Scvi Tools

If you're doing single-cell genomics work, this wraps scvi-tools, the PyTorch-based framework for probabilistic models in single-cell analysis. It handles the usual suspects: scRNA-seq batch correction and integration, CITE-seq multimodal data, spatial transcriptomics deconvolution, ATAC-seq. The whole library follows a consistent setup-train-extract pattern with AnnData objects, so it plays nice with scanpy. The real value is in the variational inference approach for things like differential expression and batch effects, where you get actual probabilistic outputs instead of just point estimates. Works with GPU acceleration for large datasets. If you're still wrestling with harmony or Seurat integration issues, the scVI and scANVI models here are worth trying.

skills.sh installs: 249
GitHub stars: 33.2K
last updated: Aug 21, 2026

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Install

$ npx -y skills add davila7/claude-code-templates --skill scvi-tools --agent claude-code

Installs into .claude/skills of the current project.

Files

scvi-tools

Overview

scvi-tools is a comprehensive Python framework for probabilistic models in single-cell genomics. Built on PyTorch and PyTorch Lightning, it provides deep generative models using variational inference for analyzing diverse single-cell data modalities.

When to Use This Skill

Use this skill when:

Analyzing single-cell RNA-seq data (dimensionality reduction, batch correction, integration)
Working with single-cell ATAC-seq or chromatin accessibility data
Integrating multimodal data (CITE-seq, multiome, paired/unpaired datasets)
Analyzing spatial transcriptomics data (deconvolution, spatial mapping)
Performing differential expression analysis on single-cell data
Conducting cell type annotation or transfer learning tasks
Working with specialized single-cell modalities (methylation, cytometry, RNA velocity)
Building custom probabilistic models for single-cell analysis