A collection of skills that guide AI coding agents (Claude Code, OpenAI Codex, Google Gemini, OpenClaw) through common bioinformatics tasks.
This repository provides AI agents with expert knowledge for bioinformatics workflows. Each skill contains code patterns, best practices, and examples that help agents generate correct, idiomatic code for common tasks.
Target users range from undergrads learning computational biology to PhD researchers processing large-scale data. The skills cover the full spectrum from basic sequence manipulation to advanced analyses like single-cell RNA-seq and population genetics.
pip install biopython pysam cyvcf2 pybedtools pyBigWig scikit-allel anndata mygene
Required for differential expression, single-cell, pathway analysis, and methylation skills.
if (!require('BiocManager', quietly = TRUE))
install.packages('BiocManager')
BiocManager::install(c('DESeq2', 'edgeR', 'Seurat', 'clusterProfiler', 'methylKit'))
# macOS
brew install samtools bcftools blast minimap2 bedtools
# Ubuntu/Debian
sudo apt install samtools bcftools ncbi-blast+ minimap2 bedtools
# conda
conda install -c bioconda samtools bcftools blast minimap2 bedtools \
fastp kraken2 metaphlan sra-tools bwa-mem2 bowtie2 star hisat2 \
manta delly cnvkit macs3 tobias
git clone https://github.com/your-username/bioSkills.git
cd bioSkills
./install-claude.sh # Install globally
./install-claude.sh --project /path/to/project # Or install to specific project
./install-claude.sh --categories "single-cell,variant-calling" # Install specific categories
./install-claude.sh --list # List available skills
./install-claude.sh --validate # Validate all skills
./install-claude.sh --update # Only update changed skills
./install-claude.sh --uninstall # Remove all bio-* skills
./install-codex.sh # Install globally
./install-codex.sh --project /path/to/project # Or install to specific project
./install-codex.sh --categories "single-cell,variant-calling" # Install specific categories
./install-codex.sh --list # List available skills
./install-codex.sh --validate # Validate all skills
./install-codex.sh --update # Only update changed skills
./install-codex.sh --uninstall # Remove all bio-* skills
./install-gemini.sh # Install globally
./install-gemini.sh --project /path/to/project # Or install to specific project
./install-gemini.sh --categories "single-cell,variant-calling" # Install specific categories
./install-gemini.sh --list # List available skills
./install-gemini.sh --validate # Validate all skills
./install-gemini.sh --update # Only update changed skills
./install-gemini.sh --uninstall # Remove all bio-* skills
Install directly from ClawHub, or use the install script:
./install-openclaw.sh # Install all skills globally
./install-openclaw.sh --categories "single-cell,variant-calling" # Install specific categories
./install-openclaw.sh --project /path/to/workspace # Install to workspace
./install-openclaw.sh --tool-type-metadata # Add OpenClaw dependency metadata
./install-openclaw.sh --dry-run # Preview install + token estimate
./install-openclaw.sh --list # List available skills
./install-openclaw.sh --validate # Validate all skills
./install-openclaw.sh --update # Only update changed skills
./install-openclaw.sh --uninstall # Remove all bio-* skills
All installers support --categories for selective installation and --dry-run for previewing. Codex and Gemini convert to the Agent Skills standard (examples/ -> scripts/, usage-guide.md -> references/). OpenClaw keeps the original directory structure and optionally adds dependency metadata with --tool-type-metadata.
