SpatialAgent focuses on the biological interpretation of spatial transcriptomics
Scanned 9/2/2026
Install to Claude Code
npx -y skills add majiayu000/claude-skill-registry --skill spatial-agent --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: spatial-agent
description: SpatialAgent focuses on the biological interpretation of spatial transcriptomics
data, specifically aiming to propose mechanistic hypotheses about tissue organization
and cellular interactions.
---
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# COPYRIGHT NOTICE
# This file is part of the "Universal Biomedical Skills" project.
# Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>
# All Rights Reserved.
#
# This code is proprietary and confidential.
# Unauthorized copying of this file, via any medium is strictly prohibited.
#
# Provenance: Authenticated by MD BABU MIA
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---
name: 'spatial-agent'
description: 'An agent that interprets spatial transcriptomics data to propose mechanistic hypotheses and analyze tissue organization.'
measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
allowed-tools:
- read_file
- run_shell_command
---
# SpatialAgent
SpatialAgent focuses on the biological interpretation of spatial transcriptomics data, specifically aiming to propose mechanistic hypotheses about tissue organization and cellular interactions.
## When to Use This Skill
* **Mechanistic Interpretation**: When you have clusters or spatial domains and need to understand *why* they are organized that way.
* **Cell-Cell Interaction**: To predict and interpret ligand-receptor interactions in a spatial context.
* **Hypothesis Generation**: To propose biological mechanisms driving the observed spatial heterogeneity.
## Core Capabilities
1. **Tissue Organization Analysis**: Decodes the structural logic of tissues (e.g., layers, niches).
2. **Cellular Interaction Prediction**: Identifies potential signaling pathways active at domain boundaries.
3. **Hypothesis Proposal**: Generates testable biological hypotheses based on spatial data.
## Workflow
1. **Input Analysis**: Accepts processed ST data (e.g., cluster annotations, DEG lists per spatial domain).
2. **Knowledge Retrieval**: Queries biological knowledge bases regarding the observed cell types and genes.
3. **Synthesis**: Constructs a narrative explaining the spatial arrangement (e.g., "The proximity of fibroblasts and tumor cells suggests a desmoplastic reaction mediated by TGF-beta signaling...").
## Example Usage
**User**: "Why are the macrophages located at the boundary of the tumor core in this sample?"
**Agent Action**:
1. Analyzes the gene expression of macrophages and adjacent tumor cells.
2. Checks for ligand-receptor pairs (e.g., CSF1-CSF1R).
3. Proposes: "Macrophages are likely recruited by CSF1 secreted by the tumor cells, forming an immunosuppressive barrier..."
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