
Description
Single-cell-rna-qc is an agent skill that runs quality control on single-cell RNA-seq data following scverse best practices. It accepts two input formats: .h5ad files in the AnnData format used by scanpy and Python workflows, and .h5 files from 10X Genomics Cell Ranger output. The skill is used to filter out low-quality cells, assess overall data quality, and produce QC visualizations and metrics. Its filtering is based on median absolute deviation, which flags outliers relative to the data itself rather than against fixed cutoffs. By default it recommends the complete pipeline, which covers the standard QC steps, and it only switches to custom filtering when a user has specific requirements or asks for non-standard logic. The skill is aimed at researchers and bioinformaticians who analyze single-cell data and want a consistent QC routine. It is part of Anthropic's knowledge-work plugins.…
Highlights
- Quality control for .h5ad and 10X Cell Ranger .h5 files
- Filters low-quality cells using MAD-based outlier detection
- Produces QC visualizations and metrics
- Follows scverse and scanpy best practices by default
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single-cell-rna-qc FAQ
What data formats does it accept?
.h5ad files in AnnData format from scanpy workflows, and .h5 files from 10X Genomics Cell Ranger.
What does the QC do?
It filters low-quality cells with MAD-based outlier detection, assesses the data, and produces QC visualizations and metrics.
Which approach does it use by default?
The complete pipeline, unless you have specific requirements or ask for non-standard filtering.
Who is it for?
Researchers and bioinformaticians working with single-cell RNA-seq data in Python.
How do I install it?
Run npx skills add https://github.com/anthropics/knowledge-work-plugins --skill single-cell-rna-qc in your terminal.
Is it free?
Yes. The skill is listed as free on skills.sh.
What security audits has it passed?
skills.sh lists three audits: Gen Agent Trust Hub Pass, Socket Pass and Snyk Pass.
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