This connects GWAS hits to actual drug targets by doing the full chain: variant to gene (via fine-mapping and eQTL), gene to druggable protein (via OpenTargets tractability), protein to existing compounds (via ChEMBL and DGIdb). The workflow is smart about the causal direction problem: a loss-of-function variant protecting against disease means you want an inhibitor, not an agonist. It includes a composite scoring system weighing genetic evidence, druggability, clinical data, and novelty. The documentation is unusually good about parameter gotchas (ensemblId vs ensemblID, disease_trait not trait). Best for target validation when you have genetic evidence and want to skip straight to what's druggable and whether something already exists for repurposing.
npx -y skills add mims-harvard/tooluniverse --skill tooluniverse-gwas-drug-discovery --agent claude-codeInstalls into .claude/skills of the current project.
Select a file.
coreyhaines31/marketingskills
alirezarezvani/claude-skills