Handles the full pipeline for spatial transcriptomics data from platforms like 10x Visium, MERFISH, and Slide-seq. You get quality control, spatial clustering to identify tissue domains, detection of spatially variable genes using Moran's I, and cell-cell interaction mapping through ligand-receptor databases. The integration piece is solid: it can deconvolve cell types from scRNA-seq references using Cell2location or Tangram, then map those annotations onto tissue architecture. Built on squidpy and scanpy, so if you're already in that ecosystem it's a natural fit. The workflow is comprehensive but you'll need local Python for the heavy computation, while gene context and L-R databases hit ToolUniverse APIs.
npx -y skills add mims-harvard/tooluniverse --skill tooluniverse-spatial-transcriptomics --agent claude-codeInstalls into .claude/skills of the current project.
Select a file.
juliusbrussee/caveman
mattpocock/skills
shadcn/improve
obra/superpowers
forrestchang/andrej-karpathy-skills
vercel-labs/skills