'description: LLM-driven multi-agent framework for automated single-cell
Scanned 9/2/2026
Install to Claude Code
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---
name: cellagent
description: 'description: LLM-driven multi-agent framework for automated single-cell
analysis.'
---
---name: cell_agent
description: LLM-driven multi-agent framework for automated single-cell analysis.
keywords:
- scRNA-seq
- scanpy
- annotation
- autonomous
- bioinformatics
measurable_outcome: Achieves >85% accuracy in cell type annotation compared to manual curation on standard benchmarks.
license: MIT
metadata:
author: Artificial Intelligence Group
version: "1.0.0"
compatibility:
- system: Python 3.9+
allowed-tools:
- run_shell_command
- read_file
---"
# CellAgent
CellAgent is a multi-agent system capable of autonomously handling the entire single-cell RNA-seq (scRNA-seq) analysis pipeline. It simulates a team of biological experts to process data, annotate cells, and perform downstream analysis.
## When to Use This Skill
* **Automated Annotation**: When you have raw scRNA-seq data and need cell type labels without manual curation.
* **Complex Workflows**: For multi-step analysis (QC -> Clustering -> Annotation -> DE Analysis).
* **Data Integration**: When merging multiple datasets (e.g., from different batches).
## Core Capabilities
1. **Planning**: Decomposes analysis goals into executable steps.
2. **Tool Execution**: Generates and runs Python code for Scanpy/Seurat.
3. **Self-Correction**: detects errors in execution and attempts to fix them.
## Workflow
1. **Input**: User query + scRNA-seq data (H5AD).
2. **Planner**: The Planning Agent breaks the task into sub-tasks.
3. **Executor**: The Coding Agent writes scripts to execute the plan.
4. **Reviewer**: Checks the results and logs outputs.
## Example Usage
**User**: "Process this dataset, filter low-quality cells, and annotate clusters."
**Agent Action**:
```bash
# Assuming a wrapper exists or running the main module from the repo
python3 Skills/Genomics/Single_Cell/CellAgent/repo/main.py --data "./data.h5ad" --goal "annotate"
```
## References
- *Mao et al., 2025*
- *arXiv 2407.09811*
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