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Use when the user is doing AI/ML work in a scientific domain such as biology, chemistry, physics, astronomy, climate, genomics, materials, medicine, ecology, energy, engineering, math, drug discovery, protein design, weather modeling, theorem proving, single-cell, or PDE solving. Hugging Science is a curated catalog of scientific datasets, models, blog posts, and interactive Spaces. This skill helps discover and use resources via `datasets`, `transformers`, the HF Inference API, `gradio_client`,
Passed 15 of 19 test cases with the skill, and 7 of 19 without it.
and 8 more in the full report
9 fixed9 passed either way5 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.
What is the lightweight, compact markdown index URL on the Hugging Science website used for quick LLM context injection without downloading all full descriptions?
| Checked | Without skill | With skill |
|---|---|---|
| The URL specified is https://huggingscience.co/llms.txt. | Missed | Passed |
The instructions your model reads when the skill loads.
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:
huggingscience.co — a static, parseable index of resources across 17 scientific domains. It exposes llms.txt (compact), llms-full.txt (full content), and topics/<slug>.md (per-domain). These are markdown files designed to be fetched and read.hugging-science Hugging 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.
Engage this skill when the user's task involves AI/ML applied to science. Common signals:
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.
Most invocations follow this five-step loop. Don't skip discovery — the value of Hugging Science is that it has already filtered hundreds of resources down to high-signal picks per domain.
Map the user's task to one or more of the 17 topic slugs:
astronomy · benchmark · biology · biotechnology · chemistry · climate · conservation · earth-science · ecology · energy · engineering · genomics · materials-science · mathematics · medicine · physics · scientific-reasoning
Some tasks span multiple topics (e.g., drug discovery → chemistry + biology + medicine). Fetch each relevant topic.
Use the bundled script for clean, structured access:
test cases passed, vs 7 / 19 without
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