Seurat Single-Cell Analyzer Skill

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Seurat single-cell analysis skill for clustering, annotation, and trajectory analysis

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Seurat Single-Cell Analyzer Skill skill

What this skill tells your AI

The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/science/bioinformatics/skills/seurat-single-cell-analyzer/SKILL.md and read by ahel’s review.

Purpose

Enable Seurat single-cell analysis for clustering, annotation, and trajectory analysis of scRNA-seq data.

Capabilities

Quality filtering and normalization
Dimensionality reduction (PCA, UMAP)
Graph-based clustering
Marker gene identification
Cell type annotation
Integration across datasets
Trajectory inference

Usage Guidelines

Apply quality filters appropriate for experiment
Normalize data before dimensionality reduction
Select clustering resolution based on biology
Identify markers for cluster annotation
Integrate datasets to remove batch effects
Document analysis parameters

Dependencies

Seurat
Scanpy
CellRanger

Process Integration

Single-Cell RNA-seq Analysis (scrnaseq-analysis)
Spatial Transcriptomics Analysis (spatial-transcriptomics)

Signals

GitHub stars: 2k
Forks: 112
Last commit: Sep 2026

Advanced

Item type: skill
Key: seurat-single-cell-analyzer
Source: github.com/a5c-ai/babysitter

github.com/a5c-ai/babysitter