scenario-modeler
Monte Carlo simulations for exit scenarios, return distributions
Profile →npx skills add a5c-ai/babysitter --skill scenario-modeler --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 distri
- Overview
- Capabilities
- Exit Scenario Modeling
- Monte Carlo Simulation
- Sensitivity Analysis
- Return Distribution Analysis
- Usage
- Model Exit Scenarios
- Run Monte Carlo
- Analyze Sensitivities
- Model Portfolio Returns
- Scenario Framework
- Integration Points
- Simulation Parameters
What does the scenario-modeler skill do?
Monte Carlo simulations for exit scenarios, return distributions
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill scenario-modeler --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.