scvi-tools

Guides Claude through scvi-tools deep-learning workflows on AnnData single-cell inputs, writing trained model artifacts, latent representations, and normalized expression matrices.

   
Type Claude Skill
Supplier Anthropic
Availability GA — distributed via anthropics/life-sciences (Oct 2025)
Pricing Free / OSS
Capabilities Read/Write

How to install

  • Also packaged in the SciAgent-Skills collection (jaechang-hits (community OSS, CC BY 4.0)): clone jaechang-hits/SciAgent-Skills and run /plugin install sciagent-skills in Claude Code (or copy skills/genomics-bioinformatics/single-cell/scvi-tools-single-cell into ~/.claude/skills/).
  • Claude Code — plugin marketplace: 
    /plugin marketplace add anthropics/life-sciences
/plugin install scvi-tools@life-sciences
  • Claude.ai — Settings → Capabilities → Skills → Upload skill, using the skill bundle from the anthropics/life-sciences repo.
  • Alternative — K-Dense skill: npx skills add K-Dense-AI/scientific-agent-skills, then enable the scvi-tools skill. A community-maintained skill (MIT collection) wrapping the same scvi-tools library; use it if you don’t have anthropics/life-sciences marketplace access.

What it does

Workflows for scVI / scANVI (batch correction, semi-supervised cell-type annotation), totalVI and MultiVI (CITE-seq, RNA+ATAC multi-modal integration), PeakVI and scBasset (chromatin accessibility), DestVI / Tangram / cell2location / Stereoscope (spatial deconvolution), contrastiveVI (perturbation), sysVI (cross-cohort batch correction), and veloVI (RNA velocity). Covers model setup, training, and latent-extraction code patterns.

Primary use cases: Deep-learning-based batch integration, reference-mapped cell-type annotation, multi-modal CITE-seq / multiome analysis, spatial transcriptomics deconvolution.

Notes

Requires Python with scvi-tools (PyTorch + AnnData stack) installed locally. GPU recommended for larger datasets. Expects a raw-count layer (not log-normalized) when registering AnnData via setup_anndata.

Sources

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