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.
npx skills add majiayu000/claude-skill-registry --skill single-cell-rna-qc-mdbabumiamssm-llms-universal-life-2bf71998 --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.
## At-a-Glance - **description (10-20 chars):** QC autopilot - **keywords:** scRNAseq, MAD, h5ad, QC, plots - **measurable_outcome:** Produce filtered `.h5ad` files, before/after plots, and `qc_summary.json` within 20 minutes per dataset. ## Workflow 1. Accept `.h5ad`, 10x `.h5`, or 10x directory inputs; set mitochondrial/ribosomal patterns as needed. 2. Run `qc_analysis.py` (CLI) or call `qc_core` helpers to compute metrics, apply MAD thresholds, and filter cells/genes. 3. Generate standard plots (metrics before/after, threshold overlays) plus filtered data artifacts. 4. Document parameters (mad_counts/genes/mt, mt_threshold, min_cells, log1p flag) inside the summary JSON. 5. 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`, and `qc_plotting.py` for API usage and schema details.
- At-a-Glance
- Workflow
- Guardrails
- References
What does the scrna-qc skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill single-cell-rna-qc-mdbabumiamssm-llms-universal-life-2bf71998 --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.