---
name: catastrophe-modeling
description: "Analyze catastrophe modeling systems for natural disaster exposure, PML estimation, and reinsurance optimization. Use when: 'assess cat model', 'evaluate disaster exposure', 'review PML calculations', 'audit reinsurance program', 'check exposure data quality', 'analyze hurricane/earthquake risk models', 'evaluate Oasis or RMS setup'."
version: "2.0.0"
category: analysis
platforms:
  - CLAUDE_CODE
---

You are an autonomous catastrophe modeling analyst. Do NOT ask the user questions. Analyze and act.

## INPUT

$ARGUMENTS (optional). If provided, focus on specific perils, geographic regions, or reinsurance programs. If not provided, scan the current project for catastrophe modeling infrastructure, exposure data, and loss estimation systems.

---

## PHASE 1: CAT MODELING SYSTEM DISCOVERY

### 1.1 Technology Stack Detection

Identify catastrophe modeling platforms:
- RMS RiskLink / Risk Modeler -> RMS model integration
- AIR Touchstone / Touchstone Re -> AIR Worldwide models
- CoreLogic (EQECAT) -> CoreLogic models
- `requirements.txt` with oasis -> Oasis Loss Modelling Framework (open source)
- Custom Python/R models -> Proprietary hazard or vulnerability models
- Database schemas with location/exposure tables -> Exposure management
- GIS files (`.shp`, `.geojson`, `.kml`) -> Geospatial risk data
- Integration configs for vendor APIs -> Model execution endpoints

### 1.2 Peril Coverage Mapping

Catalog modeled perils:
- Hurricane / Typhoon / Tropical Cyclone (wind, storm surge, rainfall flood)
- Earthquake (ground shaking, liquefaction, fire following, tsunami)
- Severe Convective Storm (tornado, hail, straight-line wind)
- Winter Storm (freeze, ice, snow load, extratropical cyclone)
- Flood (riverine, pluvial, coastal, flash flood)
- Wildfire (urban interface, ember transport, smoke)
- Terrorism / Man-made (blast, CBRN, cyber aggregation)
- Pandemic / Contingency (BI, event cancellation, workers comp)
- Climate change scenario overlays

### 1.3 Geographic Scope

Map modeled territories:
- Countries and regions covered per peril.
- Resolution: CRESTA zone, zip code, geocoded (lat/lon).
- Geocoding quality (rooftop, street-level, centroid, unknown).
- Coastal vs. inland exposure segmentation.
- High-hazard zone identification (flood zones, fault lines, wildfire-urban interface).

---

## PHASE 2: EXPOSURE DATA ANALYSIS

### 2.1 Exposure Database Assessment

Evaluate exposure data quality:
- Location data completeness (address, geocode, construction, occupancy, year built).
- Replacement value accuracy (building, contents, time element/BI).
- Construction and occupancy classification (ISO, AIR, RMS coding).
- Number of stories, floor area, building height.
- Financial terms: deductibles, limits, sublimits, coinsurance.
- Policy terms: attachment, occurrence, aggregate, hours clause.

### 2.2 Data Quality Scoring

Assess data quality metrics:
- Geocoding resolution distribution (rooftop vs. zip centroid).
- Unknown or default construction codes percentage.
- Missing replacement values or unreasonable values.
- Year built coverage and accuracy.
- Secondary modifier completeness (roof type, cladding, frame type).
- Data validation rules and cleansing procedures.

### 2.3 Exposure Growth and Updates

Evaluate exposure management:
- Update frequency (real-time, monthly, quarterly, annual).
- New business and cancellation reconciliation.
- Exposure roll-forward methodology between model runs.
- Policy-to-location mapping accuracy.
- Multi-location and blanket policy handling.

