show community comments if any
- Ask if I want a deep read
4. For deep reads:
- Fetch the full paper via arxiv-source
- Summarize key contributions, methodology, and results
- Note any linked code repos I should check out
Keep a running list of papers I've reviewed today with one-line takeaways.
- Try it: “What's trending on Hugging Face Papers today?”
Source: hesamsheikh/awesome-openclaw-usecases and its contributors. License: MIT.
Copyright (c) 2026
Translation and editorial adaptation by ClawCases.
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