name: scenario-modeler description: Monte Carlo simulations for exit scenarios, return distributions allowed-tools:

• Read
• Write
• Glob
• Grep
• Bash
• WebFetch metadata: specialization: venture-capital domain: business skill-id: vc-skill-024

Scenario Modeler

Overview

The Scenario Modeler skill provides advanced scenario analysis and Monte Carlo simulations for venture capital return modeling. It enables probabilistic analysis of exit outcomes and return distributions to inform investment decisions and portfolio construction.

Capabilities

Exit Scenario Modeling

• Model multiple exit scenarios (IPO, M&A, secondary)
• Assign probabilities to scenarios
• Calculate expected returns across outcomes
• Account for timing variations

Monte Carlo Simulation

• Run thousands of probabilistic scenarios
• Model parameter distributions
• Generate return distributions
• Calculate confidence intervals

Sensitivity Analysis

• Identify key value drivers
• Model driver interactions
• Create tornado charts
• Determine break-even assumptions

Return Distribution Analysis

• Calculate expected IRR and MOIC
• Generate return percentiles
• Model loss probability
• Analyze portfolio-level returns

Usage

Model Exit Scenarios

Input: Company data, exit assumptions
Process: Build scenarios, assign probabilities
Output: Scenario matrix, expected value

Run Monte Carlo

Input: Base assumptions, parameter distributions
Process: Run simulation iterations
Output: Return distribution, percentile analysis

Analyze Sensitivities

Input: Base case, key drivers
Process: Calculate driver sensitivities
Output: Sensitivity analysis, tornado chart

Model Portfolio Returns

Input: Portfolio of investments, scenarios
Process: Aggregate portfolio outcomes
Output: Portfolio return distribution

Scenario Framework

Integration Points

• VC Method Valuation: Scenario-based valuation
• Cap Table Modeling: Ownership under scenarios
• DCF Analysis: Probability-weighted DCF
• Sensitivity Analyst (Agent): Support scenario analysis

Simulation Parameters

Best Practices

1. Ground scenarios in historical data
2. Validate probability assumptions
3. Include tail scenarios (both positive and negative)
4. Consider correlation between assumptions
5. Use simulations for insight, not precision