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 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
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

Project links

Data verified by PyPI on Aug 16, 2026
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Avatar for zamushwani from gravatar.com zamushwani

Credits

Author/Maintainer: Hussain Zubair

GitHub Statistics

Data verified by PyPI on Aug 16, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
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Requires

Python >=3.9

Tags

ai-agent bioinformatics cancer-genomics claude-code multi-omics single-cell spatial-transcriptomics

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