Individual skill
bio-research-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.
skills-imported/bio-research-scvi-tools/SKILL.md
Source commit faf37c0859ebf627bfcb5225e8057d088691e0ae
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- Matches the observed repository HEAD
- Yes
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- No
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- No
- Observed behavior
- Not checked yet
- Last checked
- Nov 8, 2026
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Repository context
- Repository stars (not skill ratings)
- 54
- Repository last updated
- Oct 10, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent-skills Repository topic
- ai-agents Repository topic
- ai-agents-automation Repository topic
- ai-skills Repository topic
- awesome Repository topic
- awesome-list Repository topic
- chatgpt Repository topic
- claude-skills Repository topic
- meta-ai Repository topic
- muse-ai Repository topic
- data Derived category
- research Derived category
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Browse files
CONNECTORS.mddoc · 1,409 bytesLICENSE.txtdoc · 10,254 bytesSKILL.mdcore · 7,831 bytesSKILL.mdcore · 7,831 bytesSKILL.mdcore · 7,831 bytesSKILL.mdcore · 7,831 bytesSKILL.mdcore · 7,831 bytesreferences/atac_peakvi.mddoc · 9,498 bytesreferences/batch_correction_sysvi.mddoc · 10,456 bytesreferences/citeseq_totalvi.mddoc · 10,341 bytesreferences/data_preparation.mddoc · 6,335 bytesreferences/environment_setup.mddoc · 6,473 bytesreferences/label_transfer.mddoc · 10,431 bytesreferences/multiome_multivi.mddoc · 9,145 bytesreferences/rna_velocity_velovi.mddoc · 8,824 bytesreferences/scarches_mapping.mddoc · 11,090 bytesreferences/scrna_integration.mddoc · 9,877 bytesreferences/spatial_deconvolution.mddoc · 11,428 bytesreferences/troubleshooting.mddoc · 11,010 bytesscripts/cluster_embed.pyruns · 6,416 bytesscripts/differential_expression.pyruns · 6,821 bytesscripts/integrate_datasets.pyruns · 7,027 bytesscripts/model_utils.pyruns · 17,434 bytesscripts/prepare_data.pyruns · 5,218 bytesscripts/train_model.pyruns · 11,604 bytesscripts/transfer_labels.pyruns · 7,354 bytesscripts/validate_adata.pyruns · 12,211 bytes
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<!-- Provenance: adapted from anthropics/knowledge-work-plugins (Apache-2.0, (c) Anthropic).
Original skill: https://github.com/anthropics/knowledge-work-plugins/tree/main/bio-research/skills/scvi-tools. Changes for Muse AI: Claude/Cowork product refs -> Muse AI; MCP -> connector; CLAUDE.md -> MUSE.md; ${CLAUDE_PLUGIN_ROOT} -> this skill's directory; /plugin:command refs -> skill refs. -->
# 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`