Hugging Science
Skill Verifiziert AktivUse when the user is doing AI/ML work in a scientific domain — biology, chemistry, physics, astronomy, climate, genomics, materials science, medicine, ecology, energy, conservation, engineering, mathematics, scientific reasoning, drug discovery, protein design, weather modeling, theorem proving, single-cell, PDE solving, or anything similar. Hugging Science (huggingscience.co) is a curated catalog of scientific datasets, models, blog posts, and interactive Spaces; the `hugging-science` org on Hugging Face hosts community datasets, models, and demo Spaces. This skill helps you discover the right resource AND actually use it — loading datasets via `datasets`, running models via `transformers` or the HF Inference API, calling Spaces like BoltzGen via `gradio_client`, and citing blog posts for methodology. Trigger this skill whenever a user mentions a scientific ML task, asks for "a dataset/model for X" where X is a scientific topic, wants to fine-tune on scientific data, asks about protein / molecule / genome / climate / materials / astronomy / pathology / weather ML, or needs AI tools for research — even if they never say "Hugging Science" explicitly. The catalog is purpose-built for LLM agents (it ships an `llms-full.txt`); prefer it over generic web search for these tasks.
To provide AI agents with curated, high-signal access to scientific ML resources, facilitating research and development across diverse scientific domains.
Funktionen
- Discover scientific datasets, models, and interactive spaces
- Fetch and parse catalog content programmatically
- Load datasets using the `datasets` library
- Run models locally or via Hugging Face APIs
- Interact with Hugging Face Spaces via `gradio_client`
Anwendungsfälle
- Finding a dataset or model for a specific scientific ML task (e.g., protein folding, climate modeling)
- Identifying appropriate tools for fine-tuning on scientific data
- Discovering interactive demos for scientific research problems
- Reproducing scientific ML papers by finding relevant resources
Nicht-Ziele
- Performing generic ML tasks unrelated to science
- Replacing direct Hugging Face Hub search when a resource is not listed
- Acting as an inference endpoint itself (it points to external resources)
Execution
- info:Pinned dependenciesThe project documentation mentions using `uv` and installing dependencies, but explicit pinning via lockfiles or interpreter declaration in scripts is not detailed in the provided context.
Installation
npx skills add K-Dense-AI/claude-scientific-skillsFührt das Vercel skills CLI (skills.sh) via npx aus — benötigt Node.js lokal und mindestens einen installierten skills-kompatiblen Agent (Claude Code, Cursor, Codex, …). Setzt voraus, dass das Repo dem agentskills.io-Format folgt.
Qualitätspunktzahl
VerifiziertVertrauenssignale
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