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Universal Single Cell Annotator

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universal-single-cell-annotator

Universal Single Cell Annotator is an Agent Skill for documents published in the majiayu000/claude-skill-registry repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

A unified interface for annotating single-cell RNA-seq data using Marker Genes, Deep Learning (CellTypist), or LLMs.

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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 and paste the prompt below.
  3. Review files and risks before approving any action.
Prompt to paste
Inspect the Agent Skill at https://github.com/majiayu000/claude-skill-registry/blob/HEAD/skills/data/RNA/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/universal-single-cell-annotator, but wait for my approval before writing files or running scripts.

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

---name: universal-single-cell-annotator description: A unified interface for annotating single-cell RNA-seq data using Marker Genes, Deep Learning (CellTypist), or LLMs. license: MIT metadata: author: AI Group version: "1.0.0" category: Genomics compatibility:

  • system: Python 3.9+
  • library: scanpy
  • library: celltypist (optional) allowed-tools:
  • run_shell_command
  • read_file

keywords:

  • rna
  • automation
  • biomedical measurable_outcome: execute task with >95% success rate. ---"

Universal Single-Cell Annotator

This skill wraps multiple cell type annotation strategies into a single Python class. It allows agents to flexibly choose between rule-based (markers), data-driven (CellTypist), or reasoning-based (LLM) approaches depending on the context.

When to Use This Skill

  • Initial Analysis: When processing raw AnnData objects.
  • Validation: When cross-referencing automated labels with known markers.
  • Discovery: When identifying rare cell types using LLM reasoning on marker lists.

Core Capabilities

  1. Marker-Based Scoring: Scores cells based on provided gene lists (e.g., "T-cell": ["CD3D", "CD3E"]).
  2. Deep Learning Reference: Wraps celltypist to transfer labels from massive atlases.
  3. LLM Reasoning: Extracts top markers per cluster and constructs prompts for LLM interpretation.

Workflow

  1. Load Data: Ensure data is in AnnData format (standard for Scanpy).
  2. Choose Strategy:
    • Use Markers if you have a known gene panel.
    • Use CellTypist for broad immune/tissue profiling.
    • Use LLM for novel clusters.
  3. Annotate: Run the corresponding method.
  4. Inspect: Check adata.obs for the new annotation columns.

Example Usage

User: "Annotate this dataset looking for T-cells and B-cells."

Agent Action:

from universal_annotator import UniversalAnnotator
import scanpy as sc

adata = sc.read_h5ad('data.h5ad')
annotator = UniversalAnnotator(adata)

markers = {
    'T-cell': ['CD3D', 'CD3E', 'CD8A'],
    'B-cell': ['CD79A', 'MS4A1']
}

annotator.annotate_marker_based(markers)
# Results in adata.obs['predicted_cell_type']