seuratclustering
Performs unsupervised clustering on single-cell RNA-seq data using Seurat. This process finds nearest neighbors, computes UMAP for visualization, and applies Louvain/Leiden algorithms to identify cell clusters. Clusters can be explored at multiple resolutions to balance granularity and biological relevance.
npx skills add majiayu000/claude-skill-registry --skill seuratclustering-pwwang-immunopipe-750e0e96 --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.
# SeuratClustering Process Configuration ## Purpose Performs unsupervised clustering on single-cell RNA-seq data using Seurat. This process finds nearest neighbors, computes UMAP for visualization, and applies Louvain/Leiden algorithms to identify cell clusters. Clusters can be explored at multiple resolutions to balance granularity and biological relevance. ## When to Use - **After SeuratPreparing**: Standard workflow after QC and normalization - **T/B cell selection**: After SeuratClusteringOfAllCells (if TOrBCellSelection enabled) - **Reference-based annotation**: Alternative to SeuratMap2Ref or CellTypeAnnotation - **Standard clustering**: When you need unsupervised cell type discovery - **Multi-resolution exploration**: When unsure of optimal cluster granularity ## Configuration Structure ### Process Enablement ```toml [SeuratClustering] cache = true # Cache intermediate results for faster re-runs ``` ### Input Specification ```toml [SeuratClustering.in] srtobj = ["SeuratPreparing"] # Path or reference to Seurat object ``` ### Environment Variables #### Core Parameters ```toml [SeuratClustering.envs] # Number of cores for parallelization ncores = 1 # int; Higher values speed u
- Purpose
- When to Use
- Configuration Structure
- Process Enablement
- Input Specification
- Environment Variables
- External References
- FindNeighbors (Seurat v5)
- RunUMAP (Seurat v5)
- FindClusters (Seurat v5)
- Algorithm Comparison: Leiden vs Louvain
- Integration Method Support
- Configuration Examples
- Minimal Configuration
What does the seuratclustering skill do?
Performs unsupervised clustering on single-cell RNA-seq data using Seurat. This process finds nearest neighbors, computes UMAP for visualization, and applies Louvain/Leiden algorithms to identify cell clusters. Clusters can be explored at multiple resolutions to balance granularity and biological relevance.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill seuratclustering-pwwang-immunopipe-750e0e96 --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 majiayu000/claude-skill-registry, a repository with 534 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.