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 scientific-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 for pharmaceutical companies and clinical researchers, focusing on group-level analyses and evidence synthesis rather than individual patient plans.
How it works
- Produces two main document types: Patient Cohort Analysis and Treatment Recommendation Reports.
- Includes biomarker integration, statistical analyses (hazard ratios, survival curves, waterfall plots), and GRADE-based evidence grading.
- Outputs publication-ready LaTeX/PDF files, with regulatory-submission compatibility and publication formatting guidance.
- Requires generation of AI-generated figures using the scientific-schematics skill: create at least 1-2 figures (diagrams, flowcharts, or patient flow) prior to finalization.
- Uses TikZ diagrams for treatment algorithms and flowcharts; includes Kaplan-Meier curves and forest plots as examples of statistical visuals.
- Emphasizes population-level analyses and evidence synthesis; explicitly notes that bedside, individual patient treatment plans are outside its scope.
When to use it
- When needing biomarker-stratified patient cohort analyses with statistical outcomes.
- When generating evidence-based treatment recommendation reports with GRADE grading and decision algorithms.
- When producing pharmaceutical research documents for drug development, trials, or regulatory submissions.
- When documenting biomarker-guided therapy selection at the population level and synthesizing evidence from multiple trials or real-world data.
What it can touch
- Generates LaTeX/PDF documents with publication-ready formatting.
- Incorporates figures produced via the scientific-schematics workflow, described to generate schematics programmatically.
Caveats
- Do not use for individual bedside treatment planning; intended for population-level analyses.
- Requires the integration of regulatory-compliant elements (HIPAA de-identification, ICH-GCP alignment) as described in the capabilities.
- Mandates at least 1-2 AI-generated figures created with the scientific-schematics skill before finalizing documents.
# 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 majiayu000/claude-skill-registry --skill scientific-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.