Agent skill · research science · k-dense-ai
scvi-tools
Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, or multi-modal integration (TOTALVI, MultiVI). Best for advanced modeling, batch effects, multimodal data. For standard analysis pipelines use scanpy.
Why this skill is useful
Adds advanced probabilistic modeling capabilities for single-cell genomics that the AI cannot generate on its own.
What it needs
Requires python, pytorch, scanpy installed locally. About 4k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
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. Current stable release: scvi-tools 1.4.3 (May 2026). Model namespaces matter: core models (scVI, scANVI, totalVI, MultiVI, PeakVI, AUTOZI, CondSCVI, DestVI, LinearSCVI, AmortizedLDA, JaxSCVI) live under scvi.model. Most other models (VeloVI, contrastiveVI, CellAssign, PoissonVI, scBasset, MrVI, MethylVI/MethylANVI, CytoVI, SysVI, Decipher, gimVI, scVIVA, ResolVI, Stereoscope, Solo, totalANVI, DIAGVI) live under scvi.external. The reference files specify the correct namespace per model. 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 Core Capabilities scvi-tools provides models organized by data modality: 1. Single-Cell RNA-seq Analysis Core models for expression analysis, batch correction, and integration. See references/models-scrna-seq.md for: scVI: Unsupervised dimensionality reduction and batch correction scANVI: Semi-supervised cell type annotation and integration AUTOZI: Zero-inflation detection and modeling VeloVI: RNA velocity analysis contrastiveVI: Perturbation effect isolation 2. Chromatin Accessibility (ATAC-seq) Models for analyzing single-cell chromatin data. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/scvi-tools