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monte-carlo-engine

Run large-scale Monte Carlo simulations on stochastic models to quantify uncertainty.

Updated Jan 26, 2026
Search Tags:#uncertainty#risk-analysis#monte-carlo#stochastic-models#probabilistic-forecasts#convergence-diagnostics
One-click install
npx skills add https://github.com/SPIRAL-EDWIN/MCM-ICM-2601000 --skill monte-carlo-engine
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: monte-carlo-engine
Source: https://github.com/SPIRAL-EDWIN/MCM-ICM-2601000/tree/main/.github/skills/monte-carlo-engine
Command: npx skills add https://github.com/SPIRAL-EDWIN/MCM-ICM-2601000 --skill monte-carlo-engine

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill enables rigorous uncertainty quantification and risk assessment by running large-scale Monte Carlo simulations on stochastic models, producing distributions, confidence intervals, and convergence diagnostics.

Core Features & Use Cases

  • Uncertainty quantification: sample from parameter distributions and generate outcome distributions.
  • Probabilistic reporting: produce confidence intervals, histograms, and convergence plots for decision making.
  • Use Case: apply to scenarios with randomness or incomplete data to compare risk across alternatives (e.g., project cost, demand, or arrival processes).

Quick Start

Run a basic Monte Carlo forecast by calling monte_carlo_simulation(n_simulations=10000) and inspect the results in the results directory for mean, CI, and convergence diagnostics.

Frequently Asked Questions about monte-carlo-engine

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I quantify uncertainty in stochastic models with Monte Carlo simulations?▼

Probabilistic forecasts from Monte Carlo simulations produce confidence intervals, outcome distributions, and convergence diagnostics, enabling risk assessment and decision making when comparing alternatives with inherent randomness or incomplete data.

What's the best way to run probabilistic forecasts for risk analysis?▼

Probabilistic forecasts from Monte Carlo simulations produce confidence intervals, outcome distributions, and convergence diagnostics, enabling risk assessment and decision making when comparing alternatives with inherent randomness or incomplete data.

How do I perform Monte Carlo simulations and generate convergence diagnostics step by step?▼

Monte Carlo simulations require reproducible workflows using seeded randomness, and leverage vectorized or parallel computation to efficiently handle large-scale simulation runs across stochastic models.

Do I need seeded randomness and parallel computation for large-scale Monte Carlo risk analysis?▼

Monte Carlo simulations require reproducible workflows using seeded randomness, and leverage vectorized or parallel computation to efficiently handle large-scale simulation runs across stochastic models.

Can I use Monte Carlo simulations to compare project cost risk across alternatives?▼

Monte Carlo simulations are suited for problems with inherent randomness or uncertain inputs, but require defining appropriate stochastic models and parameter distributions to produce valid probabilistic forecasts.

When should I not use Monte Carlo simulations for uncertainty quantification?▼

Monte Carlo simulations are suited for problems with inherent randomness or uncertain inputs, but require defining appropriate stochastic models and parameter distributions to produce valid probabilistic forecasts.

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