scvi-tools Skill
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 decon Published by anthropics in knowledge-work-plugins.
Decision snapshot
Is this a fit?
Data analysis, Includes SKILL.md, Reusable instructions
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Copy skill directory
22 days ago · Apache-2.0 license
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What is scvi-tools Skill?
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 decon Published by anthropics in knowledge-work-plugins. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement. See how SkillIndex evaluates profiles.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Data analysis
- Data analysis use cases
Declared skill metadata
- Source file: bio-research/skills/scvi-tools/SKILL.md
These fields retain source and confidence evidence from the indexed SKILL.md.
Compatibility and setup
- Install or run with Copy skill directory
When to use scvi-tools Skill
- Use it for data analysis.
Built with
Editorial notes
Source
- Creator: anthropics
- Repository: anthropics/knowledge-work-plugins
- Skill file: bio-research/skills/scvi-tools/SKILL.md
What it does
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 decon
Skill instructions
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/environmentsetup.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 | |-
Verified compatibility and discovery
Frequently asked questions
What is scvi-tools?
scvi-tools is a open-source AI agent skill with Copy skill directory. 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.
Who is scvi-tools best for?
scvi-tools is best for reusing agent instructions, scripts, and references, data analysis workflows.
How do I install scvi-tools?
Install or run scvi-tools using Copy skill directory. Check scvi-tools for the latest setup command.
Is scvi-tools actively maintained?
scvi-tools may need a closer maintenance check before production use.
Project health auto-fetched from the source repository.
Maintain this resource?
Review this source-backed profile, send a correction with evidence, or link to it from your documentation. Claims verify your relationship to the project; profile facts still require source evidence and editorial review.
