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by ssinder
Agent Skills for computational biology — RNA-seq, single-cell, protein structure, molecular dynamics, peptide design, literature mining and publication figures. Turn any AI coding agent into a computational biology assistant.
# Add to your Claude Code skills
git clone https://github.com/ssinder/bioinformatics-agent-skillsGuides for using ai agents skills like bioinformatics-agent-skills.
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bioinformatics-agent-skills is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by ssinder. Agent Skills for computational biology — RNA-seq, single-cell, protein structure, molecular dynamics, peptide design, literature mining and publication figures. Turn any AI coding agent into a computational biology assistant. It has 55 GitHub stars.
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Clone the repository with "git clone https://github.com/ssinder/bioinformatics-agent-skills" and add it to your Claude Code skills directory (see the Installation section above).
bioinformatics-agent-skills is primarily written in Python. It is open-source under ssinder on GitHub, so you can review or fork the full source.
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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:
padj suddenly came back all NA — almost always a mis-specified designThose 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
| Skill | Description | Key capabilities |
|---|---|---|
bulk-rnaseq |
RNA-seq quantification and differential expression | FastQC QC · Salmon quantification · DESeq2 differential analysis · GO/KEGG enrichment |
ena-download |
ENA raw data download | Portal API FASTQ retrieval · resumable transfers · MD5 verification · metadata export |
single-cell-rnaseq |
Standard single-cell RNA-seq workflow | QC filtering · CP10K normalization · HVG · PCA · Leiden clustering · UMAP · cell annotation |
| Skill | Description | Key capabilities |
|---|---|---|
protein-structure |
PDB structure parsing and secondary structure | Built-in PDB parser · DSSP secondary structure · geometry computation · UniProt metadata |
protein-interactions |
Protein-ligand interactions | PLIP XML parsing · residue-level extraction · interaction networks · PyMOL annotations |
molecular-dynamics |
Molecular dynamics simulation and trajectory analysis | RMSD/RMSF/PCA · hydrogen bonds · Ramachandran · trajectory clustering · built-in DCD parser |
alphafold-db |
AlphaFold DB structure download | API link resolution · batch download · pLDDT confidence filtering · metadata export |
protein-docking-hdock |
HDOCK protein docking | HTTP job submission · Docking Score · confidence formula · model download · queue-status distinction |
protein-docking-lightdock |
LightDock local protein docking | GSO swarm optimization · DFIRE scoring parsing · best-complex assembly · resumable batch runs |
pymol-structure-visualization |
Inter-chain residue contacts and PyMOL visualization | Grid spatial partitioning · hydrogen bond/salt bridge/hydrophobic/π-π/cation-π classification · hotspot residues · Mermaid network diagrams · PyMOL script generation |
| Skill | Description | Key capabilities |
|---|---|---|
peptide-design |
Antimicrobial peptide design and screening | Physicochemical properties · hydrophobicity/pI · stability · MMseqs2 clustering for redundancy removal |
ml-peptide-generation |
ML peptide generation and screening | PepMLM masked language model · illegal residue filtering · pseudo-perplexity ranking · reproducible seeds |
| Skill | Description | Key capabilities |
|---|---|---|
bio-database-fetch |
PubChem compound and taxonomy lookup | PUG-REST batch properties · CAS number extraction · full taxonomy lineage · no browser dependency |
| Skill | Description | Key capabilities |
|---|---|---|
literature-search |
Multi-source scholarly literature search | PubMed · OpenAlex · Semantic Scholar · DOI deduplication · RIS/BibTeX export |
bioproject-search |
NCBI BioProject metadata retrieval | E-utilities API · title/description/species · keyword filtering · CSV/JSON export |
paper-fulltext-extract |
Structured full-text extraction from PDFs | GROBID to TEI · section tree/figure/reference parsing · JSON/MD/HTML output · corpus search · lead sentences |
| Skill | Description | Key capabilities |
|---|---|---|
scientific-figures |
Publication-grade figure generation | ROC · confusion matrices · heatmaps · volcano plots · SHAP summaries · unified publication styling |
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 ski