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Single Cell Rna Qc

Official
anthropics·vlatest·other

This skill automates RNA quality control for single-cell data, ensuring reliable analysis and accurate research results.

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/life-sciences --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

    • 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
    • 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/life-sciences at e96556b637.

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

Commit
e96556b637
Manifest
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 (QC) on single-cell RNA sequencing (scRNA-seq) data. It identifies and filters out low-quality cells and genes, ensuring robust downstream analysis. The tool helps researchers improve the accuracy and reliability of their scRNA-seq experiments by removing noise and artifacts.

When to use it

  • Before performing differential gene expression analysis on scRNA-seq datasets.
  • When dealing with scRNA-seq data exhibiting high dropout rates or technical variation.
  • To prepare scRNA-seq data for clustering, trajectory inference, or other downstream analyses.
  • For initial exploration and cleaning of newly generated scRNA-seq data.

Key capabilities

  • Cell filtering based on quality metrics (e.g., number of detected genes).
  • Gene filtering based on expression thresholds.
  • Data normalization.
  • Identification of outlier cells.

Example prompts

  • "Perform QC on my scRNA-seq data and filter out cells with fewer than 500 expressed genes."
  • “Normalize the gene counts in this single-cell dataset.”
  • "Identify and remove outlier cells from this scRNA-seq experiment."

Tips & gotchas

The skill requires properly formatted scRNA-seq count matrices as input. Ensure your data is appropriately structured before using the tool to avoid errors.