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 skills add crazymsn/academic-skills --skill hugging-sciencegit clone --depth 1 https://github.com/crazymsn/academic-skillsWrote 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/crazymsn/academic-skills/hugging-science)<a href="https://agentmods.dev/skills/crazymsn/academic-skills/hugging-science"><img src="https://agentmods.dev/badge/skills/crazymsn/academic-skills/hugging-science/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/crazymsn/academic-skills/hugging-science"><img src="https://agentmods.dev/badge/skills/crazymsn/academic-skills/hugging-science.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00135 | $0.02276 |
| Opus 5.5 | $0.00054 | $0.00910 |
| Sonnet 5.5 | $0.00027 | $0.00455 |
| Haiku 4.5 | $0.00014 | $0.00228 |
Grade A, and why
hugging-science scanned grade A with 0 findings 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 1mo 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
91% identical to hugging-science — 65 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hugging Science
Hugging Science is a curated, LLM-friendly index of scientific datasets, models, blog posts, and interactive demos for ML researchers. Use it when a scientific ML question lands in front of you — it's much higher signal than generic search and the entries are pre-filtered for quality and openness.
There are two related surfaces, and you should use both:
- The catalog at
huggingscience.co— a static, parseable index of resources across 17 scientific domains. It exposesllms.txt(compact),llms-full.txt(full content), andtopics/<slug>.md(per-domain). These are markdown files designed to be fetched and read. - The
hugging-scienceHugging Face organization —huggingface.co/hugging-science— community-submitted datasets, a few models, and ~27 interactive Spaces (notably BoltzGen for protein/binder design, Dataset Quest for submissions, and Science Release Heatmap for ecosystem visualization).
The catalog points to resources hosted on the broader Hugging Face Hub. So an entry like arcinstitute/opengenome2 is a regular HF dataset that you load with the datasets library; an entry like facebook/esm2_t33_650M_UR50D is a regular HF model you load with transformers. The catalog's job is curation and discovery; usage goes through standard Hugging Face APIs.
When to use this skill
Engage this skill when the user's task involves AI/ML applied to science. Common signals:
- Names a scientific domain (protein, genome, molecule, crystal, weather, climate, galaxy, EEG, microbiome, pathology, plasma, …)
- Asks "is there a dataset/model for X" where X is scientific
- Wants to fine-tune on scientific data, evaluate on scientific benchmarks, or reproduce a scientific ML paper
- Asks about specific known scientific models (Evo-2, ESM2, BoltzGen, Nucleotide Transformer, AlphaFold-derived, etc.)
- Needs an interactive demo for a scientific task (binder design, theorem proving, etc.)
If the task is generic ML (recommendation systems, chatbot RAG, vision on cats and dogs), this skill is not the right tool — defer to general HF Hub knowledge instead.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 130 lines · 135 tokens per session scan A 856c948b310b
hugging-science is a skill published in the GitHub repository crazymsn/academic-skills (23 stars, last pushed 5mo ago), licensed MIT. It adds 135 tokens to every session and 2,276 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 91% identical to hugging-science, differing in 65 lines, and is treated as a copy.
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