| Category | Skills | Primary Tools | Description |
|---|---|---|---|
| sequence-io | 9 | Bio.SeqIO | Read, write, convert FASTA/FASTQ/GenBank and 40+ formats |
| sequence-manipulation | 7 | Bio.Seq, Bio.SeqUtils | Transcription, translation, motif search, sequence properties |
| database-access | 11 | Bio.Entrez, BLAST+, SRA toolkit, UniProt API, STRINGdb | NCBI/UniProt queries, SRA downloads, BLAST, homology searches, interaction databases |
| alignment-files | 9 | samtools, pysam | SAM/BAM/CRAM viewing, sorting, filtering, statistics, validation |
| variant-calling | 13 | bcftools, cyvcf2, Manta, Delly, VEP, SnpEff | VCF/BCF calling, SVs, filtering, annotation, clinical interpretation |
| alignment | 4 | Bio.Align, Bio.AlignIO | Pairwise and multiple sequence alignment, MSA statistics, alignment I/O |
| phylogenetics | 5 | Bio.Phylo, IQ-TREE2, RAxML-ng | Tree I/O, visualization, ML inference with model selection, ultrafast bootstrap |
| differential-expression | 6 | DESeq2, edgeR, ggplot2, pheatmap | RNA-seq differential expression, visualization, batch correction |
| structural-biology | 6 | Bio.PDB, ESMFold, Chai-1 | PDB/mmCIF parsing, SMCRA navigation, geometric analysis, ML structure prediction |
| single-cell | 14 | Seurat, Scanpy, Pertpy, Cassiopeia, MeboCost | scRNA-seq QC, clustering, trajectory, communication, annotation, perturb-seq, lineage tracing, metabolite communication |
| pathway-analysis | 6 | clusterProfiler, ReactomePA, rWikiPathways, enrichplot | GO, KEGG, Reactome, WikiPathways enrichment |
| restriction-analysis | 4 | Bio.Restriction | Restriction sites, mapping, enzyme selection |
| methylation-analysis | 4 | Bismark, methylKit, bsseq | Bisulfite alignment, methylation calling, DMRs |
| chip-seq | 7 | MACS3, ChIPseeker, DiffBind | Peak calling, annotation, differential binding, motifs, QC, super-enhancers |
| metagenomics | 7 | Kraken2, MetaPhlAn, Bracken, HUMAnN | Taxonomic classification, abundance estimation, functional profiling, AMR detection |
| long-read-sequencing | 8 | Dorado, minimap2, Clair3, modkit, IsoSeq3 | Basecalling, alignment, polishing, variant calling, SV calling, methylation, Iso-Seq |
| read-qc | 7 | FastQC, MultiQC, fastp, Trimmomatic, Cutadapt | Quality reports, adapter trimming, filtering, UMIs |
| genome-intervals | 7 | BEDTools, pybedtools, pyBigWig | BED/GTF operations, interval arithmetic, bedGraph, bigWig |
| population-genetics | 6 | PLINK, FlashPCA2, ADMIXTURE, scikit-allel | GWAS, biobank-scale PCA, admixture, selection statistics |
| rna-quantification | 4 | featureCounts, Salmon, kallisto, tximport | Gene/transcript quantification, count matrix QC |
| read-alignment | 4 | bwa-mem2, bowtie2, STAR, HISAT2 | Short-read alignment for DNA and RNA-seq |
| expression-matrix | 4 | pandas, anndata, scanpy, biomaRt | Count matrix handling, gene ID mapping |
| copy-number | 4 | CNVkit, GATK | CNV detection, visualization, annotation |
| phasing-imputation | 4 | Beagle, SHAPEIT5, bcftools | Haplotype phasing, genotype imputation |
| atac-seq | 6 | MACS3, DiffBind, chromVAR, TOBIAS | ATAC-seq peaks, differential accessibility, footprinting, TF motif deviation |
| genome-assembly | 8 | SPAdes, Flye, hifiasm, QUAST, BUSCO | Assembly, polishing, scaffolding, quality assessment |
| primer-design | 3 | primer3-py | PCR primer design, qPCR probes, validation |
| spatial-transcriptomics | 11 | Squidpy, SpatialData, Scanpy, scimap | Visium, Xenium, Slide-seq, spatial stats, domain detection, deconvolution, spatial proteomics |
| hi-c-analysis | 8 | cooler, cooltools, pairtools, HiCExplorer | Contact matrices, compartments, TADs, loops, differential |
| alternative-splicing | 6 | rMATS-turbo, SUPPA2, IsoformSwitchAnalyzeR | Splicing quantification, differential splicing, isoform switching, sashimi visualization |
| chemoinformatics | 7 | RDKit, DeepChem, AutoDock Vina | Molecular I/O, descriptors, similarity, ADMET, virtual screening, reaction enumeration |
| liquid-biopsy | 6 | ichorCNA, fgbio, VarDict, FinaleToolkit | cfDNA preprocessing, fragmentomics, tumor fraction, ctDNA mutations, longitudinal monitoring |
| workflows | 40 | Various (workflow-specific) | End-to-end pipelines: RNA-seq, variants, ChIP-seq, scRNA-seq, spatial, Hi-C, proteomics, microbiome, CRISPR, metabolomics, multi-omics, immunotherapy, outbreak, metabolic modeling, splicing, liquid biopsy, genome annotation, GRN, causal genomics, time-course, eDNA |
| proteomics | 9 | pyOpenMS, MSstats, limma, QFeatures | Mass spec data import, QC, quantification, differential abundance, PTM, DIA |
