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 imaging — reading vendor formats (OpenSlide), the level-0 coordinate frame and microns-per-pixel semantics behind most WSI bugs, tissue detection, tiling, stain normalization (torchstain, HistomicsTK), H&E colour deconvolution, pathology foundation models as tile encoders (UNI, CONCH, Phikon) with their gating and licence constraints, multiple instance learning (CLAM, DSMIL, TransMIL), cell segmentation (StarDist, HoVer-Net), and spatial statistics on cell positions (squidpy) | validated |
biomedical-mcp |
Building Model Context Protocol servers that give AI agents tested access to biomedical databases — MCP tool design (mcp 2.0), the GDC REST API behind TCGA (projects, mutations, clinical), GEO search and Series Matrix retrieval, aggregating the CIViC, OncoKB and ClinVar biomarker databases, pagination and caching, and the data-shape traps (GDC expression is file references, GEO rows are probes not genes, CIViC evidence lives on molecular profiles, OncoKB is token-gated, cross-source nomenclature does not match) | validated |
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 |
Slash commands
Ten Claude Code slash commands that run the protocols above. Clone the repo and they work immediately — commands live in .claude/commands/, which Claude Code picks up per project.
| Command | Arguments | Skill it follows |
|---|---|---|
/analyze-degs |
[counts-file] [condition-column] |
cancer-multiomics |
/run-gsea |
[de-results-file] [gene-set-collection] |
cancer-multiomics |
/plot-survival |
[clinical-file] [group-column] |
survival-analysis |
/annotate-variants |
[vcf-file] [tumour-type] |
variant-annotation |
/deconvolve-immune |
[expression-file] [method] |
immune-deconvolution |
/qc-single-cell |
[h5ad-file] |
single-cell-atlas |
/analyze-spatial |
[data-path] [platform] |
spatial-transcriptomics |
/fit-dose-response |
[data-file] |
drug-response |
/tile-wsi |
[slide-path] [target-mpp] |
computational-pathology |
/query-tcga |
[project-id] [gene] |
biomedical-mcp |
/annotate-variants sample.vcf melanoma
/plot-survival clinical.tsv IDH_status
/tile-wsi slide.svs 0.5
Each command carries the pitfalls from its skill inline, so the protocol travels with the prompt rather than depending on the skill being loaded. Arguments are optional — a command invoked bare asks for what it needs.
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
1 maintainer
Metadata
1 maintainer
Release files for biomedical-ai-skills 0.9.0
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