Turn any AI coding assistant into a computational biologist's research partner

📖 Full documentation —— quick start, skill catalogue, methodology references, and troubleshooting

A curated collection of Agent Skills for computational biology. Every skill is a standardized SKILL.md package that gives Claude Code, Cursor, Codex, and any other host supporting the open Agent Skills standard domain expertise, reproducible workflows, and validation checks — instead of another generalist that merely knows how to call an API.

Coverage spans transcriptomics, single-cell analysis, protein structure, molecular dynamics, protein docking, inter-chain contact analysis with PyMOL visualization, antimicrobial peptide design, ML peptide generation, literature mining, NCBI BioProject metadata, and publication-grade figures.

Refactored from a private archive of 217 real research scripts into a discoverable, triggerable, reproducible skill library that follows an open standard. The per-script mapping — what was ported, what was rewritten, and why — is documented in the migration map.

Most AI assistants can write scanpy code. They do not know:

• how much mitochondrial gene percentage to drop from your single-cell data before the filter starts eating real cells
• why DESeq2's padj suddenly came back all NA — almost always a mis-specified design
• which flexible loop the first spike in an RMSF curve corresponds to, and what experiment to run on it
• which of 3,000 retrieved papers are genuinely target discovery rather than functional validation
• where to truncate the axes of a reviewer-requested ROC curve so it does not invite criticism

Those are the things this project carries. Each skill ships a references/methodology.md recording the scientific reasoning behind parameter choices, a references/troubleshooting.md recording root causes for common failures, and a Validation section in SKILL.md describing how to check whether your results are right.

git clone https://github.com/ssinder/bioinformatics-agent-skills.git
cp -r bioinformatics-agent-skills/skills/* ~/.claude/skills/

Restart Claude Code and the skills are discovered automatically. Ask it to "run a bulk RNA-seq differential expression analysis" and it will load the bulk-rnaseq skill on its own.

pip install -e .
biopipe --list                      # list every skill
biopipe bulk-rnaseq --help         # usage for one skill

export PYTHONPATH="$PWD/packages"

# peptide physicochemical properties
python skills/peptide-design/scripts/analyze_peptides.py \
    --input peptides.fasta --output results/peptide_stats.csv

# ROC curve
python skills/scientific-figures/scripts/make_figures.py roc \
    --input metrics.csv --output figures/roc.png

# literature search
python skills/literature-search/scripts/search_literature.py \
    --topic "antimicrobial peptide delivery" --source pubmed --max-results 50

Self-contained — core algorithms ship as pure Python implementations. No scanpy installed? The single-cell workflow still runs. No OpenMM? DCD trajectories still parse, and RMSD/PCA still compute. Dependencies are optional; capability is not.

Reproducible — fixed random seeds, recorded parameters, pinned versions, and run metadata written alongside the results.

Verifiable — every skill defines its own quality-control stage, so there is an explicit way to check whether the output is correct.

No hardcoded paths — all paths are parameterized and all output follows a standard directory convention. Not a single /Users/xxx/... is baked into the code.

Zero setup — pip install -e . is enough. No cloud services, API keys, or databases to configure.

bioinformatics-agent-skills/
├── skills/                    # 17 independently installable skills
│   ├── bulk-rnaseq/
│   │   ├── SKILL.md           # trigger description + workflow + validation
│   │   ├── scripts/           # executable scripts
│   │   └── references/        # methodology + troubleshooting guide
│   └── ...
├── packages/
│   ├── biopipe_core/          # shared layer: config/logging/HTTP retry/CLI/IO
│   └── biopipe/               # unified CLI entry point and skill registry
├── scripts/
│   ├── validate_skills.py     # skill spec validator (CI core)
│   └── init_skill.py          # skill scaffolding generator
├── tests/                     # unit tests and validator self-tests
├── docs/                      # usage guides and skill catalogue
└── .github/workflows/         # 7 CI pipelines

git clone https://github.com/ssinder/bioinformatics-agent-skills.git
cd bioinformatics-agent-skills
pip install -e ".[dev]"

python -m pytest tests -v              # run the test suite
python scripts/validate_skills.py      # validate every skill
python scripts/validate_skills.py --strict   # treat warnings as failures

See CONTRIBUTING.md before adding a new skill.

If you use this project in your research, please cite:

@software{bioinfoagent2026,
  title  = {BioinfoAgent Skills: Agent Skills for Computational Biology},
  author = {sinder},
  year   = {2026},
  url    = {https://github.com/ssinder/bioinformatics-agent-skills}
}

See CITATION.cff for details.

Contributions of new skills, bug fixes, and documentation improvements are welcome. See CONTRIBUTING.md. Read docs/SKILL_SPEC.md before submitting a new skill.

Participation in this project implies agreement with the Code of Conduct.

MIT © sinder. See LICENSE.

This project provides analysis and computation tooling. Its outputs require independent verification by the researcher and do not constitute medical, clinical, or regulatory advice.