| name | oraclaw-simulate |
| description | Monte Carlo simulation for AI agents. Run thousands of probabilistic scenarios to model risk, forecast revenue, estimate project timelines, and quantify uncertainty. Supports 6 distribution types. |
| version | 1.0.0 |
| metadata | {"openclaw":{"requires":{"env":["ORACLAW_API_KEY"]},"primaryEnv":"ORACLAW_API_KEY","emoji":"🎲","homepage":"https://web-olive-one-89.vercel.app/simulate","tags":["monte-carlo","simulation","risk","forecasting","probability","finance","trading"],"price":0.05,"currency":"USDC"}} |
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
{
"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
- Use at least 1,000 iterations for reliable results, 10,000 for precision
- Normal distribution for symmetric uncertainty (±range)
- Lognormal for strictly positive values (revenue, prices)
- Triangular when you know min/mode/max but not the shape
- 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.
