What does the Scanpy AI skill do?

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 use Scanpy on TypingMind?

Because you install it once and use it with any model. Scanpy is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Scanpy in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jimmc414/Kosmos/tree/master/kosmos-claude-scientific-skills/scientific-skills/scanpy. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Scanpy?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Scanpy?

As many as you like. As long as a model supports skills, you can use Scanpy with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Scanpy AI skill free?

It is published on GitHub by jimmc414. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

Scanpy by jimmc414 — AI Skill for Any LLM

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

python
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

python
# From 10X Genomics
adata = sc.read_10x_mtx('path/to/data/')
adata = sc.read_10x_h5('path/to/data.h5')

# From h5ad (AnnData format)
adata = sc.read_h5ad('path/to/data.h5ad')

# From CSV
adata = sc.read_csv('path/to/data.csv')

Understanding AnnData Structure

The AnnData object is the core data structure in scanpy:

python
adata.X          # Expression matrix (cells × genes)
adata.obs        # Cell metadata (DataFrame)
adata.var        # Gene metadata (DataFrame)
adata.uns        # Unstructured annotations (dict)
adata.obsm       # Multi-dimensional cell data (PCA, UMAP)
adata.raw        # Raw data backup

# Access cell and gene names
adata.obs_names  # Cell barcodes
adata.var_names  # Gene names

Standard Analysis Workflow

1. Quality Control

Identify and filter low-quality cells and genes:

python
# Identify mitochondrial genes
adata.var['mt'] = adata.var_names.str.startswith('MT-')

# Calculate QC metrics
sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], inplace=True)

# Visualize QC metrics
sc.pl.violin(adata, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt'],
             jitter=0.4, multi_panel=True)

# Filter cells and genes
sc.pp.filter_cells(adata, min_genes=200)
sc.pp.filter_genes(adata, min_cells=3)
adata = adata[adata.obs.pct_counts_mt < 5, :]  # Remove high MT% cells

Use the QC script for automated analysis:

bash
python scripts/qc_analysis.py input_file.h5ad --output filtered.h5ad

2. Normalization and Preprocessing

python
# Normalize to 10,000 counts per cell
sc.pp.normalize_total(adata, target_sum=1e4)

# Log-transform
sc.pp.log1p(adata)

# Save raw counts for later
adata.raw = adata

# Identify highly variable genes
sc.pp.highly_variable_genes(adata, n_top_genes=2000)
sc.pl.highly_variable_genes(adata)

# Subset to highly variable genes
adata = adata[:, adata.var.highly_variable]

# Regress out unwanted variation
sc.pp.regress_out(adata, ['total_counts', 'pct_counts_mt'])

# Scale data
sc.pp.scale(adata, max_value=10)

3. Dimensionality Reduction

python
# PCA
sc.tl.pca(adata, svd_solver='arpack')
sc.pl.pca_variance_ratio(adata, log=True)  # Check elbow plot

# Compute neighborhood graph
sc.pp.neighbors(adata, n_neighbors=10, n_pcs=40)

# UMAP for visualization
sc.tl.umap(adata)
sc.pl.umap(adata, color='leiden')

# Alternative: t-SNE
sc.tl.tsne(adata)

4. Clustering

python
# Leiden clustering (recommended)
sc.tl.leiden(adata, resolution=0.5)
sc.pl.umap(adata, color='leiden', legend_loc='on data')

# Try multiple resolutions to find optimal granularity
for res in [0.3, 0.5, 0.8, 1.0]:
    sc.tl.leiden(adata, resolution=res, key_added=f'leiden_{res}')

5. Marker Gene Identification

python
# Find marker genes for each cluster
sc.tl.rank_genes_groups(adata, 'leiden', method='wilcoxon')

# Visualize results
sc.pl.rank_genes_groups(adata, n_genes=25, sharey=False)
sc.pl.rank_genes_groups_heatmap(adata, n_genes=10)
sc.pl.rank_genes_groups_dotplot(adata, n_genes=5)

# Get results as DataFrame
markers = sc.get.rank_genes_groups_df(adata, group='0')

6. Cell Type Annotation

python
# Define marker genes for known cell types
marker_genes = ['CD3D', 'CD14', 'MS4A1', 'NKG7', 'FCGR3A']

# Visualize markers
sc.pl.umap(adata, color=marker_genes, use_raw=True)
sc.pl.dotplot(adata, var_names=marker_genes, groupby='leiden')

# Manual annotation
cluster_to_celltype = {
    '0': 'CD4 T cells',
    '1': 'CD14+ Monocytes',
    '2': 'B cells',
    '3': 'CD8 T cells',
}
adata.obs['cell_type'] = adata.obs['leiden'].map(cluster_to_celltype)

# Visualize annotated types
sc.pl.umap(adata, color='cell_type', legend_loc='on data')

7. Save Results

python
# Save processed data
adata.write('results/processed_data.h5ad')

# Export metadata
adata.obs.to_csv('results/cell_metadata.csv')
adata.var.to_csv('results/gene_metadata.csv')

Common Tasks

Creating Publication-Quality Plots

python
# Set high-quality defaults
sc.settings.set_figure_params(dpi=300, frameon=False, figsize=(5, 5))
sc.settings.file_format_figs = 'pdf'

# UMAP with custom styling
sc.pl.umap(adata, color='cell_type',
           palette='Set2',
           legend_loc='on data',
           legend_fontsize=12,
           legend_fontoutline=2,
           frameon=False,
           save='_publication.pdf')

# Heatmap of marker genes
sc.pl.heatmap(adata, var_names=genes, groupby='cell_type',
              swap_axes=True, show_gene_labels=True,
              save='_markers.pdf')

# Dot plot
sc.pl.dotplot(adata, var_names=genes, groupby='cell_type',
              save='_dotplot.pdf')

Refer to references/plotting_guide.md for comprehensive visualization examples.

