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.
Best use case
geo-infer-risk is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using geo-infer-risk should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/GEO-INFER-RISK/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How geo-infer-risk Compares
| Feature / Agent | geo-infer-risk | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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