Agent skill
scrna-qc
Execute the MAD-based single-cell RNA-seq QC workflow (scripts + Python API) to filter low-quality cells and emit reports plus filtered AnnData files.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/single-cell-rna-qc-mdbabumiamssm-llms-universal-life--2bf71998
SKILL.md
At-a-Glance
- description (10-20 chars): QC autopilot
- keywords: scRNAseq, MAD, h5ad, QC, plots
- measurable_outcome: Produce filtered
.h5adfiles, before/after plots, andqc_summary.jsonwithin 20 minutes per dataset.
Workflow
- Accept
.h5ad, 10x.h5, or 10x directory inputs; set mitochondrial/ribosomal patterns as needed. - Run
qc_analysis.py(CLI) or callqc_corehelpers to compute metrics, apply MAD thresholds, and filter cells/genes. - Generate standard plots (metrics before/after, threshold overlays) plus filtered data artifacts.
- Document parameters (mad_counts/genes/mt, mt_threshold, min_cells, log1p flag) inside the summary JSON.
- Provide guidance on next steps (doublet detection, downstream analysis).
Guardrails
- Adjust MT% expectations for tissue context; avoid over-filtering rare populations.
- This workflow is QC only—doublet handling and batch correction stay separate.
- Keep reproducibility by storing command invocations and environment info.
References
- See
README.md,qc_core.py,qc_analysis.py, andqc_plotting.pyfor API usage and schema details.
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