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.
SkillScore100Maintained
Automatic
◆Research pack
Last verified 8/11/2026
What it changes
Passed 23 of 23 test cases with the skill, and 17 of 23 without it.
Without skill74%
With skill100%
Checks it passed that the model alone missed
The response invokes sc.settings.set_figure_params.
The response references assets/analysis_template.py.
The response references references/plotting_guide.md.
and 3 more in the full report
6 fixed17 passed either way0 still failing0 broke
Pass-rate lift
+26 pp
Tokens
+147%
Agent turns
0%
Response time
-43%
Model gemini-3.6-flashMeasured 8/11/2026v123 of 23 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 6
Prompt
I am starting a new single-cell RNA-seq project from scratch and want to copy a pre-built full workflow template script provided in the workspace repository assets directory. What file path should I copy?
What was checked, without and with the skill
Checked
Without skill
With skill
The response references assets/analysis_template.py.
Missed
Passed
case-18 · gemini-3.6-flash · 8/11/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md380 linessingle file
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
import scanpy as sc
import pandas as pd
import numpy as np
# Configure settings
sc.settings.verbosity = 3
sc.settings.set_figure_params(dpi=80, facecolor='white')
sc.settings.figdir = './figures/'
Loading Data
# From 10X Genomics
adata = sc.read_10x_mtx('path/to/data/')
adata = sc.read_10x_h5('path/to/data.h5')
# From h5ad (AnnData format)