single-cell-rna-qc Skill
Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis. Published by anthropics in life-sciences.
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Data analysis, Includes SKILL.md, Reusable instructions
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Copy skill directory
4 months ago
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What is single-cell-rna-qc Skill?
Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis. Published by anthropics in life-sciences. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement. See how SkillIndex evaluates profiles.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Data analysis
- Data analysis use cases
Declared skill metadata
- Source file: single-cell-rna-qc/SKILL.md
These fields retain source and confidence evidence from the indexed SKILL.md.
Compatibility and setup
- Install or run with Copy skill directory
When to use single-cell-rna-qc Skill
- Use it for data analysis.
Built with
Editorial notes
Source
- Creator: anthropics
- Repository: anthropics/life-sciences
- Skill file: single-cell-rna-qc/SKILL.md
What it does
Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.
Skill instructions
Single-Cell RNA-seq Quality Control Automated QC workflow for single-cell RNA-seq data following scverse best practices. When to Use This Skill Use when users: - Request quality control or QC on single-cell RNA-seq data - Want to filter low-quality cells or assess data quality - Need QC visualizations or metrics - Ask to follow scverse/scanpy best practices - Request MAD-based filtering or outlier detection Supported input formats: - .h5ad files (AnnData format from scanpy/Python workflows) - .h5 files (10X Genomics Cell Ranger output) Default recommendation: Use Approach 1 (complete pipeline) unless the user has specific custom requirements or explicitly requests non-standard filtering logic. Approach 1: Complete QC Pipeline (Recommended for Standard Workflows) For standard QC following scverse best practices, use the convenience script scripts/qcanalysis.py: bash python3 scripts/qcanalysis.py input.h5ad or for 10X Genomics .h5 files: python3 scripts/qcanalysis.py rawfeaturebcmatrix.h
Verified compatibility and discovery
Frequently asked questions
What is single-cell-rna-qc?
single-cell-rna-qc is a open-source AI agent skill with Copy skill directory. Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations.
Who is single-cell-rna-qc best for?
single-cell-rna-qc is best for reusing agent instructions, scripts, and references, data analysis workflows.
How do I install single-cell-rna-qc?
Install or run single-cell-rna-qc using Copy skill directory. Check single-cell-rna-qc for the latest setup command.
Is single-cell-rna-qc actively maintained?
single-cell-rna-qc may need a closer maintenance check before production use.
Project health auto-fetched from the source repository.
Maintain this resource?
Review this source-backed profile, send a correction with evidence, or link to it from your documentation. Claims verify your relationship to the project; profile facts still require source evidence and editorial review.
