Keeping up with ML research means refreshing Hugging Face Papers daily, scanning dozens of titles, clicking into each one for the abstract, and manually cross-referencing GitHub repos. You want a conversational way to discover, triage, and deep-read trending papers without leaving your workspace.
This workflow combines two skills to create a full research discovery pipeline:
- Browse today's trending papers on Hugging Face — sorted by upvotes or date
- Search papers by keyword to find relevant work on any topic
- Get full paper metadata: abstract, authors, GitHub repos, community upvotes, AI-generated summaries
- Read community discussion and comments on any paper
- Deep-read the full LaTeX source of any paper via its arXiv ID (using arxiv-source)
Skills you Need
- hf-papers skill (4 tools:
hf_daily_papers,hf_search_papers,hf_paper_detail,hf_paper_comments) - arxiv-source skill (3 tools:
arxiv_fetch,arxiv_sections,arxiv_abstract) — for full paper text
No Docker or authentication required — both skills use public APIs with local caching.
How to Set it Up
- 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?"