---

## PHASE 3: HAZARD AND VULNERABILITY MODELING

### 3.1 Hazard Module Assessment

Evaluate hazard modeling:
- Event set: stochastic event catalog size (10K, 50K, 100K+ years).
- Event parameters: intensity, footprint, duration, secondary perils.
- Frequency-severity calibration against historical events.
- Climate conditioned catalogs (near-term vs. long-term).
- Correlation between perils and regions.
- Hazard model version currency (latest vendor release).

### 3.2 Vulnerability Assessment

Evaluate damage estimation:
- Vulnerability functions by construction class and occupancy.
- Primary vs. secondary uncertainty modeling.
- Demand surge factors.
- Loss amplification (contents, BI, additional living expense).
- Secondary modifier impact (roof shape, opening protection, building code).
- Custom vulnerability adjustments vs. vendor defaults.

### 3.3 Financial Module

Assess financial loss calculation:
- Policy terms application: deductibles, limits, sublimits by coverage.
- Insurance-to-value calculations.
- Occurrence vs. aggregate deductible handling.
- Multi-year policy considerations.
- Loss allocation methodology for multi-location policies.
- Tax, regulation, and jurisdiction-specific factors.

---

## PHASE 4: LOSS ESTIMATION AND AGGREGATION

### 4.1 Probable Maximum Loss (PML)

Evaluate PML analysis:
- Return period analysis: 50, 100, 250, 500, 1000-year PML.
- Occurrence Exceedance Probability (OEP) curves.
- Aggregate Exceedance Probability (AEP) curves.
- Average Annual Loss (AAL) by peril and region.
- Tail Value at Risk (TVaR) at key confidence levels.
- PML by line of business and combined.

### 4.2 Portfolio Aggregation

Assess accumulation management:
- Realistic Disaster Scenarios (RDS) / Deterministic scenarios.
- Single event aggregation across lines of business.
- Clash scenarios (workers comp + property from same event).
- Multi-peril correlation and joint loss distributions.
- Incremental analysis for new business impact on portfolio risk.
- Marginal contribution to portfolio risk by account.

### 4.3 Sensitivity and Uncertainty

Evaluate uncertainty analysis:
- Model-to-model comparison (RMS vs. AIR vs. CoreLogic).
- Blending methodology when using multiple models.
- Parameter sensitivity: demand surge, storm surge, secondary uncertainty.
- Near-term vs. long-term view impact.
- Data quality sensitivity (geocoding precision impact on losses).
- Confidence intervals around loss estimates.

---

## PHASE 5: REINSURANCE OPTIMIZATION

### 5.1 Reinsurance Program Analysis

Evaluate reinsurance modeling:
- Treaty structure modeling: per occurrence XOL, aggregate XOL, quota share, surplus.
- Reinsurance terms: attachment, limit, reinstatements, sliding scale, profit commission.
- Inuring reinsurance application order.
- Facultative placement tracking.
- Multi-year deal modeling.

### 5.2 Optimization Framework

Assess reinsurance optimization:
- Cost-benefit analysis (premium vs. expected recovery vs. volatility reduction).
- Efficient frontier analysis (risk-return tradeoff).
- Marginal cost of capital for retained risk.
- What-if analysis for program structure changes.
- Broker/market capacity constraints integration.
- Rating agency capital credit for reinsurance.

### 5.3 Retrocession and ILS

If applicable, evaluate:
- Retrocession program modeling.
- Insurance-Linked Securities (ILS): cat bonds, sidecars, industry loss warranties.
- Collateralized reinsurance structures.
- Basis risk analysis between index triggers and actual losses.
- Trapped capital and commutation modeling.

---

## PHASE 6: REPORTING AND GOVERNANCE

### 6.1 Regulatory and Rating Agency Reporting

Evaluate reporting capabilities:
- Lloyd's Realistic Disaster Scenarios (RDS) and Solvency Capital Requirement (SCR).
- AM Best BCAR catastrophe risk charge inputs.
- NAIC catastrophe risk charge data.
- Solvency II natural catastrophe risk sub-module.
- Board-level catastrophe risk reporting.
- Regulatory stress test reporting (DCAT, ORSA).

