skills/hugging-face-papers/SKILL.md
Read and analyze Hugging Face paper pages or arXiv papers with markdown and papers API metadata.
npx skillsauth add ranbot-ai/awesome-skills hugging-face-papersInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
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:
authors field). This makes the paper page appear on their Hugging Face profile.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.
https://huggingface.co/papers/2602.08025)https://huggingface.co/papers/2602.08025.md)https://arxiv.org/abs/2602.08025 or https://arxiv.org/pdf/2602.08025)2602.08025)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.
The content of a paper can be fetched as markdown like so:
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:
Alternatively, you can request markdown from the normal paper page URL, like so:
curl -s -H "Accept: text/markdown" "https://huggingface.co/papers/{PAPER_ID}"
All endpoints use the base URL https://huggingface.co.
Fetch the paper metadata as JSON using the Hugging Face REST API:
curl -s "https://huggingface.co/api/papers/{PAPER_ID}"
This returns structured metadata that can include:
To find models linked to the paper, use:
curl https://huggingface.co/api/models?filter=arxiv:{PAPER_ID}
To find datasets linked to the paper, use:
curl https://huggingface.co/api/datasets?filter=arxiv:{PAPER_ID}
To find spaces linked to the paper, use:
curl https://huggingface.co/api/spaces?filter=arxiv:{PAPER_ID}
Claim authorship of a paper for a Hugging Face user:
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}"
}'
POST /api/settings/papers/claimpaperId (string, required): arXiv paper identifier being claimedclaimAuthorId (string): author entry on the paper being claimed, 24-char hex IDtargetUserId (string): HF user who should receive the claim, 24-char hex IDdevelopment
Production-grade Android app development guide covering native (Kotlin/Java), cross-platform (Flutter, RN, KMM), and hybrid architectures.
testing
Plan, orchestrate, and adversarially verify parallel AI coding agents with a dynamic multi-agent workflow engine.
development
Generate professional, ATS-optimized CVs for FlowCV, Canva, Google Docs, or Word. Handles multi-source merging, JD targeting, seniority adaptation, and humanized rewriting. Outputs paste-ready text wi
tools
Generate hand-drawn 16:9 article illustrations with the Grav character IP, sparse annotations, and absurd but clear visual metaphors.