This skill should be used when working with single-cell omics data analysis using scvi-tools, including scRNA-seq, scATAC-seq, CITE-seq, spatial transcriptomics, and other single-cell modalities. Use this skill for probabilistic modeling, batch correction, dimensionality reduction, differential expression, cell type annotation, multimodal integration, and spatial analysis tasks.
8.7
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Data & Analytics
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Excellent skill for single-cell omics analysis with scvi-tools. The description clearly identifies when to use this skill across multiple modalities (scRNA-seq, scATAC-seq, CITE-seq, spatial). SKILL.md provides comprehensive task knowledge with concrete code examples covering the full workflow (data loading, registration, training, extraction, downstream analysis), differential expression, batch correction, and model persistence. The structure is exemplary: a concise, well-organized main file with clear sections and delegation of detailed model-specific information to referenced files. The skill addresses a complex domain (probabilistic modeling, variational inference, multimodal integration) that would require extensive tokens and specialized knowledge for a CLI agent to handle independently. Best practices and theoretical foundations are appropriately included. Minor point: while highly useful, some basic scvi-tools operations might be accomplishable by a capable CLI agent with documentation, but the integrated workflow, model selection guidance, and domain-specific best practices provide clear value.
davila7
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