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Single Cell Rna Qc

Official
anthropics·vlatest·other·Apache-2.0

This skill automates RNA quality control for single-cell data, ensuring reliable and accurate research results by flagging low-quality samples.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add anthropics/knowledge-work-plugins --skill single-cell-rna-qc -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

anthropics

Matched to the vendor's own GitHub organisation.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: www.sc-best-practices.org.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    www.sc-best-practices.orgundeclared

    What it found

    • bio-research/skills/single-cell-rna-qc/scripts/qc_analysis.py:5contacts www.sc-best-practices.org
      https://www.sc-best-practices.org/preprocessing_visualization/quality_control.html
    • bio-research/skills/single-cell-rna-qc/scripts/qc_core.py:7contacts www.sc-best-practices.org
      https://www.sc-best-practices.org/preprocessing_visualization/quality_control.html

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned anthropics/knowledge-work-plugins at da38ec1ee8.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
da38ec1ee8
Manifest
bio-research/skills/single-cell-rna-qc/SKILL.md
Scanned
6 files, 1,054 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill performs quality control checks on single-cell RNA sequencing data to ensure dataset integrity before downstream analysis. It identifies and flags low-quality cells, such as those with high mitochondrial gene expression or excessive ambient RNA contamination.

When to use it

  • Before running differential expression analysis to prevent skewed results from dead or dying cells.
  • During initial data processing pipelines to filter out technical artifacts early in the workflow.
  • When integrating multiple samples to standardize quality thresholds across different experimental batches.
  • To validate raw sequencing output before investing time in complex clustering or trajectory inference.

Key capabilities

  • Detects and removes low-quality cells based on mitochondrial gene percentage thresholds.
  • Identifies potential doublets or multiplets within the cell population.
  • Filters out ambient RNA contamination to improve gene expression accuracy.
  • Generates quality metrics reports for manual review or automated gating.

Example prompts

  • "Run quality control checks on my single-cell RNA dataset and remove cells with more than 20% mitochondrial reads."
  • "Identify potential doublets in this scRNA-seq sample using standard contamination thresholds."
  • "Filter out ambient RNA noise from my raw sequencing data before proceeding to clustering."

Tips & gotchas

Ensure your reference genome annotation is up-to-date, as outdated gene symbols can lead to incorrect mitochondrial percentage calculations. Always review the generated quality metrics visually, as automated filters may occasionally remove biologically relevant rare cell types if thresholds are set too aggressively.