clinical-decision-support
Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis.
npx skills add LeonChaoX/qinyan-academic-skills --skill clinical-decision-support --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Generates professional CDS documents for pharmaceutical companies and clinical researchers, focusing on group-level analyses and evidence synthesis rather than individual patient plans. Produces two main document types: (1) Patient Cohort Analysis with biomarker-based stratification and statistical outcome comparisons, and (2) Treatment Recommendation Reports featuring evidence-based guidelines with GRADE grading and decision algorithms. All outputs are publication-ready LaTeX/PDF tailored for drug development, clinical research, and regulatory submissions.
How it works
The skill structures documents with explicit content sections including biomarker integration, statistical analyses (hazard ratios, p-values, confidence intervals, survival analyses), and guidance on regulatory compliance. It supports the inclusion of executive summaries, methodological details, and visual elements (e.g., flow diagrams, Kaplan-Meier plots, forest plots) via a formal document structure designed for journals and regulatory submissions. It mandates the use of AI-generated schematic figures created through the scientific-schematics workflow and requires the first page to contain a complete executive summary with defined colored boxes and bullet points. It emphasizes publication-ready formatting, GRADE methodology, and biomarker-driven decision criteria, and it documents when to use this skill versus bedside-treatment planning.
When to use it
Use when you need biomarker-guided population analyses, evidence-based treatment recommendations with GRADE, and publication-ready CDS documents for pharmaceutical development, regulatory submissions, or clinical guideline development. Do not use for individual patient treatment plans or bedside documentation.
What it can touch
This skill leverages the allowed-tools Read, Write, Edit, Bash to perform data processing, document generation, and schematic figure creation. It requires integration with the scientific-schematics workflow to generate diagrams via the described commands and ensures figures are stored in the figures/ directory as part of the LaTeX/PDF output.
Caveats
License: MIT License. Documents must begin with a complete executive summary on page 1, using the specified tcolorbox structure and color conventions. The skill requires adherence to publication-ready formatting and GRADE methodology; it does not substitute for actual clinical decision-making at the patient level.
# Clinical Decision Support Documents ## Description Generate professional clinical decision support (CDS) documents for pharmaceutical companies, clinical researchers, and medical decision-makers. This skill specializes in analytical, evidence-based documents that inform treatment strategies and drug development: 1. **Patient Cohort Analysis** - Biomarker-stratified group analyses with statistical outcome comparisons 2. **Treatment Recommendation Reports** - Evidence-based clinical guidelines with GRADE grading and decision algorithms All documents are generated as publication-ready LaTeX/PDF files optimized for pharmaceutical research, regulatory submissions, and clinical guideline development. **Note:** For individual patient treatment plans at the bedside, use the `treatment-plans` skill instead. This skill focuses on group-level analyses and evidence synthesis for pharmaceutical/research settings. **Writing Style:** For publication-ready documents targeting medical journals, consult the **venue-templates** skill's `medical_journal_styles.md` for guidance on structured abstracts, evidence language, and CONSORT/STROBE compliance. ## Capabilities ### Document Types **Patient Coho
- Description
- Capabilities
- Document Types
- Clinical Features
- Pharmaceutical and Research Use Cases
- When to Use
- Visual Enhancement with Scientific Schematics
- Document Structure
- Page 1 Executive Summary Structure
- Patient Cohort Analysis (Detailed Sections - Page 3+)
- Treatment Recommendation Reports (Detailed Sections - Page 3+)
- Output Format
- Integration
- Key Differentiators from Treatment-Plans Skill
python scripts/generate_schematic.py "your diagram description" -o figures/output.png
What does the clinical-decision-support skill do?
Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis.
How do I install it?
Run `npx skills add LeonChaoX/qinyan-academic-skills --skill clinical-decision-support --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From LeonChaoX/qinyan-academic-skills, a repository with 759 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.