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Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page. Use when the user shares a Hugging Face paper page URL, an arXiv URL or ID, or asks to summarize,
$ npx -y skills add waybarrios/opencode-power-pack --skill huggingface-papers --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/huggingface-papersContext preview
The summary Claude sees to decide when to auto-load this skill.
Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page. Use when the user shares a Hugging Face paper page URL, an arXiv URL or ID, or asks to summarize,
name: huggingface-papers description: Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page. Use when the user shares a Hugging Face paper page URL, an arXiv URL or ID, or asks to summarize, explain, or analyze an AI research paper. license: Apache-2.0 (modified; see UPSTREAMS.json)
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:
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.
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}"
}'Fetch the Daily Papers feed:
curl -s -H "Authorization: Bearer $HF_TOKEN" \ "https://huggingface.co/api/daily_papers?p=0&limit=20&date=2017-07-21&sort=publishedAt"
54 rigorous skills for Codex, OpenCode, and Pi: code review, security audit, feature development, frontend design, MCP tools, Hugging Face ML/training, and more.
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