What does this skill do, and when should you use it?
This skill looks up and reads Hugging Face paper pages and their corresponding arXiv papers. It can fetch paper content as Markdown and use the Hugging Face papers API to retrieve structured information such as authors, summaries, linked models, datasets, Spaces, GitHub repositories, and project pages. It is intended for requests to summarize, explain, or analyze AI research papers. Public reads require no authentication, while authorship claims, indexing, and link updates require HF_TOKEN.
Parses a paper ID from a Hugging Face paper URL, Markdown paper URL, arXiv URL, or arXiv ID; requests paper Markdown with curl; retrieves structured metadata from /api/papers/{PAPER_ID}; searches for linked models, datasets, and Spaces; calls paper search, listing, and Daily Papers endpoints; and, with HF_TOKEN and the required permissions, claims authorship, indexes papers, or updates project-page, GitHub, and organization links.
- A researcher shares a Hugging Face paper page and needs machine-readable Markdown for an agent to read.
- An engineer provides an arXiv ID and wants the paper's authors, abstract, and linked Hugging Face resources.
- A reviewer searches papers by topic, title, authors, or full-text content.
- A project maintainer checks which models, datasets, Spaces, GitHub repositories, or project pages are linked to a paper.
- A paper author claims authorship or updates paper links with HF_TOKEN and the necessary permissions.
How do you install this skill?
- Write endpoints can modify paper indexing, authorship claims, or links; confirm user intent separately and restrict the scope and exposure of HF_TOKEN.
- Core functionality depends on huggingface.co, arxiv.org, and network access; mainland-China reachability and offline alternatives are not documented.
- Rate limits, authentication failures, network interruptions, and API evolution are not covered; manually verify responses in actual use.
- Shell / CLI
- Network access
Copy or symlink skills/huggingface-papers/ from the repository's skills/ directory into a Codex-supported .agents/skills location, such as $REPO_ROOT/.agents/skills or $HOME/.agents/skills. The README does not provide a separate installation command for this individual skill.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/huggingface/skills.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/skills/huggingface-papers" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- Use the Hugging Face papers skill to summarize https://huggingface.co/papers/2602.08025
- Analyze arXiv paper 2602.08025.
After installation, use a prompt such as: “Use the Hugging Face papers skill to summarize https://huggingface.co/papers/2602.08025” or “Analyze arXiv paper 2602.08025.” Public paper-page reads require no authentication. Authorship claims, indexing, and link updates require HF_TOKEN and the relevant permissions.
What are this skill's strengths and limitations?
- Accepts both Hugging Face paper pages and arXiv URLs or IDs.
- Offers both Markdown content and structured metadata access.
- Can discover linked models, datasets, Spaces, GitHub repositories, and project pages.
- Covers paper search, listing, Daily Papers, and selected paper-management operations.
- Requires network access to Hugging Face and related arXiv pages or APIs.
- Requests may return 404 when a paper has not yet been indexed on Hugging Face.
- Write operations require HF_TOKEN, specific permissions, and accurate paper or user identifiers.
- The source does not document a test suite, rate limits, or required runtime versions.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Hugging Face Paper Research Assistant this page | 49 · Use with care | ★ 11k | 18d ago | Apache-2.0 |
| HF Paper Publisher | 45 · Not recommended | ★ 38k | 14d ago | MIT |
| NeMo AutoModel Model Onboarding ✓ NVIDIA · Official | 60 · Recommended | ★ 3.6k | 12d ago | Apache-2.0 |
| Hugging Face ZeroGPU Builder | 56 · Use with care | ★ 11k | 18d ago | Apache-2.0 |
| Hugging Face CLI Skill | 55 · Use with care | ★ 38k | 14d ago | MIT |
The documented fallbacks are the regular Hugging Face paper page and the arXiv abstract page or PDF. The source does not identify other comparable skills or products.
How did FollowSkills review this skill?
The skill mainly performs public-page and API lookups, so its stated permission scope is relatively limited. However, it also documents write operations for indexing papers, claiming authorship, and updating links, requires HF_TOKEN, and lacks explicit user confirmation, sensitive-data handling, data-flow disclosure, or rollback guidance; points are deducted for these gaps.
Input formats, primary API paths, 404 handling, and fallbacks are reasonably clear. However, there are no helper scripts, committed tests, or static reproducibility evidence, and failure feedback for authentication errors, network failures, rate limits, and API changes is thin; points are deducted accordingly.
Trigger conditions, arXiv ID parsing, and intended paper-analysis use cases are clear. Non-fit boundaries, output-format expectations, semantic false-trigger protections, Chinese-language support, and mainland-China network reachability are not addressed; points are deducted for those uncertainties.
The documentation is readable and includes installation context, use cases, API parameters, error handling, and fallbacks. Repository context supplies Apache-2.0 licensing, CI validation, and a maintenance workflow, but the skill lacks its own version, changelog, explicit maintenance owner, dependency notes, and fuller examples; points are deducted for these omissions.
The documented Markdown and structured-metadata APIs plausibly support lookup, reading, and linked-resource analysis, with outputs that could be directly used. Static review provides no representative executed outputs, and API availability, content completeness, and comparative benefit are not demonstrated; points are deducted for that uncertainty.
Concrete endpoints, parameters, error codes, and authorization requirements provide limited auditability. There is no third-party execution evidence, key-path test coverage, or cross-source corroboration, and static review cannot verify current endpoint behavior; points are deducted accordingly.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →