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Scrna qc

Skill BioTender-max/awesome-bio-agent-skills/skills/openclaw/scrna-qc

A curated collection of AI agent skills for biomedical research, covering genomics, proteomics, single-cell analysis, clinical AI, and protein design.From the repository description

Install
npx -y skills add BioTender-max/awesome-bio-agent-skills --skill scrna-qc

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.

SKILL.md

1.7 KB, 456 tokens by cl100k_base, as published. Nobody here has run it

<!-- # COPYRIGHT NOTICE # This file is part of the "Universal Biomedical Skills" project. # Copyright (c) 2026 MD BABU MIA, PhD <[email protected]> # All Rights Reserved. # # This code is proprietary and confidential. # Unauthorized copying of this file, via any medium is strictly prohibited. # # Provenance: Authenticated by MD BABU MIA -->

name: scrna-qc description: 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. measurable_outcome: Produce filtered .h5ad files, before/after plots, and qc_summary.json within 20 minutes per dataset. allowed-tools:

  • read_file
  • run_shell_command

At-a-Glance

  • description (10-20 chars): QC autopilot
  • keywords: scRNAseq, MAD, h5ad, QC, plots

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.
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->

What ships with it: 5 files

36.2 KB alongside SKILL.md, 3 of them executable

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