Agent skill · research science · anthropics
scvi-tools
Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
Why this skill is useful
Adds multiple executable scripts for deep learning workflows in single-cell analysis that are not commonly found in public documentation.
What it needs
Requires python installed locally. About 4k tokens when loaded. Last updated 2026-08-07. 23,345 stars on the source repository.
What this skill does
scvi-tools Deep Learning Skill This skill provides guidance for deep learning-based single-cell analysis using scvi-tools, the leading framework for probabilistic models in single-cell genomics. How to Use This Skill 1. Identify the appropriate workflow from the model/workflow tables below 2. Read the corresponding reference file for detailed steps and code 3. Use scripts in scripts/ to avoid rewriting common code 4. For installation or GPU issues, consult references/environmentsetup.md 5. For debugging, consult references/troubleshooting.md When to Use This Skill When scvi-tools, scVI, scANVI, or related models are mentioned When deep learning-based batch correction or integration is needed When working with multi-modal data (CITE-seq, multiome) When reference mapping or label transfer is required When analyzing ATAC-seq or spatial transcriptomics data When learning latent representations of single-cell data Model Selection Guide Data Type Model Primary Use Case ----------- ------- ------------------ scRNA-seq scVI Unsupervised integration, DE, imputation scRNA-seq + labels scANVI Label transfer, semi-supervised integration CITE-seq (RNA+protein) totalVI Multi-modal integration, protein denoising scATAC-seq PeakVI Chromatin accessibility analysis Multiome (RNA+ATAC) MultiVI Joint modality analysis Spatial + scRNA reference DestVI Cell type deconvolution RNA velocity veloVI Transcriptional dynamics Cross-technology sysVI System-level batch correction Workflow Reference Files Workflow Reference File Description ---------- --------------- ------------- Environment Setup references/environmentsetup.md Installation, GPU, version info Data Preparation references/datapreparation.md Formatting data for any model scRNA Integration references/scrnaintegration.md scVI/scANVI batch correction ATAC-seq Analysis references/atacpeakvi.md PeakVI for accessibility CITE-seq Analysis references/citeseqtotalvi.md totalVI for protein+RNA Multiome Analysis references/multiomemultivi.md …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills anthropics/scvi-tools