Individual skill
tooluniverse-scvi-tools
Deep learning for single-cell omics using scvi-tools — probabilistic (VAE-based) batch correction and integration with scVI, semi-supervised integration and label transfer with scANVI, CITE-seq RNA+protein joint modeling with totalVI, scATAC-seq analysis with PeakVI, multiome RNA+ATAC joint modeling with MultiVI, spatial transcriptomics cell-type deconvolution with DestVI, and RNA velocity with veloVI. Use when scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, or veloVI are mentioned by name, or when a request needs deep-learning-based batch correction, reference mapping / scArches-style query-to-reference mapping, denoising single-cell counts, joint modeling of two modalities (RNA+protein, RNA+ATAC, single-cell+spatial), or a variational-autoencoder latent space for single-cell data — as opposed to classic scanpy/Harmony-based integration.
plugins/tooluniverse/skills/tooluniverse-scvi-tools/SKILL.md
Source commit 5598a9fc97e32964fe9b703818e7d0747142f179
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- Last checked
- Nov 5, 2026
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- Repository last updated
- Nov 8, 2026
- License
- Apache-2.0
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- agents Repository topic
- reasoning-agent Repository topic
- reasoning-language-models Repository topic
- tool-use Repository topic
- ai-agents Repository topic
- ai-scientists Repository topic
- automated-science Repository topic
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SKILL.mdcore · 5,366 bytesSKILL.mdcore · 5,366 bytesreferences/model_selection.mddoc · 3,331 bytesreferences/multimodal_workflows.mddoc · 4,061 bytesreferences/scrna_integration.mddoc · 2,904 bytesreferences/troubleshooting.mddoc · 3,799 bytesscripts/_scvi_common.pyruns · 3,228 bytesscripts/differential_expression.pyruns · 3,331 bytesscripts/prepare_data.pyruns · 3,419 bytesscripts/train_model.pyruns · 6,148 bytesscripts/transfer_labels.pyruns · 3,871 bytesscripts/validate_adata.pyruns · 5,313 bytes
Read excerpt
# scvi-tools Deep Learning for Single-Cell Omics
Local, guided workflows for training and applying scvi-tools' probabilistic
deep-learning models on a user's own AnnData files.
## Honesty contract (read first)
This skill trains real neural networks via the real `scvi-tools` package. It
must never fabricate a latent space, a training curve, or a DE result.
1. **Preflight before anything.** Every script checks `import scvi` (and
`anndata`/`scanpy`/`numpy`) before touching data. If scvi-tools is not
installed, the script prints the exact `pip install scvi-tools` command
and exits 0 — it does not estimate, guess, or describe hypothetical