Trajectory Inference

python
# PAGA (Partition-based graph abstraction)
sc.tl.paga(adata, groups='leiden')
sc.pl.paga(adata, color='leiden')

# Diffusion pseudotime
adata.uns['iroot'] = np.flatnonzero(adata.obs['leiden'] == '0')[0]
sc.tl.dpt(adata)
sc.pl.umap(adata, color='dpt_pseudotime')

Differential Expression Between Conditions

python
# Compare treated vs control within cell types
adata_subset = adata[adata.obs['cell_type'] == 'T cells']
sc.tl.rank_genes_groups(adata_subset, groupby='condition',
                         groups=['treated'], reference='control')
sc.pl.rank_genes_groups(adata_subset, groups=['treated'])

Gene Set Scoring

python
# Score cells for gene set expression
gene_set = ['CD3D', 'CD3E', 'CD3G']
sc.tl.score_genes(adata, gene_set, score_name='T_cell_score')
sc.pl.umap(adata, color='T_cell_score')

Batch Correction

python
# ComBat batch correction
sc.pp.combat(adata, key='batch')

# Alternative: use Harmony or scVI (separate packages)

Key Parameters to Adjust

Quality Control

min_genes: Minimum genes per cell (typically 200-500)
min_cells: Minimum cells per gene (typically 3-10)
pct_counts_mt: Mitochondrial threshold (typically 5-20%)

Normalization

target_sum: Target counts per cell (default 1e4)

Feature Selection

n_top_genes: Number of HVGs (typically 2000-3000)
min_mean, max_mean, min_disp: HVG selection parameters

Dimensionality Reduction

n_pcs: Number of principal components (check variance ratio plot)
n_neighbors: Number of neighbors (typically 10-30)

Clustering

resolution: Clustering granularity (0.4-1.2, higher = more clusters)

Common Pitfalls and Best Practices

Always save raw counts: adata.raw = adata before filtering genes
Check QC plots carefully: Adjust thresholds based on dataset quality
Use Leiden over Louvain: More efficient and better results
Try multiple clustering resolutions: Find optimal granularity
Validate cell type annotations: Use multiple marker genes
Use use_raw=True for gene expression plots: Shows original counts
Check PCA variance ratio: Determine optimal number of PCs
Save intermediate results: Long workflows can fail partway through

Bundled Resources

scripts/qc_analysis.py

Automated quality control script that calculates metrics, generates plots, and filters data:

bash
python scripts/qc_analysis.py input.h5ad --output filtered.h5ad \
    --mt-threshold 5 --min-genes 200 --min-cells 3

references/standard_workflow.md

Complete step-by-step workflow with detailed explanations and code examples for:

Data loading and setup
Quality control with visualization
Normalization and scaling
Feature selection
Dimensionality reduction (PCA, UMAP, t-SNE)
Clustering (Leiden, Louvain)
Marker gene identification
Cell type annotation
Trajectory inference
Differential expression

Read this reference when performing a complete analysis from scratch.

references/api_reference.md

Quick reference guide for scanpy functions organized by module:

Reading/writing data (sc.read_*, adata.write_*)
Preprocessing (sc.pp.*)
Tools (sc.tl.*)
Plotting (sc.pl.*)
AnnData structure and manipulation
Settings and utilities

Use this for quick lookup of function signatures and common parameters.

references/plotting_guide.md

Comprehensive visualization guide including:

Quality control plots
Dimensionality reduction visualizations
Clustering visualizations
Marker gene plots (heatmaps, dot plots, violin plots)
Trajectory and pseudotime plots
Publication-quality customization
Multi-panel figures
Color palettes and styling

Consult this when creating publication-ready figures.

assets/analysis_template.py

Complete analysis template providing a full workflow from data loading through cell type annotation. Copy and customize this template for new analyses:

bash
cp assets/analysis_template.py my_analysis.py
# Edit parameters and run
python my_analysis.py

The template includes all standard steps with configurable parameters and helpful comments.

Additional Resources

Official scanpy documentation: https://scanpy.readthedocs.io/
Scanpy tutorials: https://scanpy-tutorials.readthedocs.io/
scverse ecosystem: https://scverse.org/ (related tools: squidpy, scvi-tools, cellrank)
Best practices: Luecken & Theis (2019) "Current best practices in single-cell RNA-seq"

Tips for Effective Analysis

Start with the template: Use assets/analysis_template.py as a starting point
Run QC script first: Use scripts/qc_analysis.py for initial filtering
Consult references as needed: Load workflow and API references into context
Iterate on clustering: Try multiple resolutions and visualization methods
Validate biologically: Check marker genes match expected cell types
Document parameters: Record QC thresholds and analysis settings
Save checkpoints: Write intermediate results at key steps

Publisher	jimmc414
Repository	`Kosmos`
Skill name	`scanpy`
Stars	595
Forks	105
Bundled files	5
Links	Skill source Repository

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Scanpy: Single-Cell Analysis

Overview

When to Use This Skill

Quick Start

Basic Import and Setup

Loading Data

Understanding AnnData Structure

Standard Analysis Workflow

1. Quality Control

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

Trajectory Inference

Differential Expression Between Conditions

Gene Set Scoring

Batch Correction

Key Parameters to Adjust

Quality Control

Normalization

Feature Selection

Dimensionality Reduction

Clustering

Common Pitfalls and Best Practices

Bundled Resources

scripts/qc_analysis.py

references/standard_workflow.md

references/api_reference.md

references/plotting_guide.md

assets/analysis_template.py

Additional Resources

Tips for Effective Analysis

Bundled files

Frequently asked questions

Related AI Skills

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