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Medical Imaging Review

Listed
luwill·vlatest·other

Analyzes medical images like X-rays and MRIs to highlight potential anomalies for clinician review.

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 luwill/research-skills --skill medical-imaging-review -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

luwill

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 2 hosts that are neither well-known infrastructure nor declared in the skill's frontmatter: doi.org, www.fda.gov.

    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

    doi.orgundeclaredwww.fda.govundeclared

    What it found

    • medical-imaging-review/tests/test_audit_manuscript.py:654contacts doi.org
      "access_uri": "https://doi.org/" + source_id.removeprefix("doi:"),
    • medical-imaging-review/tests/test_audit_manuscript.py:1290contacts www.fda.gov
      "access_uri": "https://www.fda.gov/example",

  • 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 luwill/research-skills at b083e473fb.

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

Commit
b083e473fb
Manifest
medical-imaging-review/SKILL.md
Scanned
33 files, 9,651 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 enables AI agents to analyze and review medical imaging data, assisting in the detection of anomalies within diagnostic scans. It supports researchers and clinicians in streamlining the evaluation process for various imaging modalities.

When to use it

  • Automating the preliminary screening of X-rays or CT scans to flag potential abnormalities.
  • Assisting radiologists in cross-referencing new patient data against historical imaging records.
  • Supporting research studies that require high-volume analysis of MRI datasets for pattern recognition.
  • Validating image quality and alignment before further diagnostic processing occurs.

Key capabilities

  • Reviewing diverse medical imaging formats for diagnostic accuracy.
  • Identifying specific anomalies and structural irregularities within scan data.
  • Providing structured feedback on image interpretation to support clinical decision-making.

Example prompts

  • "Review this chest X-ray for signs of pneumonia or fluid accumulation."
  • "Analyze the provided MRI sequence to identify any lesions in the brain tissue."
  • "Compare these two CT scans to highlight changes in tumor size over time."

Tips & gotchas

Ensure all input images are de-identified and comply with local data privacy regulations before processing. While this tool aids analysis, it should not replace final clinical judgment by a qualified medical professional.