enterprise-risk-aggregator

What it does

Aggregates individual project risks into a portfolio-level view. Identifies correlated risks across projects, calculate enterprise risk exposure, and develop portfolio-wide mitigation strategies.

How it works

• Provides a risk aggregation framework that groups project risks by category and computes a portfolio-wide view including total exposure, top risks, and risk categories.
• Implements a Python-based technical implementation with classes for ProjectRisk, RiskCorrelation, AggregatedRisk, PortfolioRiskProfile, and an EnterpriseRiskAggregator controller.
• Allows adding risks per project via add_risk, and supports importing risks from project risk records via import_project_risks.
• Detects correlations by examining shared triggers between risks or same-category risks across different projects, creating RiskCorrelation records with a POSITIVE type and a calculated strength.
• Identifies systemic risks by tallying triggers across risks, determining which triggers affect multiple projects, and computing total exposure for those systemic risks.
• Aggregates risks by category, computing total_exposure, expected_loss, worst_case, risk_count, and mitigation_cost; uses a simple aggregation model with no correlation adjustments.
• Calculates Value at Risk (VaR) usingMonte Carlo simulation over a number of iterations to estimate losses based on risk probabilities and impacts.
• Generates a portfolio profile including report date, project counts, risk counts, exposure metrics, by-category breakdown, top risks, correlations, and systemic risks.
• Provides mitigation prioritization via suggest_mitigation_priorities, ranking systemic risks first, then highly correlated risks, and finally individual high-impact risks.

When to use it

• When you need a portfolio-level view of construction project risks and how they interrelate.
• When identifying systemic exposures and prioritizing mitigation across multiple projects is required.
• When generating a formal risk report with category summaries, top risks, and recommended mitigations.

What it can touch

• Risks are stored internally within the EnterpriseRiskAggregator instance and manipulated through methods: add_risk, import_project_risks, detect_correlations, identify_systemic_risks, aggregate_by_category, calculate_var, generate_portfolio_profile, suggest_mitigation_priorities, generate_report.
• It uses the following data structures and components: ProjectRisk, RiskCorrelation, AggregatedRisk, PortfolioRiskProfile, and internal constants like SYSTEMIC_TRIGGERS.
• No external tools are invoked in this description beyond standard Python constructs; tools listed in the skill are for implementation (claude-code).

Caveats

• The model uses a simplified aggregation approach that does not adjust for cross-risk dependencies beyond detected correlations.
• VaR is derived from Monte Carlo simulations with a fixed number of iterations; results depend on the stochastic sampling and risk inputs.
• Systemic risk identification relies on trigger counts across risks and predefined SYSTEMIC_TRIGGERS; behavior is as implemented in the code and may not capture all real-world systemic factors.
• No guarantees are provided about risk mitigation outcomes; the skill suggests priorities based on internal calculations.