Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/waybarrios/opencode-power-pack/huggingface-papersnpx skills add waybarrios/opencode-power-pack --skill huggingface-papersgit clone --depth 1 https://github.com/waybarrios/opencode-power-packWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/waybarrios/opencode-power-pack/huggingface-papers)<a href="https://agentmods.dev/skills/waybarrios/opencode-power-pack/huggingface-papers"><img src="https://agentmods.dev/badge/skills/waybarrios/opencode-power-pack/huggingface-papers.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00078 | $0.02530 |
| Opus 5 | $0.00039 | $0.01265 |
| Sonnet 5 | $0.00016 | $0.00506 |
| Haiku 4.5 | $0.00008 | $0.00253 |
Grade A, and why
huggingface-papers scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s "https://huggingface.co/papers/{PAPER_ID}.md" This is a copy
98% identical to huggingface-papers — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 240 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hugging Face Paper Pages
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:
- claim their paper (by clicking their name on the
authorsfield). This makes the paper page appear on their Hugging Face profile. - link the associated model checkpoints, datasets and Spaces by including the HF paper or arXiv URL in the model card, dataset card or README of the Space
- link the Github repository and/or project page URLs
- link the HF organization. This also makes the paper page appear on the Hugging Face organization page.
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.
When to Use
- User shares a Hugging Face paper page URL (e.g.
https://huggingface.co/papers/2602.08025) - User shares a Hugging Face markdown paper page URL (e.g.
https://huggingface.co/papers/2602.08025.md) - User shares an arXiv URL (e.g.
https://arxiv.org/abs/2602.08025orhttps://arxiv.org/pdf/2602.08025) - User mentions a arXiv ID (e.g.
2602.08025) - User asks you to summarize, explain, or analyze an AI research paper
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 240 lines · 78 tokens per session scan A c7ae7993f1fc
huggingface-papers is a skill published in the GitHub repository waybarrios/opencode-power-pack (490 stars, last pushed 2d ago), licensed MIT. It adds 78 tokens to every session and 2,530 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 98% identical to huggingface-papers, differing in 3 lines, and is treated as a copy.
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