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+# Clinical Decision Support Skill
+
+Professional clinical decision support documents for medical professionals in pharmaceutical and clinical research settings.
+
+## Quick Start
+
+This skill enables generation of three types of clinical documents:
+
+1. **Individual Patient Treatment Plans** - Personalized protocols for specific patients
+2. **Patient Cohort Analysis** - Biomarker-stratified group analyses with outcomes
+3. **Treatment Recommendation Reports** - Evidence-based clinical guidelines
+
+All documents are generated as compact, professional LaTeX/PDF files.
+
+## Directory Structure
+
+```
+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
+```
+
+## Example Use Cases
+
+### Create a Patient Cohort Analysis
+```
+> Analyze a cohort of 45 NSCLC patients stratified by PD-L1 expression 
+  (<1%, 1-49%, ≥50%) including ORR, PFS, and OS outcomes
+```
+
+### Generate Treatment Recommendations
+```
+> Create evidence-based treatment recommendations for HER2-positive 
+  metastatic breast cancer with GRADE methodology
+```
+
+### Build Clinical Pathway
+```
+> Generate a clinical decision algorithm for acute chest pain 
+  management with TIMI risk score
+```
+
+## Key Features
+
+- **GRADE Methodology**: Evidence quality grading (High/Moderate/Low/Very Low)
+- **Recommendation Strength**: Strong (Grade 1) vs Conditional (Grade 2)
+- **Biomarker Integration**: Genomic, expression, and molecular subtype classification
+- **Statistical Analysis**: Kaplan-Meier, Cox regression, log-rank tests
+- **Guideline Concordance**: NCCN, ASCO, ESMO, AHA/ACC integration
+- **Professional Output**: 0.5in margins, color-coded boxes, publication-ready
+
+## Dependencies
+
+Python scripts require:
+- `pandas`, `numpy`, `scipy`: Data analysis and statistics
+- `lifelines`: Survival analysis (Kaplan-Meier, Cox regression)
+- `matplotlib`: Visualization
+- `pyyaml` (optional): YAML input for decision trees
+
+Install with:
+```bash
+pip install pandas numpy scipy lifelines matplotlib pyyaml
+```
+
+## References Included
+
+1. **Patient Cohort Analysis**: Stratification methods, biomarker correlations, statistical comparisons
+2. **Treatment Recommendations**: Evidence grading, treatment sequencing, special populations
+3. **Clinical Decision Algorithms**: Risk scores, decision trees, TikZ flowcharts
+4. **Biomarker Classification**: Genomic alterations, molecular subtypes, companion diagnostics
+5. **Outcome Analysis**: Survival methods, response criteria (RECIST), effect sizes
+6. **Evidence Synthesis**: Guideline integration, systematic reviews, meta-analysis
+
+## Templates Provided
+
+1. **Cohort Analysis**: Demographics table, biomarker profile, outcomes, statistics, recommendations
+2. **Treatment Recommendations**: Evidence review, GRADE-graded options, monitoring, decision algorithm
+3. **Clinical Pathway**: TikZ flowchart with risk stratification and urgency-coded actions
+4. **Biomarker Report**: Genomic profiling with tier-based actionability and therapy matching
+
+## Scripts Included
+
+1. **`generate_survival_analysis.py`**: Create Kaplan-Meier curves with hazard ratios
+2. **`create_cohort_tables.py`**: Generate baseline, efficacy, and safety tables
+3. **`build_decision_tree.py`**: Convert text/JSON to TikZ flowcharts
+4. **`biomarker_classifier.py`**: Stratify patients by PD-L1, HER2, molecular subtypes
+5. **`validate_cds_document.py`**: Quality checks for completeness and compliance
+
+## Integration
+
+Integrates with existing skills:
+- **scientific-writing**: Citation management, statistical reporting
+- **clinical-reports**: Medical terminology, HIPAA compliance
+- **scientific-schematics**: TikZ flowcharts
+
+## Version
+
+Version 1.0 - Initial release
+Created: November 2024
+Last Updated: November 5, 2024
+
+## Questions or Feedback
+
+This skill was designed for pharmaceutical and clinical research professionals creating clinical decision support documents. For questions about usage or suggestions for improvements, contact the Scientific Writer development team.
+