# Geo Infer Risk

> Geospatial risk modeling including catastrophe models, exposure analysis, and underwriting. Use when assessing spatial risk, building catastrophe models, analyzing exposure/hazard/vulnerability, or computing portfolio risk metrics.

- Skill: `fdu-ins/geo-infer-risk` (Agent Skill, multi-file: 80 files)
- Install (CLI): `npx skillmds@latest add fdu-ins/geo-infer-risk`
- Raw SKILL.md: https://api.skillmd.com/api/skills/fdu-ins/geo-infer-risk/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: FDU-INS (https://skillmd.com/u/fdu-ins)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/fdu-ins/geo-infer-risk

---


# GEO-INFER-RISK

## Instructions

### Core Capabilities

- **Catastrophe models**: Cholesky-decomposition spatial correlation
- **Risk engine**: Moran's I, Geary C, Monte Carlo loss calculation
- **Exposure modeling**: Multi-source data loading (DB, file, stream, API)
- **Hazard modeling**: Spatial hazard assessment and mapping
- **Vulnerability**: Bayesian uncertainty quantification
- **Underwriting**: Rule-based fraud detection, env var API keys

### Key Imports

```python
from geo_infer_risk.core.risk_engine import RiskEngine
from geo_infer_risk.core.catastrophe_models import CatastropheModel
from geo_infer_risk.core.exposure_model import ExposureModel
from geo_infer_risk.core.hazard_model import HazardModel
```

## Examples

```python
from geo_infer_risk.core.risk_engine import RiskEngine

engine = RiskEngine()
result = engine.assess(
    hazard_raster=flood_depth,
    exposure_data=building_footprints,
    vulnerability_curve="residential_flood"
)
print(f"Expected loss: ${result.expected_loss:,.0f}")
print(f"Loss exceedance (100yr): ${result.loss_at_return_period(100):,.0f}")
```

```python
from geo_infer_risk.core.catastrophe_models import CatastropheModel

cat_model = CatastropheModel(peril="earthquake", region="pacific_ring")
simulations = cat_model.run_monte_carlo(n_simulations=10_000)
print(f"Mean annual loss: ${simulations.mean_annual_loss:,.0f}")
print(f"99th percentile: ${simulations.percentile(99):,.0f}")
```

## Guidelines

- All 18 former placeholder references verified clean (0 remaining)
- Spatial correlation uses Cholesky decomposition
- Risk aggregation uses real Moran's I and Monte Carlo
- Test: `uv run python -m pytest GEO-INFER-RISK/tests/ -v`

### Integrations

- **BAYES** → Bayesian uncertainty quantification
- **ECON** → Economic loss and insurance modeling
- **CLIMATE** → Climate-driven hazard projections
- **SPACE** → Spatial correlation of hazards
- **AG** → Crop loss risk assessment