Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata.
8.3
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Exceptional skill for single-cell RNA-seq analysis. The description clearly defines use cases (QC, normalization, clustering, etc.) and boundaries (referring deep learning to scvi-tools). SKILL.md provides comprehensive task knowledge with complete code examples covering the full analysis pipeline from data loading through cell type annotation. Structure is excellent: concise overview with well-organized sections, and detailed references/scripts are properly offloaded to separate files. The skill demonstrates high novelty by packaging a complex bioinformatics workflow that would otherwise require extensive domain knowledge and many CLI agent tokens to orchestrate correctly. Minor improvement possible: could add more explicit troubleshooting patterns for common errors. Overall, this is a well-crafted, production-ready skill that meaningfully reduces complexity and cost for scRNA-seq analysis.
K-Dense-AI
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