Skip to content
TrustedSkillsbeta
Back to all skills

Tooluniverse Crispr Screen Analysis

Checked
mims-harvard·vlatest·other·Apache-2.0

Analyzes CRISPR screen data from ToolUniverse experiments to identify gene essentiality and potential therapeutic targets.

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-crispr-screen-analysis -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 passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • 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 undeclared network calls: passed

    Makes no network calls.

    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.

  • 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-crispr-screen-analysis/SKILL.md
Scanned
27 files, 5,664 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

tooluniverse-crispr-screen-analysis

What it does

This skill enables AI agents to analyze CRISPR screen data, helping researchers interpret genetic perturbation results. It processes raw screening outputs to identify significant gene hits and potential biological mechanisms.

When to use it

  • Analyzing large-scale CRISPR knockout or activation datasets to find disease-associated genes.
  • Identifying candidate therapeutic targets from high-throughput genetic screening results.
  • Validating experimental hypotheses by correlating screen hits with known pathway databases.
  • Automating the initial interpretation of wet-lab CRISPR experiments before manual review.

Key capabilities

  • Processes raw CRISPR screen data formats (e.g., count matrices, enrichment scores).
  • Identifies statistically significant gene hits based on user-defined thresholds.
  • Integrates with external biological knowledge bases for functional annotation.
  • Generates summary reports highlighting top candidates and confidence scores.

Example prompts

  • "Analyze this CRISPR screen dataset and list the top 10 most significant gene hits."
  • "Interpret these enrichment scores and suggest potential pathways involved in cell survival."
  • "Compare the results of two different CRISPR screens to find overlapping gene targets."

Tips & gotchas

Ensure your input data is properly formatted (e.g., tab-separated count matrices) before analysis. This skill works best when combined with domain-specific knowledge to validate biological relevance of identified hits.