founder-mode-oncology

Design personalized cancer treatment plans from genomic and liquid biopsy data.

3|2|Updated Mar 17, 2026

Search Tags:#bioinformatics #oncology #neoantigen #immunotherapy #personalized medicine #ctdna #radioligand

Ready to run

npx skills add https://github.com/broomva/skills --skill founder-mode-oncology

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: founder-mode-oncology
Source: https://github.com/broomva/skills/tree/main/skills/healthcare/founder-mode-oncology
Command: npx skills add https://github.com/broomva/skills --skill founder-mode-oncology

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill provides a systematic, reproducible framework for navigating complex, personalized cancer treatment, moving beyond ad-hoc decision-making to a data-driven, multi-modal approach.

Core Features & Use Cases

Maximal Diagnostics: Orchestrates a comprehensive diagnostic stack including WGS, scRNA-seq, and liquid biopsy to identify non-obvious therapeutic targets.
Parallel Therapeutic Development: Guides the design of personalized combinations, including neoantigen vaccines, radioligand therapies, and cell therapies, while navigating FDA expanded access.
Real-time Monitoring: Implements a rigorous monitoring cadence using ctDNA and serial scRNA-seq to measure response and adapt treatment in real time.

Quick Start

Use the founder-mode-oncology skill to design a diagnostic and therapeutic strategy for a specific cancer case based on the provided molecular data.

Frequently Asked Questions about founder-mode-oncology

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I design a personalized cancer treatment strategy using genomic and liquid biopsy data?▼

Design a personalized cancer treatment strategy by integrating maximal diagnostics, parallel therapeutic development, and real-time monitoring. This framework requires genomic, transcriptomic, and liquid biopsy data to orchestrate a systematic, data-driven approach for patient-specific therapeutic combinations.

What is parallel therapeutic development for personalized oncology?▼

Parallel therapeutic development in personalized oncology is the simultaneous design of patient-specific treatments. It guides the creation of personalized neoantigen vaccines, radioligand therapies, and immune modulators while navigating FDA expanded access protocols.

How does ctDNA monitoring adapt immunotherapy for cancer patients?▼

ctDNA monitoring adapts immunotherapy by implementing a rigorous tracking cadence alongside serial scRNA-seq. This real-time monitoring measures patient response and facilitates immediate treatment adaptations based on circulating tumor DNA levels.

Can I use this framework to design neoantigen vaccines from scRNA-seq and WGS data?▼

Yes, you can use this framework to design neoantigen vaccines from scRNA-seq and WGS data. The maximal diagnostics pillar orchestrates a comprehensive diagnostic stack to identify non-obvious therapeutic targets for patient-specific vaccine development.

What data is needed to navigate personalized cancer treatment combinations?▼

To navigate personalized cancer treatment combinations, you need access to genomic, transcriptomic, and liquid biopsy data. This multi-modal data executes the three-pillar oncology methodology for maximal diagnostics and real-time monitoring.

When should I use a radioligand therapy framework for oncology cases?▼

Use a radioligand therapy framework for oncology cases requiring personalized combinations alongside neoantigen vaccines and immune modulators. It is indicated when comprehensive diagnostic stacks identify specific non-obvious therapeutic targets for parallel development.