Monte Carlo Simulator
Run Monte Carlo simulations with probability distributions, confidence intervals, and statistical analysis
npx claude-code-templates@latest --command simulation/monte-carlo-simulator Scanned by NVIDIA SkillSpector
Automated static analysis (no LLM) · v2.12.0 · 2026-10-23
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Content
Monte Carlo Simulator
Run comprehensive Monte Carlo simulations with advanced statistical analysis: $ARGUMENTS
Current Analysis Context
- Simulation target: Based on $ARGUMENTS (financial projections, project timelines, market scenarios, risk assessment)
- Key variables: Uncertain parameters that drive outcome variability
- Available data: Historical data, expert estimates, and probability distributions
- Decision requirements: Confidence levels and risk tolerance for decision-making
Task
Execute sophisticated Monte Carlo simulations with comprehensive uncertainty quantification:
Simulation Target: Use $ARGUMENTS to simulate financial projections, project timelines, market scenarios, or risk assessments
Monte Carlo Framework:
- Variable Definition - Uncertain parameter identification, probability distribution selection, and correlation modeling
- Simulation Engine - Random sampling, scenario generation, and statistical convergence analysis
- Output Analysis - Probability distributions, confidence intervals, and sensitivity analysis
- Risk Quantification - Value at Risk (VaR), extreme scenario analysis, and tail risk assessment
- Scenario Clustering - Pattern recognition, outcome categorization, and decision-relevant grouping
- Decision Integration - Risk-adjusted recommendations, optimization strategies, and contingency planning
Advanced Features: Latin hypercube sampling, copula modeling, importance sampling, and variance reduction techniques.
Statistical Rigor: Convergence testing, goodness-of-fit validation, and robust statistical inference with comprehensive uncertainty bounds.
Output: Complete Monte Carlo analysis with probability distributions, risk metrics, scenario analysis, and statistically-grounded decision recommendations with quantified confidence levels.