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clinical-decision-support

Generate publication-ready clinical decision support documents with GRADE evidence grading and statistical analyses.

75|7|Updated Feb 14, 2026
Search Tags:#latex#cohort-analysis#evidence-synthesis#GRADE#clinical-decision-support#biomarker
One-click install
npx skills add https://github.com/jiaxiaojunQAQ/SkillJect --skill clinical-decision-support-jiaxiaojunqaq
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: clinical-decision-support
Source: https://github.com/jiaxiaojunQAQ/SkillJect/tree/main/data/skills_sample/clinical-decision-support
Command: npx skills add https://github.com/jiaxiaojunQAQ/SkillJect --skill clinical-decision-support-jiaxiaojunqaq

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?

Generate professional clinical decision support documents for pharmaceutical companies, clinical researchers, and medical decision-makers. This skill specializes in analytical, evidence-based outputs that inform population-level decision-making, including biomarker-guided analyses, GRADE-based evidence grading, and regulatory-compliant reporting.

Core Features & Use Cases

  • Biomarker-stratified patient cohort analyses with statistical comparisons
  • Evidence-based treatment recommendations with GRADE graded outputs
  • Publication-ready LaTeX/PDF documents including executive summaries, figures, and tables for regulatory submissions
  • Integration with decision algorithms and TikZ flowcharts for clinical pathways

Quick Start

Provide the AI with a data-backed prompt to generate a CDS document.

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 publication-ready clinical decision support documents from cohort data?▼

Biomarker stratification in clinical decision support involves dividing patient cohorts by specific biological markers to apply statistical comparisons, such as hazard ratios and survival analyses, for tailored population-level medical affairs outputs.

Can I use Python lifelines for survival analysis in regulatory submission reports?▼

Yes, this skill leverages the Python lifelines library to perform survival analyses and generate forest plots, integrating these statistical results into structured LaTeX/PDF documents designed for pharmaceutical research and regulatory compliance.

What is GRADE-based evidence grading and how does it apply to clinical pathway recommendations?▼

To create clinical decision support documents, provide a data-backed prompt to the AI. The skill will then identify, rank, and assemble structured analyses using pandas and numpy, outputting a complete LaTeX/PDF document with figures and tables.

Does this skill require pandas and scipy for biomarker-guided cohort analysis?▼

Yes, this skill depends on pandas, numpy, and scipy to execute rigorous statistical methods for biomarker-guided cohort analyses, ensuring robust data processing before assembling the final publication-ready clinical decision support outputs.

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