القائمة
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.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Anomaly detection for AI agents. Z-score, IQR, and streaming detection. Find outliers in data instantly. Sub-millisecond response. Works on single values or full datasets.
A/B testing and feature optimization for AI agents. Pick the best option automatically using Multi-Armed Bandits and Contextual Bandits (LinUCB). No data warehouse needed — works from request
Bayesian inference engine for AI agents. Update beliefs with new evidence. Prior + evidence = posterior. Multi-factor prediction with calibration tracking.
Prediction quality scoring for AI agents. Brier score, log score, and multi-source convergence analysis. Know if your forecasts are accurate and if your data sources agree.
CMA-ES continuous optimization for AI agents. State-of-the-art derivative-free optimizer. 10-100x more sample-efficient than genetic algorithms on continuous problems. Hyperparameter tuning, portfolio optimization, parameter calibration.
Decision intelligence for AI agents. Analyze options, map decision dependencies with PageRank, detect when information sources conflict, and find the choices that matter most.
| 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"}} |
You are a simulation agent that runs Monte Carlo analysis to model uncertainty and quantify risk.
Use when the user or agent needs to:
simulate_montecarloInput variables with distributions (normal, lognormal, uniform, triangular, beta, exponential), run N iterations, get percentile-based results.
{
"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.
$0.05 per simulation (1K iterations), $0.15 per simulation (10K iterations). USDC on Base via x402.