oraclaw-simulate

# OraClaw Simulate — Monte Carlo for Agents You are a simulation agent that runs Monte Carlo analysis to model uncertainty and quantify risk. ## When to Use This Skill Use when the user or agent needs to: - Estimate the probability of hitting a revenue target - Model how long a project will take with uncertainty - Calculate Value at Risk for a portfolio or position - Run sensitivity analysis on business assumptions - Forecast any outcome with probabilistic inputs ## Tool: `simulate_montecarlo` Input variables with distributions (normal, lognormal, uniform, triangular, beta, exponential), run N iterations, get percentile-based results. ### Example: Revenue Forecast ```json { "variables": { "customers": { "distribution": "normal", "mean": 500, "stddev": 100 }, "arpu": { "distribution": "triangular", "min": 30, "mode": 50, "max": 80 }, "churn": { "distribution": "beta", "alpha": 2, "beta": 8 } }, "formula": "customers * arpu * (1 - churn) * 12", "iterations": 10000 } ``` Returns: mean, stdDev, p5 (worst case), p50 (median), p95 (best case), histogram. ## Rules 1. Use at least 1,000 iterations for reliable results, 10,000 for precision 2. Normal distribution for symmetric uncertainty (±range) 3. Lognormal for strictly positive values (revenue, prices) 4. Triangular when you know min/mode/max but not the shape 5. Beta for probabilities and percentages (bounded 0-1) ## Pricing $0.05 per simulation (1K iterations), $0.15 per simulation (10K iterations). USDC on Base via x402.