agentsclimarketplace

Radgpt radiology reporter

Skill BioTender-max/awesome-bio-agent-skills/skills/openclaw/radgpt-radiology-reporter

A curated collection of AI agent skills for biomedical research, covering genomics, proteomics, single-cell analysis, clinical AI, and protein design.

Install
npx -y skills add BioTender-max/awesome-bio-agent-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.

One thing 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.

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 -->

name: radgpt-radiology-reporter description: Radiology Reporter keywords:

  • radiology
  • report-generation
  • patient-friendly
  • summarization
  • explanation measurable_outcome: Generate a patient-friendly explanation of a radiology report with <1% hallucination rate within 30 seconds. license: MIT metadata: author: Stanford Medicine version: "1.0.0" compatibility:
  • system: Python 3.9+ allowed-tools:
  • run_shell_command
  • read_file

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.