Agent skill · research science · anthropics
single-cell-rna-qc
Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.
Why this skill is useful
Provides a complete automated QC pipeline for single-cell RNA-seq data with specific filtering and visualization commands that the AI wouldn't reliably generate on its own.
What it needs
Requires anndata, matplotlib, numpy, scanpy, scipy, seaborn installed locally. About 4k tokens when loaded. Last updated 2026-08-07. 23,345 stars on the source repository.
What this skill does
Single-Cell RNA-seq Quality Control Automated QC workflow for single-cell RNA-seq data following scverse best practices. When to Use This Skill Use when users: Request quality control or QC on single-cell RNA-seq data Want to filter low-quality cells or assess data quality Need QC visualizations or metrics Ask to follow scverse/scanpy best practices Request MAD-based filtering or outlier detection Supported input formats: .h5ad files (AnnData format from scanpy/Python workflows) .h5 files (10X Genomics Cell Ranger output) Default recommendation: Use Approach 1 (complete pipeline) unless the user has specific custom requirements or explicitly requests non-standard filtering logic. Approach 1: Complete QC Pipeline (Recommended for Standard Workflows) For standard QC following scverse best practices, use the convenience script scripts/qcanalysis.py: The script automatically detects the file format and loads it appropriately. When to use this approach: Standard QC workflow with adjustable thresholds (all cells filtered the same way) Batch processing multiple datasets Quick exploratory analysis User wants the "just works" solution Requirements: anndata, scanpy, scipy, matplotlib, seaborn, numpy Parameters: Customize filtering thresholds and gene patterns using command-line parameters: --output-dir - Output directory --mad-counts, --mad-genes, --mad-mt - MAD thresholds for counts/genes/MT% --mt-threshold - Hard mitochondrial % cutoff --min-cells - Gene filtering threshold --mt-pattern, --ribo-pattern, --hb-pattern - Gene name patterns for different species Use --help to see current default values. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills anthropics/single-cell-rna-qc