---name: cell_agent
description: LLM-driven multi-agent framework for automated single-cell analysis.
Best use case
---name: cell_agent is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
description: LLM-driven multi-agent framework for automated single-cell analysis.
Teams using ---name: cell_agent should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/CellAgent/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ---name: cell_agent Compares
| Feature / Agent | ---name: cell_agent | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
description: LLM-driven multi-agent framework for automated single-cell analysis.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
---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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