huggingface-papers

Overview of huggingface-papers skill

What huggingface-papers does

The huggingface-papers skill helps you read Hugging Face paper pages and pull structured metadata from the papers API, including authors, linked models, datasets, Spaces, GitHub repos, and project pages. It is useful when you have a Hugging Face paper page URL, an arXiv URL or ID, or you want a concise explanation or analysis of an AI research paper.

Who should use it

This huggingface-papers skill is a good fit for people doing paper review, literature triage, research briefings, model comparison, or repo-to-paper tracing. It is especially useful for Academic Research workflows where you need the paper page plus metadata, not just a generic summary from an LLM.

Why it is different

The main advantage is that it centers Hugging Face’s paper-page context instead of treating the paper as an isolated PDF. That means you can connect the paper to its implementation assets, see linked artifacts, and use paper-page structure to reduce ambiguity before you summarize or analyze.

How to Use huggingface-papers skill

Install and locate the skill

Use the repository install flow for huggingface-papers install: npx skills add huggingface/skills --skill huggingface-papers. After install, open SKILL.md first, then inspect any linked repository guidance such as README.md, AGENTS.md, metadata.json, or relevant folders if they exist in your local copy.

Give the skill the right input

For best huggingface-papers usage, provide one clear identifier: a Hugging Face paper page URL, an arXiv URL, or an arXiv ID. If you want analysis, add the goal and constraints up front, for example:
Summarize this paper for a research lead, highlight linked models/datasets, and note any deployment caveats: <URL>

Suggested workflow

1. Resolve the paper page or arXiv ID.
2. Read the paper page markdown first, then check the structured metadata.
3. Extract the job you need: summary, critique, related assets, or author/network context.
4. If the paper is mentioned in a model card or README, verify whether it was auto-indexed or formally submitted to Daily Papers.

What to read first in the repo

Start with SKILL.md, because it defines the core workflow and when the skill should be used. Then read any inline references in that file that explain paper ID parsing, fetching the page as markdown, and the papers API endpoints; those are the parts that most affect output quality and correct invocation.

huggingface-papers skill FAQ

Is huggingface-papers only for Hugging Face pages?

No. The skill also works with arXiv URLs or IDs, then maps that input back into the Hugging Face paper-page workflow. Use it when your source of truth is arXiv but you want HF-linked metadata and a paper-page view.

When should I not use it?

Do not use huggingface-papers if you only need a broad web search summary, if the paper is not in AI/computer science, or if you already have a clean internal abstract and do not need HF metadata. It is less useful when the task is purely editorial and unrelated to paper pages or linked research assets.

Is it beginner-friendly?

Yes, if you can supply a stable paper identifier and a clear output goal. The main failure mode is vague prompting, not technical complexity. A simple request like “summarize this paper and list linked artifacts” is usually enough to start.

How does it compare with a generic prompt?

A generic prompt may summarize text, but the huggingface-papers guide gives you a more reliable workflow for finding the paper page, reading structured metadata, and checking related assets. That reduces missed links and makes academic triage more repeatable.

How to Improve huggingface-papers skill

Be explicit about the output you want

Users get better results when they specify whether they need a summary, technical explanation, paper-to-repo mapping, or Academic Research note. Add audience and depth so the model knows whether to optimize for overview, rigor, or decision support.

Provide a paper-aware brief

Strong input looks like this: Analyze this arXiv paper for a lab meeting. Focus on method, key claims, linked HF models/datasets, and any signs the paper is mainly a benchmark or application paper: <ID>. This is better than “tell me about this paper” because it tells the skill what to prioritize and what not to spend tokens on.

Watch for common failure modes

The most common issues are ambiguous paper IDs, asking for too many unrelated tasks at once, and forgetting to ask for linked assets when that is the real need. If the first output is too generic, narrow the task to one paper, one audience, and one decision.

Iterate with the paper-page evidence

Use the first pass to identify missing links, authors, or context, then ask a second pass that focuses on the gaps. For huggingface-papers, the highest-value improvement is usually not a longer summary; it is better source selection, better metadata extraction, and a more precise research question.