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Radgpt radiology reporter

Skill bg-szy/TOP-SKILLS/skills/awesome-skills/radgpt-radiology-reporter

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Install
npx -y skills add bg-szy/TOP-SKILLS --skill radgpt-radiology-reporter

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 4 stars4 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Radiology Reporter

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

1.9 KB, as published. Nobody here has run it

<!-- # COPYRIGHT NOTICE # This file is part of the "Universal Biomedical Skills" project. # Copyright (c) 2026 MD BABU MIA, PhD <[email protected]> # All Rights Reserved. # # This code is proprietary and confidential. # Unauthorized copying of this file, via any medium is strictly prohibited. # # Provenance: Authenticated by MD BABU MIA -->

RadGPT (Radiology Report Assistant)

An LLM-based agent designed to summarize and explain complex radiology reports for patients and clinicians.

When to Use

  • Patient Communication: Converting technical findings into plain language.
  • Clinician Review: Highlighting critical findings (e.g., "Pneumothorax detected").
  • Follow-up: Suggesting appropriate next steps based on findings.

Core Capabilities

  1. Simplification: Translates "bilateral opacity" to "cloudiness in both lungs".
  2. Entity Extraction: Identifies key anatomical structures and pathologies.
  3. Q&A: Answers follow-up questions about the report.

Workflow

  1. Input: Raw text of the radiology report.
  2. Process: LLM summarizes and identifies key findings.
  3. Output: Structured summary or conversational explanation.

Example Usage

User: "Explain this chest X-ray report to the patient."

Agent Action:

python -m radgpt.explain --report ./report.txt --target_audience patient
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.