name: autonomous-oncology-agent
description: Precision Oncology
keywords:
- oncology
- multimodal
- H&E
- biomarkers
- NCCN
measurable_outcome: Generate a prioritized treatment plan with evidence levels and predicted biomarker status (MSI/KRAS) within 5 minutes of data ingest.
license: MIT
metadata:
author: Nature Cancer 2025
version: "1.0.0"
compatibility:
- system: Python 3.9+
allowed-tools:
- run_shell_command
- web_fetch
Autonomous Clinical AI Agent (Oncology)
This skill implements the capabilities of the "Autonomous Clinical AI Agent" described in Nature Cancer (2025). It combines Large Language Models (LLMs) for reasoning with specialized vision models for pathology image analysis to support precision oncology decision-making.
When to Use This Skill
Precision Oncology: For interpreting complex cancer cases involving pathology, genomics, and clinical history.
Biomarker Detection: To identify status of key biomarkers (MSI, KRAS, BRAF) from pathology slides (H&E).
Guideline Adherence: To check treatment plans against NCCN or ASCO guidelines (via OncoKB/PubMed).
Multimodal Synthesis: When you need to combine image data and text reports.
Core Capabilities
Vision Transformer Analysis: Detects MSI status and key mutations (KRAS, BRAF) directly from H&E images.
Clinical Reasoning: Synthesizes patient history, pathology, and genomics to recommend therapies.
Evidence Retrieval: Integrates real-time knowledge from OncoKB and PubMed.
Decision Support: Provides ranked treatment options with evidence levels.
Output: Generate a comprehensive "Tumor Board" style report.
Example Usage
User: "Review this case of metastatic colorectal cancer. The H&E slide is attached. What is the predicted MSI status and recommended first-line therapy?"
Agent Action:
Runs vision model on H&E image -> Output: "MSI-High (Predicted)".
Reads clinical notes -> "Patient is fit, ECOG 0."
Consults Knowledge Base -> "MSI-High CRC responds to Pembrolizumab."
Recommends: "Based on predicted MSI-High status, immunotherapy (Pembrolizumab) is recommended over standard chemotherapy..."
1---2name: autonomous-oncology-agent3description: <!--4---5<!--6# COPYRIGHT NOTICE7# This file is part of the "Universal Biomedical Skills" project.8# Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>9# All Rights Reserved.10#11# This code is proprietary and confidential.12# Unauthorized copying of this file, via any medium is strictly prohibited.13#14# Provenance: Authenticated by MD BABU MIA1516-->1718---19name: autonomous-oncology-agent20description: Precision Oncology21keywords:22 - oncology23 - multimodal24 - H&E25 - biomarkers26 - NCCN27measurable_outcome: Generate a prioritized treatment plan with evidence levels and predicted biomarker status (MSI/KRAS) within 5 minutes of data ingest.28license: MIT29metadata:30 author: Nature Cancer 202531 version: "1.0.0"32compatibility:33 - system: Python 3.9+34allowed-tools:35 - run_shell_command36 - web_fetch37---3839# Autonomous Clinical AI Agent (Oncology)4041This skill implements the capabilities of the "Autonomous Clinical AI Agent" described in Nature Cancer (2025). It combines Large Language Models (LLMs) for reasoning with specialized vision models for pathology image analysis to support precision oncology decision-making.4243## When to Use This Skill4445* **Precision Oncology**: For interpreting complex cancer cases involving pathology, genomics, and clinical history.46* **Biomarker Detection**: To identify status of key biomarkers (MSI, KRAS, BRAF) from pathology slides (H&E).47* **Guideline Adherence**: To check treatment plans against NCCN or ASCO guidelines (via OncoKB/PubMed).48* **Multimodal Synthesis**: When you need to combine image data and text reports.4950## Core Capabilities51521. **Vision Transformer Analysis**: Detects MSI status and key mutations (KRAS, BRAF) directly from H&E images.532. **Clinical Reasoning**: Synthesizes patient history, pathology, and genomics to recommend therapies.543. **Evidence Retrieval**: Integrates real-time knowledge from OncoKB and PubMed.554. **Decision Support**: Provides ranked treatment options with evidence levels.5657## Workflow58591. **Input Processing**:60 * Text: Clinical notes, pathology reports, genomic panels.61 * Image: H&E histology slides.622. **Analysis**:63 * Vision model predicts molecular features from slides.64 * LLM extracts key clinical entities (Stage, Histology, Mutations).653. **Reasoning**:66 * Query OncoKB for actionable mutations.67 * Match against standard of care guidelines.684. **Output**: Generate a comprehensive "Tumor Board" style report.6970## Example Usage7172**User**: "Review this case of metastatic colorectal cancer. The H&E slide is attached. What is the predicted MSI status and recommended first-line therapy?"7374**Agent Action**:751. Runs vision model on H&E image -> Output: "MSI-High (Predicted)".762. Reads clinical notes -> "Patient is fit, ECOG 0."773. Consults Knowledge Base -> "MSI-High CRC responds to Pembrolizumab."784. Recommends: "Based on predicted MSI-High status, immunotherapy (Pembrolizumab) is recommended over standard chemotherapy..."798081<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->
Run npx skillmds@latest add biotender-max/autonomous-oncology-agent in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.
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