seurat-single-cell-analyzer

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

509 stars

Best use case

seurat-single-cell-analyzer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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

Teams using seurat-single-cell-analyzer should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/seurat-single-cell-analyzer/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/bioinformatics/skills/seurat-single-cell-analyzer/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/seurat-single-cell-analyzer/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How seurat-single-cell-analyzer Compares

Feature / Agent	seurat-single-cell-analyzer	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

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

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Seurat Single-Cell Analyzer Skill

## 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)

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