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
npx skills add majiayu000/claude-skill-registry --skill scvi-tools-yongjianwan-agentskill --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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/environment_setup.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 integra
- How to Use This Skill
- When to Use This Skill
- Model Selection Guide
- Workflow Reference Files
- CLI Scripts
- Pipeline Scripts
- Example Workflow
- Python Utilities
- Critical Requirements
- Quick Decision Tree
- Key Resources
python scripts/validate_adata.py raw.h5ad --batch-key batch --suggest python scripts/prepare_data.py raw.h5ad prepared.h5ad --batch-key batch --n-hvgs 2000 python scripts/train_model.py prepared.h5ad results/ --model scvi --batch-key batch python scripts/cluster_embed.py results/adata_trained.h5ad results/ --resolution 0.8 python scripts/differential_expression.py results/model results/adata_clustered.h5ad results/de.csv --groupby leiden
What does the scvi-tools skill do?
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
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill scvi-tools-yongjianwan-agentskill --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.