bioSkills

A collection of skills that guide AI coding agents (Claude Code, OpenAI Codex, Google Gemini, OpenClaw) through common bioinformatics tasks.

Project Goal

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.

Requirements

Python

• Python 3.9+
• biopython, pysam, cyvcf2, pybedtools, pyBigWig, scikit-allel, anndata

pip install biopython pysam cyvcf2 pybedtools pyBigWig scikit-allel anndata mygene

R/Bioconductor

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'))

CLI Tools

# 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

Installation

Claude Code

git clone git@github.com:GPTomics/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

Codex CLI

./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

Gemini CLI

./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

OpenClaw

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.

Skill Categories

| 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, gat