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, …
---
name: hugging-science
description: 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`, and methodology citations.
metadata: {"version": "1.1", "skill-author": "K-Dense Inc."}
---
# 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 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.
- **The `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.
## 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.)… load the full skill through Skill Me