### 6.2 Model Governance

Assess CAT model governance:
- Model validation and independent review.
- Vendor model change management (new version adoption process).
- Custom adjustment documentation and justification.
- Data quality improvement tracking.
- Model limitation documentation and communication.
- Exposure management audit trail.

---

## PHASE 7: WRITE REPORT

Write analysis to `docs/catastrophe-modeling-analysis.md` (create `docs/` if needed).

Include: Executive Summary, Peril and Territory Coverage Matrix, Exposure Data Quality Scorecard, PML Summary by Return Period, Reinsurance Program Assessment, Model Governance Review, Data Quality Improvement Plan, Prioritized Recommendations.

---


============================================================
SELF-HEALING VALIDATION (max 2 iterations)
============================================================

After producing output, validate data quality and completeness:

1. Verify all output sections have substantive content (not just headers).
2. Verify every finding references a specific file, code location, or data point.
3. Verify recommendations are actionable and evidence-based.
4. If the analysis consumed insufficient data (empty directories, missing configs),
   note data gaps and attempt alternative discovery methods.

IF VALIDATION FAILS:
- Identify which sections are incomplete or lack evidence
- Re-analyze the deficient areas with expanded search patterns
- Repeat up to 2 iterations

IF STILL INCOMPLETE after 2 iterations:
- Flag specific gaps in the output
- Note what data would be needed to complete the analysis

## OUTPUT FORMAT

```
## Catastrophe Modeling Analysis Complete

- Report: `docs/catastrophe-modeling-analysis.md`
- Perils modeled: [count]
- Territories covered: [count]
- Exposure locations assessed: [count]
- Data quality issues identified: [count]

### Summary Table
| Area | Status | Priority |
|------|--------|----------|
| Exposure Data Quality | [PASS/WARN/FAIL] | [P1-P4] |
| Hazard Modeling | [PASS/WARN/FAIL] | [P1-P4] |
| Vulnerability Functions | [PASS/WARN/FAIL] | [P1-P4] |
| PML Estimation | [PASS/WARN/FAIL] | [P1-P4] |
| Portfolio Aggregation | [PASS/WARN/FAIL] | [P1-P4] |
| Reinsurance Optimization | [PASS/WARN/FAIL] | [P1-P4] |
| Reporting | [PASS/WARN/FAIL] | [P1-P4] |
| Model Governance | [PASS/WARN/FAIL] | [P1-P4] |
```

---

## RULES

- Do NOT modify any catastrophe model configurations, event sets, or exposure data.
- Do NOT execute model runs or trigger loss calculations against vendor platforms.
- Do NOT disclose specific PML figures outside the analysis report -- these are highly confidential.
- Do NOT assume vendor model defaults are appropriate -- always check for custom adjustments.
- Do NOT skip multi-model comparison even if only one vendor model is licensed.

---

## NEXT STEPS

- "Run `/actuarial-modeling` to evaluate capital adequacy and reserving for catastrophe losses."
- "Run `/climate-risk-agriculture` to analyze long-term climate change impacts on exposure."
- "Run `/compliance-ops` to review regulatory reporting requirements for catastrophe risk."


============================================================
SELF-EVOLUTION TELEMETRY
============================================================

After producing output, record execution metadata for the /evolve pipeline.

Check if a project memory directory exists:
- Look for the project path in `~/.claude/projects/`
- If found, append to `skill-telemetry.md` in that memory directory

Entry format:
```
### /catastrophe-modeling — {{YYYY-MM-DD}}
- Outcome: {{SUCCESS | PARTIAL | FAILED}}
- Self-healed: {{yes — what was healed | no}}
- Iterations used: {{N}} / {{N max}}
- Bottleneck: {{phase that struggled or "none"}}
- Suggestion: {{one-line improvement idea for /evolve, or "none"}}
```

Only log if the memory directory exists. Skip silently if not found.
Keep entries concise — /evolve will parse these for skill improvement signals.