spatial-agent

$npx mdskill add swaruplab/operon/spatial-agent

Interprets spatial transcriptomics data to propose mechanistic hypotheses about tissue organization.

  • Understands why tissue domains are organized in specific patterns.
  • Depends on read_file and run_shell_command tools.
  • Synthesizes cluster annotations and DEG lists with biological knowledge.
  • Outputs a narrative hypothesis about cellular interactions and tissue structure.

SKILL.md

.github/skills/spatial-agentView on GitHub ↗
---
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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