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OpenClaw-Medical-Skills cellagent-annotation

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install
source · Clone the upstream repo
git clone https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/cellagent-annotation" ~/.claude/skills/freedomintelligence-openclaw-medical-skills-cellagent-annotation && rm -rf "$T"
OpenClaw · Install into ~/.openclaw/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/cellagent-annotation" ~/.openclaw/skills/freedomintelligence-openclaw-medical-skills-cellagent-annotation && rm -rf "$T"
manifest: skills/cellagent-annotation/SKILL.md
tags
#single-cell#annotation#marker-evidence#tissue-context#confidence-scoring
safety · automated scan (low risk)
This is a pattern-based risk scan, not a security review. Our crawler flagged:
  • pip install
Always read a skill's source content before installing. Patterns alone don't mean the skill is malicious — but they warrant attention.
source content
<!-- # COPYRIGHT NOTICE # 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. # # This code is proprietary and confidential. # Unauthorized copying of this file, via any medium is strictly prohibited. # # Provenance: Authenticated by MD BABU MIA -->

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

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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