Agent skill

monte-carlo-engine

Monte Carlo simulation engine skill for probabilistic modeling, risk quantification, and uncertainty propagation

View SKILL.md on GitHub Repository
Stars 514
Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/domains/business/decision-intelligence/skills/monte-carlo-engine

Metadata

Additional technical details for this skill

domain
business
category
simulation
priority
high
specialization
decision-intelligence
tools libraries 
[
    "numpy",
    "scipy.stats",
    "pymc",
    "chaospy",
    "SALib"
]
shared candidate
YES

SKILL.md

Monte Carlo Engine

Overview

The Monte Carlo Engine skill provides comprehensive probabilistic simulation capabilities for quantifying uncertainty, assessing risk, and propagating variability through complex models. It supports multiple sampling strategies, correlation handling, and statistical analysis of simulation outputs for data-driven decision support.

Capabilities

  • Random variate generation (normal, triangular, PERT, uniform, lognormal, beta, etc.)
  • Latin Hypercube Sampling (LHS)
  • Correlation structure handling (Cholesky decomposition, copulas)
  • Convergence monitoring and adaptive iteration
  • Statistical output analysis (mean, variance, percentiles)
  • Tornado diagram generation
  • Value at Risk (VaR) and CVaR calculation
  • Parallel simulation execution

Used By Processes

  • Monte Carlo Simulation for Decision Support
  • Strategic Scenario Development
  • What-If Analysis Framework
  • Predictive Analytics Implementation

Usage

Distribution Specification

python
# Define input distributions
input_variables = {
    "revenue": {
        "distribution": "triangular",
        "parameters": {"min": 800000, "mode": 1000000, "max": 1500000}
    },
    "cost": {
        "distribution": "normal",
        "parameters": {"mean": 600000, "std": 50000}
    },
    "market_share": {
        "distribution": "PERT",
        "parameters": {"min": 0.05, "mode": 0.10, "max": 0.20}
    },
    "unit_price": {
        "distribution": "uniform",
        "parameters": {"min": 45, "max": 55}
    }
}

Correlation Matrix

python
# Define correlations between variables
correlations = {
    "variables": ["revenue", "cost", "market_share"],
    "matrix": [
        [1.0, 0.6, 0.3],   # revenue correlations
        [0.6, 1.0, 0.2],   # cost correlations
        [0.3, 0.2, 1.0]    # market_share correlations
    ]
}

Model Function

python
# Define the model to simulate
def profit_model(inputs):
    revenue = inputs["revenue"]
    cost = inputs["cost"]
    profit = revenue - cost
    return {"profit": profit, "margin": profit / revenue}

Sampling Strategies

  1. Simple Random Sampling: Standard Monte Carlo
  2. Latin Hypercube Sampling: Better coverage with fewer samples
  3. Quasi-Monte Carlo: Low-discrepancy sequences (Sobol, Halton)
  4. Importance Sampling: Focus on tail events

Convergence Monitoring

The skill monitors:

  • Running mean and standard deviation
  • Coefficient of variation convergence
  • Percentile stability
  • Adaptive stopping criteria

Input Schema

json
{
  "input_variables": {
    "variable_name": {
      "distribution": "string",
      "parameters": "object"
    }
  },
  "correlations": {
    "variables": ["string"],
    "matrix": "2D array"
  },
  "model": "function or expression",
  "simulation_options": {
    "iterations": "number",
    "sampling_method": "random|lhs|quasi_mc",
    "random_seed": "number",
    "parallel": "boolean",
    "convergence_threshold": "number"
  },
  "output_options": {
    "percentiles": ["number"],
    "risk_metrics": ["VaR", "CVaR"],
    "confidence_level": "number"
  }
}

Output Schema

json
{
  "summary_statistics": {
    "output_variable": {
      "mean": "number",
      "std": "number",
      "median": "number",
      "min": "number",
      "max": "number",
      "percentiles": "object"
    }
  },
  "risk_metrics": {
    "VaR": "number",
    "CVaR": "number",
    "probability_of_loss": "number"
  },
  "convergence_info": {
    "iterations_run": "number",
    "converged": "boolean",
    "stability_scores": "object"
  },
  "raw_results": "array (optional)",
  "tornado_data": "object",
  "visualization_paths": ["string"]
}

Best Practices

  1. Use at least 10,000 iterations for stable percentile estimates
  2. Apply Latin Hypercube Sampling for efficiency
  3. Validate input distributions with subject matter experts
  4. Include correlations for realistic results
  5. Monitor convergence before accepting results
  6. Perform sensitivity analysis to identify key drivers
  7. Document all distribution assumptions

Integration Points

  • Receives distributions from Risk Distribution Fitter
  • Feeds into Sensitivity Analyzer for importance analysis
  • Supports Value at Risk Calculator for risk metrics
  • Connects with Decision Tree Builder for decision node valuation
  • Integrates with Real Options Analyzer for option valuation

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

a5c-ai/babysitter

gsd-tools

Central utility skill for GSD operations. Provides config parsing, slug generation, timestamps, path operations, and orchestrates calls to other specialized skills. Acts as the unified entry point that the original gsd-tools.cjs provided via its lib/ modules (commands, config, core, init).

514 31
Explore
a5c-ai/babysitter

model-profile-resolution

Resolve model profile (quality/balanced/budget) at orchestration start and map agents to specific models. Enables cost/quality tradeoffs by selecting appropriate AI models for each agent role.

514 31
Explore
a5c-ai/babysitter

verification-suite

Plan structure validation, phase completeness checks, reference integrity verification, and artifact existence confirmation. Provides the structured verification layer ensuring GSD artifacts are well-formed and complete.

514 31
Explore
a5c-ai/babysitter

state-management

STATE.md reading, writing, and field-level updates. Provides cross-session state persistence via .planning/STATE.md with structured fields for current task, completed phases, blockers, decisions, and quick tasks.

514 31
Explore
a5c-ai/babysitter

git-integration

Git commit patterns, formats, and conventions for GSD methodology. Provides atomic commits per task, structured commit messages, planning file commits, branch management, and milestone tag operations.

514 31
Explore
a5c-ai/babysitter

frontmatter-parsing

YAML frontmatter parsing and manipulation for .planning/ documents. Provides read, write, update, query, and validation operations on frontmatter blocks in GSD markdown artifacts.

514 31
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results