| microbiome | 6 | DADA2, phyloseq, ALDEx2, QIIME2 | 16S/ITS amplicon processing, taxonomy, diversity, differential abundance |
| multi-omics-integration | 4 | MOFA2, mixOmics, SNF | Cross-modality integration, factor analysis, network fusion |
| crispr-screens | 8 | MAGeCK, JACKS, CRISPResso2, BAGEL2 | Pooled screen analysis, sgRNA efficacy modeling, hit calling, base/prime editing |
| metabolomics | 8 | XCMS, MetaboAnalystR, lipidr, MS-DIAL | Peak detection, annotation, normalization, pathway mapping, lipidomics, targeted |
| imaging-mass-cytometry | 6 | steinbock, squidpy, napari | IMC preprocessing, segmentation, spatial analysis, annotation, QC |
| flow-cytometry | 8 | flowCore, CATALYST, CytoML | FCS handling, compensation, gating, clustering, differential, QC |
| reporting | 5 | RMarkdown, Quarto, Jupyter, MultiQC, matplotlib | Reproducible reports, QC aggregation, publication figures |
| experimental-design | 4 | RNASeqPower, ssizeRNA, qvalue, sva | Power analysis, sample size, multiple testing, batch design |
| workflow-management | 4 | Snakemake, Nextflow, cwltool, Cromwell | Scalable pipeline frameworks with containers |
| data-visualization | 12 | ggplot2, matplotlib, plotly, ComplexHeatmap, NetworkX | Publication-quality figures, heatmaps, interactive plots, genome tracks, circos, UpSet, volcano, networks |
| tcr-bcr-analysis | 5 | MiXCR, VDJtools, Immcantation, scirpy | TCR/BCR repertoire analysis, clonotype assembly, diversity metrics |
| small-rna-seq | 5 | miRDeep2, miRge3, cutadapt, DESeq2 | miRNA/piRNA analysis, differential expression, target prediction |
| ribo-seq | 5 | Plastid, RiboCode, ORFik, riborex | Ribosome profiling, translation efficiency, ORF detection |
| epitranscriptomics | 5 | exomePeak2, MACS3, m6Anet, Guitar | RNA modifications (m6A), MeRIP-seq, ONT direct RNA |
| clip-seq | 5 | CLIPper, PureCLIP, umi_tools, HOMER | Protein-RNA interactions, crosslink detection, binding site motifs |
| clinical-databases | 10 | myvariant, requests, pandas, SigProfiler | Clinical variant queries, ClinVar/gnomAD, pharmacogenomics, TMB, HLA, PRS, signatures |
| genome-engineering | 5 | crisprscan, Cas-OFFinder, PrimeDesign | CRISPR guide design, off-target prediction, prime/base editing, HDR templates |
| systems-biology | 5 | cobrapy, CarveMe, memote | Flux balance analysis, metabolic reconstruction, model curation, gene essentiality |
| epidemiological-genomics | 5 | mlst, TreeTime, TransPhylo, AMRFinderPlus | Pathogen typing, phylodynamics, transmission networks, AMR surveillance |
| immunoinformatics | 5 | mhcflurry, pVACtools, BepiPred | MHC binding prediction, neoantigen identification, epitope prediction |
| comparative-genomics | 5 | MCScanX, PAML, OrthoFinder | Synteny analysis, positive selection, ancestral reconstruction, ortholog inference |
| genome-annotation | 6 | Bakta, BRAKER3, eggNOG-mapper, RepeatMasker | Prokaryotic/eukaryotic annotation, functional assignment, repeats, ncRNA, annotation transfer |
| gene-regulatory-networks | 5 | pySCENIC, SCENIC+, WGCNA, CellOracle | Co-expression networks, regulon inference, multiomics GRN, perturbation simulation |
| causal-genomics | 5 | TwoSampleMR, coloc, susieR, MR-PRESSO | Mendelian randomization, colocalization, fine-mapping, mediation, pleiotropy |
| rna-structure | 3 | ViennaRNA, Infernal, ShapeMapper2 | RNA secondary structure prediction, ncRNA search, structure probing |
| temporal-genomics | 5 | CosinorPy, Mfuzz, mgcv, statsmodels, scipy | Circadian rhythms, temporal clustering, trajectory modeling, dynamic GRN inference, periodicity detection |
| ecological-genomics | 6 | OBITools3, iNEXT, vegan, LEA, hierfstat, ASAP | eDNA metabarcoding, biodiversity metrics, community ecology, landscape genomics, conservation genetics, species delimitation |
| machine-learning | 6 | sklearn, shap, lifelines, scvi-tools | Biomarker discovery, model interpretation, survival analysis, atlas mapping |
Total: 425 skills across 62 categories
Once skills are deployed, ask your agent naturally. Here are examples across common workflows—the full collection covers 425 skills across 62 categories:
```
"I have RNA-seq counts from treated vs control samples - find the differentially expressed genes" "Run the complete RNA-seq pipeline from my FASTQ files to a list of DE genes" "What biological pathways are enriched in my upregulated genes?" "Run GSEA to see if whole pathways are up or down in my treatment" "Align my paired-end RNA-seq reads to the human genome with STAR" "Count reads per gene from my aligned BAM files"
$ claude mcp add bioSkills \
-- python -m otcore.mcp_server <graph>