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cell_agent

Agent Building
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LLM-driven multi-agent framework for automated single-cell analysis.

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How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

  1. Open your project in Codex.
  2. Copy the prompt below and paste it into your agent.
  3. Review the proposed files and risks before you approve installation.
Prompt to paste
I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/majiayu000/claude-skill-registry/blob/HEAD/skills/agent/cellagent/SKILL.md

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/cell-agent/. Do not write files or run scripts until I approve.

After I approve, install the complete skill folder, including required referenced files, into that project location. Verify it is discoverable, then tell me its actual invocation name and how to use it. Do not claim it is installed until you have verified it.

Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

---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:

# 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