Clinical Decision SupportSAFE

From the repository's own README, as read at the audited commit. Badges and raw HTML are left out.

clinical-decision-support/
├── SKILL.md                     # Main skill definition
├── README.md                    # This file
│
├── references/                  # Clinical guidance documents
│   ├── patient_cohort_analysis.md
│   ├── treatment_recommendations.md
│   ├── clinical_decision_algorithms.md
│   ├── biomarker_classification.md
│   ├── outcome_analysis.md
│   └── evidence_synthesis.md
│
├── assets/                      # Templates and examples
│   ├── cohort_analysis_template.tex
│   ├── treatment_recommendation_template.tex
│   ├── clinical_pathway_template.tex
│   ├── biomarker_report_template.tex
│   ├── example_gbm_cohort.md
│   ├── recommendation_strength_guide.md
│   └── color_schemes.tex
│
└── scripts/                     # Analysis and generation tools
├── generate_survival_analysis.py
├── create_cohort_tables.py
├── build_decision_tree.py
├── biomarker_classifier.py
└── validate_cds_document.py

> Analyze a cohort of 45 NSCLC patients stratified by PD-L1 expression 
(<1%, 1-49%, ≥50%) including ORR, PFS, and OS outcomes

> Create evidence-based treatment recommendations for HER2-positive 
metastatic breast cancer with GRADE methodology

> Generate a clinical decision algorithm for acute chest

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: clinical-decision-support
description: "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."
---

# 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 statistical analysis (OS, PFS, ORR, DOR, DCR)
- Statistical comparisons between subgroups (hazard ratios, p-values, 95% CI)
- Survival analysis with Kaplan-Meier curves and log-rank tests
- Efficacy tables and waterfall plots
- Comparative effectiveness analyses
- Pharmaceutical cohort reporting (trial subgroups, real-world evidence)

**Treatment Recommendation Reports**
- Evidence-based treatment guidelines for specific disease states
- Strength of recommendation grading (GRADE system: 1A, 1B, 2A, 2B, 2C)
- Quality of evidence assessment (high, moderate, low, very low)
- Treatment algorithm flowcharts with TikZ diagrams
- Line-of-therapy sequencing based on biomarkers
- Decision pathways with clinical and molecular criteria
- Pharmaceutical strategy documents
- Clinical guideline development for medical societies

### Clinical Features

- **Biomarker Integration**: Genomic alterations (mutations, CNV, fusions), gene expression signatures, IHC markers, PD-L1 scoring
- **Statistical Analysis**: Hazard ratios, p-values, confidence intervals, survival curves, Cox regression, log-rank tests
- **Evidence Grading**: GRADE system (1A/1B/2A/2B/2C), Oxford CEBM levels, quality of evidence assessment
- **Clinical Terminology**: SNOMED-CT, LOINC, proper medical nomenclature, trial nomenclature
- **Regulatory Compliance**: HIPAA de-identification, confidentiality headers, ICH-GCP alignment
- **Professional Formatting**: Compact 0.5in margins, color-coded recommendations, publication-ready, suitable for regulatory submissions

## Pharmaceutical and Research Use Cases

This skill is specifically designed for pharmaceutical and clinical research applications:

**Drug Development**
- **Phase 2/3 Trial Analyses**: Biomarker-stratified efficacy and safety analyses
- **Subgroup Analyses**: Forest plots showing treatment effects across patient subgroups
- **Companion Diagnostic Development**: Linking biomarkers to drug response
- **Regulatory Submissions**: IND/NDA documentation with evidence summaries

**Medical Affairs**
- **KOL Education Materials**: Evidence-based treatment algorithms for thought leaders
- **Medical Strategy Documents**: Competitive landscape and positioning strategies
- **Advisory Board Materials**: Cohort analyses and treatment recommendation frameworks
- **Publication Planning**: Manuscript-ready analyses for peer-reviewed journals

**Clinical Guidelines**
- **Guideline Development**: Evidence synthesis with GRADE methodology for specialty societies
- **Consensus Recommendations**: Multi-stakeholder treatment algorithm development
- **Practice Standards**: Biomarker-based treatment selection criteria
- **Quality Measures**: Evidence-based performance metrics

**Real-World Evidence**
- **RWE Cohort Studies**: Retrospective analyses of patient cohorts from EMR data
- **Comparative Effectiveness**: Head-to-head treatment comparisons in real-world settings
- **Outcomes Research**: Long-term survival and safety in clinical practice
- **Health Economics**: Cost-effectiveness analyses by biomarker subgroup

## When to Use

Use this skill when you need to:

- **Analyze patient cohorts** stratified by biomarkers, molecular subtypes, or clinical characteristics
- **Generate treatment recommendation reports** with evidence grading for clinical guidelines or pharmaceutical strategies
- **Compare outcomes** between patient subgroups with statistical analysis (survival, response rates, hazard ratios)
- **Produce pharmaceutical research documents** for drug development, clinical trials, or regulatory submissions
- **Develop clinical practice guidelines** with GRADE evidence grading and decision algorithms
- **Document biomarker-guided therapy selection** at the population level (not individual patients)
- **Synthesize evidence** from multiple trials or real-world data sources
- **Create clinical decision algorithms** with flowcharts for treatment sequencing

**Do NOT use this skill for:**
- Individual patient treatment plans (use `treatment-plans` skill)
- Bedside clinical care documentation (use `treatment-plans` skill)
- Simple patient-specific treatment protocols (use `treatment-plans` skill)

## Document Structure

**CR

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.