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scvi-tools Single-Cell Modeling Skill

A reference skill that guides Claude through scvi-tools deep generative models for batch correction, multimodal integration, and probabilistic differential expression in single-cell omics.

Data & AnalyticsAdvanced33,0303,248AI score 7/10Last updated: Aug 9, 2026

What it does

scvi-tools is a PyTorch/Lightning framework of probabilistic models for single-cell genomics. This skill gives Claude a curated map of the library:

  • Model selection by modality: scRNA-seq (scVI, scANVI, AUTOZI, VeloVI, contrastiveVI), ATAC-seq (PeakVI, PoissonVI, scBasset), multimodal (totalVI, totalANVI, MultiVI, MrVI, DIAGVI), spatial (DestVI, Stereoscope, Tangram, scVIVA), specialized (MethylVI, CytoVI, Solo, CellAssign)
  • Namespace clarity: core models under scvi.model, everything else under scvi.external — prevents a very common import mistake
  • Canonical workflow: setup_anndatatrainget_latent_representation / get_normalized_expression → scanpy neighbors/UMAP/Leiden
  • Uncertainty-aware DE: differential_expression(mode="change", delta=...)
  • Best practices: raw counts only, gene filtering, covariate registration, model persistence, GPU acceleration
  • Pointers to deeper references/ files per model family and to theoretical foundations

Who it's for

  • Computational biologists and grad students working with single-cell RNA/ATAC data
  • Teams integrating many batches or studies where basic scanpy correction falls short
  • Anyone modeling CITE-seq, multiome, or spatial data probabilistically
  • Overkill if you only need a standard scanpy QC-to-clustering pipeline

Example uses

  1. "Batch-correct this multi-donor scRNA-seq object with scVI and give me a UMAP" → produces filtering, setup_anndata with batch/categorical/continuous covariates, training, X_scVI, and downstream scanpy calls
  2. "I have CITE-seq protein + RNA — which model?" → recommends totalVI (or totalANVI when labels exist) with working code
  3. "Run differential expression between two cell types with uncertainty" → uses mode="change" with a delta effect-size threshold and explains how to read the output

· · · Install guide · · ·

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
Download ZIP
Install in Claude Code

Let Claude do it — paste this into Claude Code

Install the skill I found on Claude Skill Mart.
Copy the skills/scvi-tools folder from the GitHub repo K-Dense-AI/scientific-agent-skills into my ~/.claude/skills/scvi-tools/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/K-Dense-AI/scientific-agent-skills.git && mkdir -p ~/.claude/skills && cp -r scientific-agent-skills/skills/scvi-tools ~/.claude/skills/

This is a third-party skill. Check the source repository before installing.

  1. Open a terminal and clone the repository: git clone https://github.com/K-Dense-AI/scientific-agent-skills.git
  2. Create your skills folder if needed: mkdir -p ~/.claude/skills
  3. Copy just this skill: cp -r scientific-agent-skills/skills/scvi-tools ~/.claude/skills/
  4. Confirm that SKILL.md and the references/ directory both landed in ~/.claude/skills/scvi-tools/.
  5. Install the library in a Python 3.12+ environment: uv pip install scvi-tools (or uv pip install "scvi-tools[cuda]" for GPU).
  6. Restart Claude Code and prompt something like "use scvi-tools to batch-correct this AnnData object".
  7. The doc cites a version/date that may be inaccurate, so verify with python -c "import scvi; print(scvi.__version__)" and cross-check docs.scvi-tools.org before relying on any specific API.