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clinical-decision-support

Generate clinical decision support documents from biomarker data and trial evidence.

1|Updated Jan 26, 2026
Search Tags:#data-analysis#guidelines#LaTeX#publication#GRADE#clinical-decision-support#biomarker
One-click install
npx skills add https://github.com/Victory-Hugo/S2-Agent-Skill --skill clinical-decision-support-victory-hugo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: clinical-decision-support
Source: https://github.com/Victory-Hugo/S2-Agent-Skill/tree/main/skills/writing/clinical-decision-support
Command: npx skills add https://github.com/Victory-Hugo/S2-Agent-Skill --skill clinical-decision-support-victory-hugo

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scipy, lifelines, matplotlib, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill generates comprehensive clinical decision support (CDS) documents for pharmaceutical companies and clinical researchers, integrating biomarker-driven analyses, evidence grading, and publication-ready formatting.

Core Features & Use Cases

  • Biomarker-driven cohort analyses: stratify patient cohorts by molecular features and compare outcomes using survival analysis, regression, and other statistical methods.
  • GRADE-based guidelines: present evidence strength, quality of evidence, and recommended actions for disease states.
  • Publication-ready reports: deliver LaTeX/PDF reports with executive summaries, figures, tables, and TikZ diagrams suitable for regulatory submissions.
  • Use Case: A biotech team drafts a guideline for HER2+ breast cancer treatment sequencing using trial data and biomarker information.

Quick Start

  1. Load a cohort dataset with biomarker statuses and outcomes.
  2. Run the CDS document generator to produce a publication-ready report template.
  3. Customize sections with disease context, references, and figures.

Frequently Asked Questions about clinical-decision-support

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

FAQPage Schema
How do I generate clinical decision support reports from biomarker data?▼

You can generate clinical decision support reports by loading a cohort dataset with biomarker statuses and outcomes into the report generator, which produces publication-ready LaTeX/PDF outputs with diagrams, tables, and survival analyses.

Can I apply GRADE evidence grading to clinical trial data using Python?▼

Yes, you can apply GRADE-based guidelines to clinical trial data in Python to evaluate evidence strength, quality of evidence, and recommended actions for specific disease states within your generated reports.

How do I create publication-ready LaTeX reports for survival analysis and cohort stratification?▼

You can create publication-ready LaTeX reports by stratifying patient cohorts by molecular features, running survival analysis with lifelines, and exporting the results through customizable LaTeX templates into formatted PDF documents.

Do I need LaTeX installed to produce clinical decision support PDF documents?▼

Yes, LaTeX templates stored in the assets directory are required to render the reproducible PDF outputs, as the skill relies on these templates to format executive summaries, figures, tables, and TikZ diagrams.

What Python libraries are required for biomarker-driven cohort analyses and evidence grading?▼

The required Python libraries for biomarker-driven cohort analyses and evidence grading include pandas for data manipulation, numpy and scipy for statistical computations, lifelines for survival analysis, and matplotlib for figures.

Are there limitations when using this approach for regulatory submission reports?▼

While the skill produces publication-ready documents suitable for regulatory submissions, limitations may arise if your input cohort dataset lacks comprehensive biomarker statuses or complete trial outcome variables needed for statistical regression.

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