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Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>

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name: 'universal-single-cell-annotator' description: 'Annotate scRNA-seq' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

• read_file
• run_shell_command

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.

1. Annotate: Run the corresponding method.
2. 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']

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