How do I install the Scrna Qc skill?

Run npx skillmds add majiayu000/scrna-qc in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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. It is listed under Integrations & APIs on SkillMD.

Is Scrna Qc safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Scrna Qc?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Scrna Qc free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Scrna Qc?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Scrna Qc by majiayu000 — Agent Skill

Name: Scrna Qc
Rating: 4.2 (5 reviews)
Author: majiayu000

File contents

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

Accept .h5ad, 10x .h5, or 10x directory inputs; set mitochondrial/ribosomal patterns as needed.
Run qc_analysis.py (CLI) or call qc_core helpers 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, and qc_plotting.py for API usage and schema details.

Frequently asked questions