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Install with the open skills CLI (global, non-interactive — available in every Claude Code session):
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill "cellagent-annotation" -g -a claude-code -yOr manually — clone and copy the skill directory (SKILL.md + companion files):
git clone --depth 1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills /tmp/OpenClaw-Medical-Skills && cp -r /tmp/OpenClaw-Medical-Skills/skills/cellagent-annotation ~/.claude/skills/cellagent-annotationThis skill is a directory: SKILL.md is the entry point; the files below ship with it.
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# 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.
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# This code is proprietary and confidential.
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---
name: cellagent-annotation
description: Cell tagger
keywords:
- single-cell
- markers
- annotation
- confidence
- tissue
measurable_outcome: Label every provided cluster with a cell type + confidence + marker evidence (or "ambiguous") within 15 minutes per dataset.
license: MIT
metadata:
author: CellAgent Team
version: "1.0.0"
compatibility:
- system: Python 3.9+
allowed-tools:
- run_shell_command
- read_file
---
# CellAgent Annotation
Use CellTypeAgent to interpret marker genes, annotate scRNA-seq clusters, and coordinate multi-agent workflows for downstream analysis.
## When to Use
- Automated annotation of scRNA-seq datasets without manual curation.
- Multi-step workflows (QC → clustering → annotation → DE analysis).
- Integrating multiple batches requiring consistent labeling.
## Core Capabilities
1. **Planning:** Multi-agent planner decomposes analysis goals into steps.
2. **Tool execution:** Generates Scanpy/Seurat code and runs it autonomously.
3. **Self-correction:** Detects execution errors and retries with fixes.
## Workflow
1. Gather marker lists per cluster, plus species/tissue context and optional atlas references.
2. Run CellTypeAgent (`pip install -r requirements.txt` then `python repo/main.py --data data.h5ad --goal annotate`).
3. Review outputs for supporting markers; downgrade ambiguous clusters when signals conflict.
4. Produce final table (cluster, label, confidence, supporting markers, notes) and cite references when used.
## Example Usage
```bash
python3 Skills/Genomics/Single_Cell/CellAgent/repo/main.py --data "./data.h5ad" --goal "annotate"
```
## Guardrails
- Avoid over-specific lineages if markers overlap; default to broader types.
- Flag clusters showing multiple signatures for manual review.
- Respect species/tissue differences when interpreting markers.
## References
- README + upstream paper (Mao et al., 2025 / arXiv 2407.09811).
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