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Tooluniverse Image Analysis

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

Helps with image processing, analysis as part of agent workflows workflows.

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-image-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

    Only contacts known or declared hosts: huggingface.co.

    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

    huggingface.comodel provider

  • 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-image-analysis/SKILL.md
Scanned
31 files, 12,810 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

What it does

This skill, Tooluniverse Image Analysis, enables AI agents to analyze microscopy-derived measurement data and perform quantitative imaging analysis. It utilizes libraries like pandas, numpy, scipy, statsmodels, and scikit-image to process data from image analysis software such as ImageJ, CellProfiler, or QuPath. The skill prioritizes a data-first approach, emphasizing loading and inspecting data before performing statistical analyses.

When to use it

This skill is useful for:

  • Analyzing microscopy measurement data (area, circularity, intensity, cell counts) stored in CSV/TSV files.
  • Performing colony morphometry or cell counting statistics.
  • Conducting fluorescence quantification and statistical comparisons like t-tests, ANOVA, Dunnett's tests, Mann-Whitney tests, and power analysis.
  • Building regression models (polynomial, spline) for dose-response or ratio data.

Key capabilities

  • Loads and inspects CSV/TSV microscopy measurement data.
  • Performs statistical comparisons (t-tests, ANOVA, Dunnett's test, Mann-Whitney).
  • Calculates effect sizes (Cohen’s d) and performs power analysis.
  • Builds regression models (polynomial, spline) for dose-response or ratio optimization.
  • Understands common ImageJ/CellProfiler columns like Area, Circularity, Round, and Intensity.

Example prompts

  • "What is the mean circularity of genotype X?"
  • "Perform a t-test comparing NeuN counts between condition A and condition B."
  • "Build a polynomial regression model for this dose-response data."

Tips & gotchas

  • The skill requires microscopy measurement data in CSV/TSV format. It is not suitable for raw image files (TIFF, PNG) directly – those require segmentation and measurement steps first.
  • It's designed specifically for imaging context; avoid using it for statistics without a connection to image analysis.
  • Always load and inspect the data before requesting specific analyses to ensure accuracy and understanding of the dataset.