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 majiayu000/claude-skill-registry --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 clinical decision support documents focused on analytical, evidence-based, population-level analyses for pharmaceutical research and medical decision-making. It covers patient cohort analyses with biomarker stratification and treatment recommendation reports that incorporate GRADE grading, and supports outputs in publication-ready LaTeX/PDF format optimized for drug development, regulatory submissions, and guideline development.
How it works
- Produces two main document types: (1) Patient Cohort Analysis with biomarker-based stratification, outcome metrics, and statistical comparisons; (2) Treatment Recommendation Reports with evidence-based guidelines, GRADE grading, and decision algorithms.
- Includes capabilities for biomarker integration (genomic alterations, IHC, PD-L1), statistical analyses (hazard ratios, confidence intervals, survival analyses), and adherence to regulatory concepts (HIPAA de-identification, ICH-GCP alignment).
- Outputs are publication-ready LaTeX/PDF files with structured sections, executive summaries, and professional formatting (margins, color-coded boxes, TikZ diagrams, and figures such as Kaplan-Meier curves, forest plots, and waterfall plots).
- Emphasizes population-level analyses and evidence synthesis, not individual patient care planning; directs users to the treatment-plans skill for bedside or patient-specific tasks.
When to use it
- When you need cohort analyses stratified by biomarkers or molecular subtypes (e.g., GBM subtypes, gene expression profiles).
- When you require treatment recommendation reports with GRADE grading and decision algorithms for guidelines, regulatory submissions, or medical affairs materials.
- When synthesizing evidence from multiple trials or real-world data to produce publication-ready documents for pharmaceutical research and policy development.
What it can touch
- Tools: Read, Write, Edit, Bash
- Data inputs and outputs are structured to generate LaTeX/PDF documents suitable for publication and regulatory contexts.
Caveats
- Outputs are publication-ready documents intended for pharmaceutical research and guideline development; individual bedside treatment plans are outside this skill’s scope and should use the treatment-plans skill.
- This skill operates within the MIT-licensed framework and assumes de-identification and regulatory alignment as described in the capability notes.
# 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. ## Capabilities ### Document Types **Patient Cohort Analysis** - Biomarker-based patient stratification (molecular subtypes, gene expression, IHC) - Molecular subtype classification (e.g., GBM mesenchymal-immune-active vs proneural, breast cancer subtypes) - Outcome metrics with stat
- Description
- Capabilities
- Document Types
- Clinical Features
- Pharmaceutical and Research Use Cases
- When to Use
- 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
- Example Usage
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 majiayu000/claude-skill-registry --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 majiayu000/claude-skill-registry, a repository with 534 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.