Claude Scientific Skills

A comprehensive, open-source collection of 170+ ready-to-use scientific skills by K-Dense AI that transforms Claude Code, Cursor, and other AI agents into an AI Scientist with access to cutting-edge research tools.

170+ skills covering biology, chemistry, medicine, physics, and engineering. Access to 250+ scientific databases, 60+ optimized Python packages, and 15+ lab platform integrations — all in one collection.

What's Included

170+ Skills Across Science

Scientific Databases — 250+ databases including PubMed, UniProt, AlphaFold, PubChem, ChEMBL, DrugBank, COSMIC, ClinVar, GTEx
Python Package Integrations — 60+ packages: RDKit, Scanpy, PyTorch Lightning, scikit-learn, BioPython, PyMOL, DeepChem, Open Babel, ASE, Pymatgen
Lab Platform Integrations — 15+ platforms: Benchling, DNAnexus, Opentrons, LatchBio, LabArchives, OMERO
Analysis & Communication Tools — 35+ tools for visualization, scientific writing, and reporting

Installation

BASH

git clone https://github.com/K-Dense-AI/claude-scientific-skills
cp -r claude-scientific-skills/skills ~/.claude/skills/

Also works with Cursor (~/.cursor/skills/), Codex, and Gemini CLI.

Requirements: Python 3.9+, uv package manager, macOS/Linux/Windows (WSL2)

Core Domains

Drug Discovery Compound screening, molecular docking, ADMET prediction, target identification, binding affinity prediction

Genomics & Bioinformatics Sequence analysis, NGS data processing, genome annotation, variant calling, single-cell RNA analysis

Proteomics Protein structure prediction, interaction networks, expression analysis, AlphaFold integration

Multi-Omics & Systems Biology Biomarker discovery, pathway analysis, network analysis, integrated omics workflows

Materials Science Crystal structure analysis, property prediction, DFT calculations, materials simulation

Clinical Research Clinical variant interpretation, disease gene analysis, patient data processing, biomarker studies

Scientific Databases (250+)

The skills provide access to major scientific databases:

Genomics: NCBI, Ensembl, GTEx, ClinVar, COSMIC
Proteins: UniProt, PDB, AlphaFold Database
Chemistry: PubChem, ChEMBL, DrugBank, ZINC
Literature: PubMed, bioRxiv, ChemRxiv
Clinical: ClinicalTrials.gov, OMIM, PharmGKB

Python Package Integrations (60+)

PYTHON

# Bioinformatics
BioPython, Scanpy, PyDESeq2, STAR, HISAT2

# Cheminformatics
RDKit, DeepChem, Open Babel, AutoDock Vina

# Machine Learning
PyTorch Lightning, scikit-learn, XGBoost, JAX

# Structural Biology
PyMOL, MDAnalysis, OpenMM, GROMACS

# Materials
ASE, Pymatgen, VASP, Quantum ESPRESSO

Analysis & Communication Tools

Visualization

Publication-quality plots with matplotlib/seaborn
Interactive molecular viewers
Pathway and network diagrams

Scientific Writing

Hypothesis generation frameworks
Peer review checklists
Methods section templates
Statistical reporting standards

Data Analysis

Exploratory data analysis pipelines
Statistical hypothesis testing
Reproducibility and validation workflows

Who It's For

Computational biologists and bioinformaticians
Medicinal chemists and drug discovery researchers
Academic researchers across life sciences
Data scientists working in scientific domains
Materials scientists and chemical engineers
Anyone building AI-powered scientific tools

Community & Support

MIT licensed — free for research and commercial use
Active Slack community for questions and collaboration
Commercial support available through K-Dense AI
Contributions welcome via GitHub

Created by K-Dense AI, building autonomous AI scientists for scientific research. Contact: orion.li@k-dense.ai