seurat-single-cell-analyzer
Seurat single-cell analysis skill for clustering, annotation, and trajectory analysis
Profile →npx skills add a5c-ai/babysitter --skill seurat-single-cell-analyzer --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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)
- Purpose
- Capabilities
- Usage Guidelines
- Dependencies
- Process Integration
What does the seurat-single-cell-analyzer skill do?
Seurat single-cell analysis skill for clustering, annotation, and trajectory analysis
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill seurat-single-cell-analyzer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.