scanpy

combined

Performs Scanpy single-cell RNA-seq QC, normalization, HVG selection, PCA/UMAP/t-SNE, clustering, exploratory marker ranking, pseudobulk preparation, visualization, and Seurat or SingleCellExperiment RDS conversion to h5ad. Applies to established exploratory scRNA-seq workflows with explicit count and expression provenance; complementary skills cover scvi-tools models and AnnData format details. Standard single-cell RNA-seq analysis pipeline. For quality control (QC), normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression analysis, and visualization. Best suited for exploratory single-cell transcriptomics analysis using established workflows. For deep learning models, use scvi-tools; for data format issues, use anndata.

When to use

Analyzing single-cell RNA-seq data (.h5ad, 10X, CSV formats)
Working with R-friendly single-cell datasets (`.rds`, `.RData`, Seurat, SingleCellExperiment) that need conversion to `.h5ad`
Performing quality control on scRNA-seq datasets
Creating UMAP, t-SNE, or PCA visualizations
Identifying cell clusters and finding marker genes
Annotating cell types based on gene expression
Conducting trajectory inference or pseudotime analysis
Generating publication-quality single-cell plots

When not to use

Use this skill only when the task clearly matches the scope described above.
Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Skill page

Conflict

The K-Dense-AI copy is the most rigorous and specific, containing critical 'Representation and inference contracts' (e.g., raw count provenance, HVG flavor differences, Harmony/BBKNN side effects) and specific version pinning (Python 3.12+, Scanpy 1.12.4) that the others lack. However, the other three copies (Vibe-Skills, aipoch, sickn33) provide a complete, step-by-step 'Standard Analysis Workflow' with Python code snippets for QC, normalization, clustering, and annotation that the K-Dense copy omits in favor of referencing bundled scripts. The aipoch copy also adds a 'When to Use' section that explicitly frames the skill as a packaged method for reproducible deliverables. Combining is necessary to capture both the rigorous data contracts of the K-Dense copy and the executable workflow steps of the others.