Loading skill
Install any skill in seconds. Free to start, no credit card required.
Get Started Free →waybarrios/huggingface-papers
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.
Passed 20 of 21 test cases with the skill, and 11 of 21 without it.
and 14 more in the full report
10 fixed11 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
If a user supplies 'https://huggingface.co/papers/2311.00123.md', what clean paper ID should be extracted and used when querying linked datasets on the hub?
| Checked | Without skill | With skill |
|---|---|---|
| Identifies the paper ID as 2311.00123 | Passed | Passed |
| Uses filter=arxiv:2311.00123 when filtering datasets | Missed | Passed |
The instructions your model reads when the skill loads.
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:
test cases passed, vs 11 / 21 without
Published skills whose descriptions closely match this one, with their headline benchmark lift.
More skills from the same source and nearby in the registry, with their headline benchmark lift.