74 ready-to-use scientific skills for AI coding agents — covering genomics, proteomics, drug discovery, biostatistics, scientific computing, and scientific writing.
Each skill is a self-contained SKILL.md file with runnable code examples, key parameters, troubleshooting guides, and best practices. Designed for Claude Code, but compatible with any agent that reads markdown skill files.
| Category | Skills | Examples |
|---|---|---|
| Genomics & Bioinformatics | 16 | Scanpy, BioPython, pysam, gget, KEGG, PubMed, scvi-tools |
| Scientific Computing | 14 | Polars, Dask, NetworkX, SymPy, UMAP, PyG, Zarr, SimPy |
| Structural Biology & Drug Discovery | 13 | RDKit, AutoDock Vina, ChEMBL, PDB, DeepChem, datamol |
| Biostatistics | 9 | scikit-learn, statsmodels, PyMC, SHAP, survival analysis |
| Proteomics & Protein Engineering | 6 | ESM, UniProt, PyOpenMS, matchms, HMDB |
| Scientific Writing | 5 | Manuscript writing, peer review, LaTeX posters, slides |
| Systems Biology & Multi-omics | 4 | COBRApy, LaminDB, Reactome, STRING |
| Cell Biology | 3 | pydicom, histolab, FlowIO |
| Lab Automation | 2 | Opentrons, Benchling |
| Data Visualization | 2 | Plotly, Seaborn |
Skill types: 39 toolkits, 19 database connectors, 9 guides, 7 pipelines
- Claude Code CLI installed
- Git
- Python 3.12+ (only needed if you want to run validation scripts)
git clone https://github.com/jaechang-hits/SciAgent-Skills.git
cd SciAgent-SkillsLoad SciAgent-Skills as a Claude Code plugin for the current session:
claude --plugin-dir /path/to/SciAgent-SkillsTo verify the plugin loaded, run /plugin inside Claude Code and check that sciagent-skills appears in the Installed tab.
Skills become available as /sciagent-skills:<skill-name>:
/sciagent-skills:scanpy-scrna-seq
/sciagent-skills:rdkit-cheminformatics
/sciagent-skills:pymc-bayesian-modeling
Or just describe your task — the agent finds the relevant skill automatically:
"Perform differential expression analysis on this RNA-seq count matrix"
Persistent installation — to load the plugin automatically in every session, use the plugin install command inside Claude Code:
/plugin marketplace add jaechang-hits/SciAgent-Skills
/plugin install sciagent-skills
Clone into your project directory so Claude Code picks up skills via CLAUDE.md:
cd your-project
git clone https://github.com/jaechang-hits/SciAgent-Skills.git .sciagent-skillsAdd to your project's CLAUDE.md:
## Scientific Skills
Reference skills in `.sciagent-skills/skills/` for domain-specific analysis.
Registry: `.sciagent-skills/registry.yaml`cd SciAgent-Skills
pixi installPixi handles the Python environment and all required packages. If you don't have pixi installed:
curl -fsSL https://pixi.sh/install.sh | bashEach skill follows a structured template:
skills/<category>/<skill-name>/
SKILL.md # Main skill file (300-550 lines)
references/ # Optional deep-dive reference files
assets/ # Optional templates, configs
A SKILL.md contains:
- Frontmatter — name, description, license (for agent discovery)
- Overview & When to Use — what the tool does and when to reach for it
- Prerequisites — packages, data, environment setup
- Quick Start — minimal copy-paste example
- Workflow / Core API — step-by-step pipeline or module-by-module API guide
- Key Parameters — tunable settings with defaults and ranges
- Common Recipes — self-contained snippets for common tasks
- Troubleshooting — problem/cause/solution table
The agent reads only the description field during planning. Full skill content is loaded on demand when relevant.
SciAgent-Skills/
├── .claude-plugin/
│ └── plugin.json # Claude Code plugin manifest
├── skills/ # All 74 skills organized by category
│ ├── genomics-bioinformatics/
│ ├── structural-biology-drug-discovery/
│ ├── biostatistics/
│ ├── scientific-computing/
│ ├── proteomics-protein-engineering/
│ ├── scientific-writing/
│ ├── systems-biology-multiomics/
│ ├── cell-biology/
│ ├── lab-automation/
│ └── data-visualization/
├── templates/ # Skill authoring templates
├── registry.yaml # Index of all skills
├── CLAUDE.md # Skill authoring guide
└── scripts/
└── validate_registry.py
Drug Discovery Pipeline
"Search ChEMBL for EGFR inhibitors with IC50 < 100nM, filter with Lipinski rules using RDKit, dock top candidates with AutoDock Vina"
Uses: chembl-database-bioactivity → rdkit-cheminformatics → autodock-vina-docking
Single-Cell RNA-seq Analysis
"Load 10X data, QC filter, normalize, cluster, find marker genes, and annotate cell types"
Uses: anndata-data-structure → scanpy-scrna-seq
Bayesian Biostatistics
"Fit a hierarchical Bayesian model to this clinical trial data with patient-level random effects"
Uses: pymc-bayesian-modeling → matplotlib-scientific-plotting
Protein Structure Analysis
"Get the AlphaFold structure for UniProt P04637, assess confidence, find high-confidence binding regions"
Uses: uniprot-protein-database → alphafold-database-access
- Read
CLAUDE.mdfor the full authoring workflow - Classify your topic (pipeline / toolkit / database / guide)
- Pick a category from the table above
- Use the appropriate template from
templates/ - Add the entry to
registry.yaml - Validate:
python scripts/validate_registry.py
| Template | Use When |
|---|---|
SKILL_TEMPLATE.md |
Linear input→process→output pipeline (e.g., DESeq2) |
SKILL_TEMPLATE_TOOLKIT.md |
Collection of independent modules (e.g., RDKit) |
SKILL_TEMPLATE_PROSE.md |
Conceptual guide, decision frameworks (e.g., statistical test selection) |
- Python 3.12+ (for validation scripts)
- No runtime dependencies — skills are markdown files read by the agent
- Individual skills list their own tool-specific prerequisites (e.g.,
pip install scanpy)
CC-BY-4.0 for original content. Individual skills note the license of their underlying tools.
This project builds on 50+ open-source scientific Python packages. If you find a skill useful, consider starring the underlying tool's repository and supporting its maintainers.