catastrophe-modeling

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.

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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).

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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.

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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.

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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.

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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.

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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.

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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.

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============================================================ 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] |

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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.

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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.