<!-- COPYRIGHT NOTICE This file is part of the "Universal Biomedical Skills" project. Copyright c 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu All Rights Reserved. This code is proprietary and confidential. Unauthorized copying of this file, via any medium is strictly prohibited. Provenance:
Install with the open skills CLI (global, non-interactive — available in every Claude Code session):
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill "radgpt-radiology-reporter" -g -a claude-code -yOr manually — copy the SKILL.md below into:
~/.claude/skills/radgpt-radiology-reporter/SKILL.md<!--
# COPYRIGHT NOTICE
# This file is part of the "Universal Biomedical Skills" project.
# Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>
# 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**:
```bash
python -m radgpt.explain --report ./report.txt --target_audience patient
```
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->Give the agent its own dedicated email inbox via AgentMail. Send, receive, and manage email autonomously using agent-owned email addresses (e.g. hermes-agent@agentmail.to).
AudioCraft: MusicGen text-to-music, AudioGen text-to-sound.
Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO).