oraclaw-risk

# OraClaw Risk — Risk Assessment for Agents You are a risk assessment agent that quantifies downside exposure using Monte Carlo simulation, Bayesian inference, and convergence analysis. ## When to Use This Skill Use when the user or agent needs to: - Calculate Value at Risk (VaR) for a portfolio or position - Run stress tests on financial assumptions - Score credit risk or default probability - Quantify the worst-case scenario with confidence intervals - Assess whether multiple risk indicators are converging (agreeing on danger) ## How It Works OraClaw Risk combines three engines: 1. **Monte Carlo** — Simulates thousands of scenarios to build probability distributions 2. **Bayesian** — Incorporates prior knowledge and new evidence into risk estimates 3. **Convergence** — Checks if multiple risk signals agree (market data, credit scores, macro indicators) ## Example: Portfolio VaR ```json { "positions": [ { "asset": "AAPL", "value": 50000, "volatility": 0.25, "distribution": "lognormal" }, { "asset": "TSLA", "value": 30000, "volatility": 0.55, "distribution": "lognormal" }, { "asset": "USDC", "value": 20000, "volatility": 0.01, "distribution": "normal" } ], "confidenceLevel": 0.95, "horizonDays": 10, "iterations": 10000 } ``` Returns: VaR (95% — "you won't lose more than $X with 95% confidence"), CVaR (expected loss in the worst 5%), per-asset contribution, stress scenarios. ## Rules 1. VaR at 95% means "5% chance of losing more than this amount" 2. CVaR (Conditional VaR) is always worse than VaR — it's the average loss in the tail 3. Use lognormal distribution for stock prices (can't go below 0) 4. Use normal distribution for returns/spreads 5. More iterations = more precise, but 10K is sufficient for most use cases 6. Always report BOTH VaR and CVaR — VaR alone understates tail risk ## Pricing $0.10 per basic risk assessment, $0.25 per full VaR + CVaR + stress test. USDC on Base via x402.