How do I install the Spatial Transcriptomics Analysis skill?

Run npx skillmds@latest add biotender-max/spatial-transcriptomics-analysis in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Spatial Transcriptomics Analysis skill do?

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Is Spatial Transcriptomics Analysis safe to use?

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Which AI agents work with Spatial Transcriptomics Analysis?

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Is Spatial Transcriptomics Analysis free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Spatial Transcriptomics Analysis?

BioTender-max (@biotender-max) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Spatial Transcriptomics Analysis by biotender-max

Name: Spatial Transcriptomics Analysis
Author: BioTender-max

File contents

name: spatial-transcriptomics-analysis description: Automated analysis pipeline for Spatial Transcriptomics (Visium, Xenium) integrating histology and gene expression. keywords: - spatial-transcriptomics - visium - xenium - scanpy - squidpy measurable_outcome: Process a Visium dataset, identify spatially variable genes, and generate spatial feature plots within 30 minutes. license: MIT metadata: author: MD BABU MIA, PhD version: "1.0.0" compatibility: - system: python 3.9+ allowed-tools: - run_shell_command - read_file - write_file

Spatial Transcriptomics Skill

Version: 1.0.0 Author: MD BABU MIA, PhD Date: February 2026

Overview

This skill provides automated analysis capabilities for Spatial Transcriptomics data, specifically designed for 10x Visium and Xenium platforms. It enables the integration of histological data with gene expression profiles to uncover spatial organization of cell types.

Capabilities

Data Loading: Supports Spaceranger output (h5, images).
QC & Preprocessing: Spatial QC metrics, normalization.
Spatial Variable Features: Identification of spatially variable genes (SVGs) using Moran's I and Geary's C.
Deconvolution: Interface for cell type deconvolution (mapping scRNA-seq to spatial).
Visualization: Interactive spatial plots overlaying gene expression on tissue images.

Usage

from Skills.Genomics.Spatial_Transcriptomics.spatial_analyzer import SpatialAnalyzer

# Initialize
sa = SpatialAnalyzer(data_path="./data/visium_sample1")

# Run Pipeline
sa.load_data()
sa.preprocess()
sa.find_spatial_features()
sa.plot_spatial("INS", save_path="./output/insulin_spatial.png")

Requirements

scanpy
squidpy
anndata
matplotlib

---name: spatial-transcriptomics-analysisdescription: ---name: spatial-transcriptomics-analysisdescription: Automated analysis pipeline for Spatial Transcriptomics (Visium, Xenium) integrating histology and gene expression.keywords: - spatial-transcriptomics - visium - xenium - scanpy - squidpymeasurable_outcome: Process a Visium dataset, identify spatially variable genes, and generate spatial feature plots within 30 minutes.license: MITmetadata: author: MD BABU MIA, PhD version: "1.0.0"compatibility: - system: python 3.9+allowed-tools: - run_shell_command - read_file - write_file---# Spatial Transcriptomics Skill**Version:** 1.0.0**Author:** MD BABU MIA, PhD**Date:** February 2026## OverviewThis skill provides automated analysis capabilities for Spatial Transcriptomics data, specifically designed for 10x Visium and Xenium platforms. It enables the integration of histological data with gene expression profiles to uncover spatial organization of cell types.## Capabilities1. **Data Loading:** Supports Spaceranger output (h5, images).2. **QC & Preprocessing:** Spatial QC metrics, normalization.3. **Spatial Variable Features:** Identification of spatially variable genes (SVGs) using Moran's I and Geary's C.4. **Deconvolution:** Interface for cell type deconvolution (mapping scRNA-seq to spatial).5. **Visualization:** Interactive spatial plots overlaying gene expression on tissue images.## Usage```pythonfrom Skills.Genomics.Spatial_Transcriptomics.spatial_analyzer import SpatialAnalyzer# Initializesa = SpatialAnalyzer(data_path="./data/visium_sample1")# Run Pipelinesa.load_data()sa.preprocess()sa.find_spatial_features()sa.plot_spatial("INS", save_path="./output/insulin_spatial.png")```## Requirements* scanpy* squidpy* anndata* matplotlib

Frequently asked questions

Spatial Transcriptomics Analysis