Agent skill · research science · aiskillstore
scanpy
Single-cell RNA-seq analysis. Load .h5ad/10X data, QC, normalization, PCA/UMAP/t-SNE, Leiden clustering, marker genes, cell type annotation, trajectory, for scRNA-seq analysis.
Why this skill is useful
Provides specific commands and workflows for single-cell RNA-seq analysis that the AI wouldn't reliably generate on its own.
What it needs
Requires numpy, pandas, scanpy installed locally. About 5k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Scanpy: Single-Cell Analysis Overview Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis. When to Use This Skill This skill should be used when: Analyzing single-cell RNA-seq data (.h5ad, 10X, CSV formats) 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 Quick Start Basic Import and Setup Loading Data Understanding AnnData Structure The AnnData object is the core data structure in scanpy: Standard Analysis Workflow 1. Quality Control Identify and filter low-quality cells and genes: Use the QC script for automated analysis: 2. Normalization and Preprocessing 3. Dimensionality Reduction 4. Clustering 5. Marker Gene Identification 6. Cell Type Annotation 7. Save Results Common Tasks Creating Publication-Quality Plots Refer to references/plottingguide.md for comprehensive visualization examples. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/scanpy