How do I install the STAgent skill?

Run npx skillmds@latest add biotender-max/stagent 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 STAgent skill do?

<!-- It is listed under Coding & Dev Tools on SkillMD.

Is STAgent safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with STAgent?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is STAgent free to use?

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

Who published STAgent?

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

STAgent by biotender-max — Agent Skill

Name: STAgent
Author: BioTender-max

File contents

name: spatial-transcriptomics-agent description: Spatial analyst keywords: - spatial - h5ad - H&E - clustering - SVG measurable_outcome: For each sample, deliver ≥1 spatial domain map + SVG list + narrative interpretation within 30 minutes. license: MIT metadata: author: LiuLab version: "1.0.0" compatibility: - system: Python 3.9+ allowed-tools: - run_shell_command - read_file - web_fetch

Spatial Transcriptomics Agent

Run STAgent to align histology images with expression matrices, perform clustering/SVG detection, and generate literature-backed spatial reports.

When to Use

Analysis of Visium/Xenium or similar ST datasets.
Visual reasoning over spatial plots, H&E images, or cluster maps.
Automatically generating Scanpy/Squidpy code for new ST workflows.
Hypothesis generation about spatial gene expression patterns.

Core Capabilities

Dynamic code generation: Create/execute Python scripts for QC, clustering, SVG detection.
Visual reasoning: Interpret spatial plots to identify tissue domains and cell neighborhoods.
Literature retrieval: Pull references that contextualize findings.
Report generation: Deliver publication-style writeups with plots and SVG tables.

Workflow

Env setup: conda env create -f environment.yml && conda activate STAgent.
Data prep: Supply expression_path (.h5ad/Spaceranger) + image_path (H&E/IF) and metadata.
Task selection: Choose tasks such as cluster, find_svg, annotate_domains, or composite instructions; run python repo/src/main.py --data_path ... --task "...".
Execute & interpret: Let STAgent generate scripts, run analyses, and interpret results with literature references.
Package outputs: Save UMAP/spatial plots, SVG tables, QC details, and summary markdown.

Example Usage

User: "Analyze this breast cancer ST dataset, find immune infiltrates."
Agent: loads data, runs `sqidpy.gr.spatial_neighbors`, computes Leiden clusters, plots marker genes (CD3D, CD19), and summarizes which clusters map to tumor core vs. stromal/immune zones.

Guardrails

Document coordinate systems and any scaling between imaging and expression coordinates.
Avoid definitive cell-type labels without supporting markers.
Capture QC parameters for reproducibility.

References

Source repo: https://github.com/LiuLab-Bioelectronics-Harvard/STAgent
See local README.md for detailed instructions.

---name: stagentdescription: ---name: spatial-transcriptomics-agentdescription: Spatial analystkeywords: - spatial - h5ad - H&E - clustering - SVGmeasurable_outcome: For each sample, deliver ≥1 spatial domain map + SVG list + narrative interpretation within 30 minutes.license: MITmetadata: author: LiuLab version: "1.0.0"compatibility: - system: Python 3.9+allowed-tools: - run_shell_command - read_file - web_fetch---# Spatial Transcriptomics AgentRun STAgent to align histology images with expression matrices, perform clustering/SVG detection, and generate literature-backed spatial reports.## When to Use- Analysis of Visium/Xenium or similar ST datasets.- Visual reasoning over spatial plots, H&E images, or cluster maps.- Automatically generating Scanpy/Squidpy code for new ST workflows.- Hypothesis generation about spatial gene expression patterns.## Core Capabilities1. **Dynamic code generation:** Create/execute Python scripts for QC, clustering, SVG detection.2. **Visual reasoning:** Interpret spatial plots to identify tissue domains and cell neighborhoods.3. **Literature retrieval:** Pull references that contextualize findings.4. **Report generation:** Deliver publication-style writeups with plots and SVG tables.## Workflow1. **Env setup:** `conda env create -f environment.yml && conda activate STAgent`.2. **Data prep:** Supply `expression_path` (`.h5ad`/Spaceranger) + `image_path` (H&E/IF) and metadata.3. **Task selection:** Choose tasks such as `cluster`, `find_svg`, `annotate_domains`, or composite instructions; run `python repo/src/main.py --data_path ... --task "..."`.4. **Execute & interpret:** Let STAgent generate scripts, run analyses, and interpret results with literature references.5. **Package outputs:** Save UMAP/spatial plots, SVG tables, QC details, and summary markdown.## Example Usage```textUser: "Analyze this breast cancer ST dataset, find immune infiltrates."Agent: loads data, runs `sqidpy.gr.spatial_neighbors`, computes Leiden clusters, plots marker genes (CD3D, CD19), and summarizes which clusters map to tumor core vs. stromal/immune zones.```## Guardrails- Document coordinate systems and any scaling between imaging and expression coordinates.- Avoid definitive cell-type labels without supporting markers.- Capture QC parameters for reproducibility.## References- Source repo: https://github.com/LiuLab-Bioelectronics-Harvard/STAgent- See local `README.md` for detailed instructions.

Frequently asked questions

STAgent