Scvi Tools
Scvi Tools provides streamlined access to Sci-Kit Learn Vector Imputation (scvi) for enhanced biomedical data analysis and feature engineering.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add ovachiever-scvi-tools npx -- -y @trustedskills/ovachiever-scvi-tools
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"ovachiever-scvi-tools": {
"command": "npx",
"args": [
"-y",
"@trustedskills/ovachiever-scvi-tools"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
The ovachiever-scvi-tools skill provides access to single-cell variational inference tools. It allows AI agents to perform data analysis and modeling tasks related to single-cell genomics, including dimensionality reduction, clustering, and differential expression analysis. This tool is particularly useful for researchers working with complex biological datasets.
When to use it
- Analyzing scRNA-seq data to identify cell types and states.
- Performing quality control on single-cell datasets.
- Discovering gene signatures associated with specific cellular processes.
- Building predictive models based on single-cell transcriptomic profiles.
- Integrating single-cell data with other omics datasets.
Key capabilities
- Dimensionality reduction (e.g., PCA, UMAP)
- Clustering analysis
- Differential expression analysis
- Data quality control
Example prompts
- "Perform dimensionality reduction on this scRNA-seq dataset and visualize the results."
- "Cluster these single cells based on their gene expression profiles."
- "Identify genes that are differentially expressed between these two cell populations."
Tips & gotchas
This skill requires a basic understanding of single-cell genomics concepts. Ensure your data is properly formatted according to scvi-tools' requirements for optimal results.
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