Biomedical Skills

SKILL.md files for cancer bioinformatics. Drop one into your project and your AI coding agent handles TCGA data, normalization, and statistics correctly.

Works with Claude Code · Cursor · Codex CLI · Gemini CLI

Skills

Skill	Description	Tests
`cancer-multiomics`	Multi-omics analysis for TCGA/GEO — expression (DESeq2), mutation (maftools), CNV (GISTIC2), methylation (minfi, DMRcate)	TCGA-LUAD
`immune-deconvolution`	Tumor microenvironment estimation via immunedeconv — quanTIseq, EPIC, CIBERSORT, xCell, MCP-counter, TIMER, ESTIMATE, tumor purity correction	TCGA-BRCA
`survival-analysis`	Time-to-event analysis — Kaplan-Meier (ggsurvfit), Cox PH (survival), competing risks (tidycmprsk), RMST (survRM2), optimal cutpoints, forest plots	TCGA-GBM
`single-cell-atlas`	Full scRNA-seq pipeline — QC, doublet detection, normalization, batch integration (Harmony, scVI), Leiden clustering, annotation (CellTypist), pseudobulk DE, trajectory (scVelo, Monocle3), cell communication (CellChat, LIANA), TF activity (decoupleR). Seurat v5 + scanpy	PBMC 3k
`spatial-transcriptomics`	Visium, Visium HD, Xenium, MERSCOPE, CosMx — loading (spatialdata, VisiumIO), spatially local QC (SpotSweeper), spatially variable genes (squidpy, nnSVG), deconvolution (RCTD, cell2location), domains (BANKSY, CellCharter), communication (LIANA+)	Visium mouse brain
`foundation-models`	scGPT, Geneformer, UCE, TranscriptFormer, Nicheformer, Tahoe-x1 — zero-shot embeddings, fine-tuning for annotation, in-silico perturbation, and the benchmark evidence for when a linear baseline wins instead	PBMC 3k
`variant-annotation`	VCF normalization and filtering (bcftools), functional annotation (VEP, SnpEff, ANNOVAR), germline classification (ACMG/AMP with ClinGen refinements), somatic oncogenicity (ClinGen/CGC/VICC) and clinical tiers (AMP/ASCO/CAP, OncoKB, CIViC), TMB, MSI (msisensor-pro), neoantigen prediction (pVACtools)	TCGA-LAML
`drug-response`	Dose-response modeling and drug sensitivity prediction — IC50/AUC curve fitting (drc, nplr, gdscIC50), GDSC/CTRP/PRISM retrieval and cross-dataset harmonization (PharmacoGx, DepMap), sensitivity prediction with regularized regression, tissue-corrected pharmacogenomic biomarkers	simulated
`clinical-nlp`	Information extraction from clinical free text — note sectioning and biomedical NER (scispaCy, cTAKES), assertion and negation via ConText (medspaCy), concept normalization to UMLS and ICD-10 (MedCAT), temporal relations, adverse events, de-identification (Presidio)	synthetic
`computational-pathology`	Whole-slide image processing — reading vendor formats (OpenSlide), the level-0 coordinate frame and microns-per-pixel semantics behind most WSI bugs, tissue detection, tile extraction, stain normalization (torchstain, HistomicsTK), H&E colour deconvolution	—
`meta-analysis`	Systematic review from protocol to synthesis — PROSPERO pre-specification, PICO search construction (MeSH, Emtree, Cochrane CHSSS), deduplication (synthesisr), two-reviewer screening with kappa, PRISMA 2020 flow diagrams, data extraction, risk of bias (RoB 2, ROBINS-I, ROBINS-E, robvis), pooling with metafor (REML, Knapp-Hartung, prediction intervals), subgroup analysis, meta-regression, small-study effects, network meta-analysis (netmeta, transitivity, inconsistency, rankings) and GRADE/CINeMA certainty	BCG

Quick start

pip install biomedical-ai-skills

From your project directory:

biomedical-skills list                                    # what's available
biomedical-skills install spatial-transcriptomics         # -> .claude/skills/
biomedical-skills install --all                           # everything
biomedical-skills install cancer-multiomics --target .cursor/skills

No dependencies, so it installs in a couple of seconds.

Or skip the package and copy the files directly:

git clone https://github.com/zamushwani/biomedical-ai-skills.git

mkdir -p your-project/.claude/skills/cancer-multiomics
cp skills/cancer-multiomics/SKILL.md your-project/.claude/skills/cancer-multiomics/

What's a SKILL.md?

A file that gives AI coding agents domain knowledge for a specific field. The agent reads it before generating code and follows tested protocols instead of guessing at parameters.

Without a skill: agent runs DESeq2 without pre-filtering, skips lfcShrink(), uses wrong contrast syntax. With a skill: agent pre-filters low-count genes, applies apeglm shrinkage, handles TCGA barcodes correctly.

Contributing

See CONTRIBUTING.md and SECURITY.md.

License

MIT

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