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Tooluniverse Rnaseq Deseq2

Listed
mims-harvard·vlatest·data·Apache-2.0

Analyzes RNA sequencing data using DESeq2 to identify differentially expressed genes, crucial for biological research and understanding gene regulation.

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 mims-harvard/tooluniverse --skill tooluniverse-rnaseq-deseq2 -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

mims-harvard

Taken from the source listing. We haven't verified who controls this account.

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: cran.r-project.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

    cran.r-project.orgundeclared

    What it found

    • plugin/skills/tooluniverse-rnaseq-deseq2/scripts/convert_rds_to_csv.py:179contacts cran.r-project.org
      print("  Or visit: https://cran.r-project.org/")
    • plugins/tooluniverse/skills/tooluniverse-rnaseq-deseq2/scripts/convert_rds_to_csv.py:179contacts cran.r-project.org
      print("  Or visit: https://cran.r-project.org/")
    • skills/tooluniverse-rnaseq-deseq2/scripts/convert_rds_to_csv.py:179contacts cran.r-project.org
      print("  Or visit: https://cran.r-project.org/")

  • 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 mims-harvard/tooluniverse at f5c37eebeb.

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

Commit
f5c37eebeb
Manifest
skills/tooluniverse-rnaseq-deseq2/SKILL.md
Scanned
60 files, 16,548 lines
Copies in repo
3 — all of them scanned
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

The tooluniverse-rnaseq-deseq2 skill executes DESeq2, a robust R package for differential analysis of count data, specifically tailored for RNA-seq experiments. It automates the workflow to identify genes with statistically significant expression changes between biological conditions.

When to use it

  • Analyzing bulk RNA-seq datasets to find differentially expressed genes (DEGs) between treatment and control groups.
  • Performing statistical normalization and variance stabilization on raw count matrices before hypothesis testing.
  • Generating standard DESeq2 output tables containing log2 fold changes, p-values, and adjusted p-values for downstream interpretation.

Key capabilities

  • Runs the full DESeq2 pipeline including data import, design matrix creation, and model fitting.
  • Calculates Wald or LRT test statistics to assess gene significance.
  • Produces results objects containing shrinkage estimates for fold changes to improve reliability with low counts.

Example prompts

  • "Run DESeq2 on my RNA-seq count matrix to find genes upregulated in the disease group compared to healthy controls."
  • "Perform differential expression analysis using DESeq2 and return a table of results sorted by adjusted p-value."
  • "Execute a standard DESeq2 workflow including normalization and result extraction for my paired sample experiment."

Tips & gotchas

Ensure your input data is a properly formatted count matrix with row names as gene identifiers and column names as sample IDs. The skill requires an active R environment configured within the agent's execution context to process the statistical models correctly.