Scanpy

For analyzing single-cell RNA-seq data with the standard Python toolkit. Covers the full workflow from QC and normalization through clustering, UMAP visualization, and marker gene identification. Built on AnnData, works with 10X data and h5ad files. Note that version 1.12 requires Python 3.12+ and deprecated the per-plot save parameter in favor of autosave settings. The docs correctly warn that rank_genes_groups is fine for exploratory cluster markers but shouldn't be used for rigorous differential expression between conditions, you need pseudobulk and proper DE tools for that. Includes a QC script for automated filtering. Good default choice for exploratory scRNA-seq work unless you need probabilistic models, then reach for scvi-tools instead.

skills.sh installs: 2K
GitHub stars: 56.6K
last updated: Aug 21, 2026

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Install

$ npx -y skills add k-dense-ai/scientific-agent-skills --skill scanpy --agent claude-code

Installs into .claude/skills of the current project.

Files

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. Targets Scanpy 1.12.4 (released 2026-08-27), reviewed 2026-10-01. Native synthetic tests establish data/API contracts, not biological validity.

Installation

Requires Python 3.12+ (scanpy 1.12 dropped Python ≤3.11) and anndata ≥0.10.

uv pip install "scanpy[leiden]"

The [leiden] extra installs igraph and leidenalg; the scripts explicitly select flavor="igraph". For reproducible environments, pin a version: uv pip install "scanpy[leiden]==1.12.4".

For large or out-of-core datasets, many functions support Dask arrays (experimental):