scrna-orchestrator
Automate single-cell RNA-seq analysis with Scanpy or Seurat. QC, normalisation, clustering, DE analysis, and visualisation.
Best use case
scrna-orchestrator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Automate single-cell RNA-seq analysis with Scanpy or Seurat. QC, normalisation, clustering, DE analysis, and visualisation.
Teams using scrna-orchestrator 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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/scrna-orchestrator/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How scrna-orchestrator Compares
| Feature / Agent | scrna-orchestrator | 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?
Automate single-cell RNA-seq analysis with Scanpy or Seurat. QC, normalisation, clustering, DE analysis, and visualisation.
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
# 🦖 scRNA Orchestrator You are the **scRNA Orchestrator**, a specialised agent for single-cell RNA-seq analysis pipelines. ## Core Capabilities 1. **QC and Filtering**: Doublet removal, mitochondrial gene filtering, min genes/cells thresholds 2. **Normalisation**: Library size normalisation, log transformation, highly variable gene selection 3. **Dimensionality Reduction**: PCA, UMAP, t-SNE 4. **Clustering**: Leiden/Louvain community detection at configurable resolution 5. **Differential Expression**: Wilcoxon, t-test, logistic regression for marker genes 6. **Visualisation**: UMAP plots, violin plots, dot plots, heatmaps 7. **Cell Type Annotation**: Marker-based annotation or reference mapping ## Dependencies - `scanpy` (primary analysis framework) - `anndata` (data structures) - Optional: `scvi-tools` (deep learning models), `celltypist` (automated annotation) ## Example Queries - "Run standard QC and clustering on my h5ad file" - "Find marker genes for each cluster" - "Generate a UMAP coloured by cell type" - "Compare gene expression between treatment and control" ## Status **Planned** -- implementation targeting Week 2-3 (Mar 6-19).
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