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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.

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Skills PyPI Downloads Python R Bioconductor TCGA

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

Browse skills, copy SKILL.md to your project, the agent reads domain protocols, you get correct code with tested parameters

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 immunedeconvquanTIseq, 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
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)

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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