modeling-management-case-scenarios

Builds base, upside, and downside operating scenarios with key assumption sensitivity and return distribution analysis. Use when building operating cases, stress testing projections, or presenting scenario analysis.

11 stars

Best use case

modeling-management-case-scenarios is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Builds base, upside, and downside operating scenarios with key assumption sensitivity and return distribution analysis. Use when building operating cases, stress testing projections, or presenting scenario analysis.

Teams using modeling-management-case-scenarios should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/modeling-management-case-scenarios/SKILL.md --create-dirs "https://raw.githubusercontent.com/CaseMark/skills/main/skills/capital/modeling-management-case-scenarios/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/modeling-management-case-scenarios/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How modeling-management-case-scenarios Compares

Feature / Agentmodeling-management-case-scenariosStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Builds base, upside, and downside operating scenarios with key assumption sensitivity and return distribution analysis. Use when building operating cases, stress testing projections, or presenting scenario analysis.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Modeling Management Case Scenarios

Builds base, upside, and downside operating scenarios with key assumption sensitivity and return distribution analysis for PE-backed companies.

## When To Use

- Building operating cases around a management plan for IC memo or deal screening
- Stress testing a target's projections during diligence to bound downside risk
- Presenting scenario-driven return distributions to LP advisory committees
- Evaluating hold-period sensitivities for add-on acquisitions or bolt-ons
- Assessing covenant headroom across operating environments in leveraged structures

## Inputs To Gather

- **Management projections**: Revenue build-up, gross margin assumptions, opex plan, capex schedule, and working capital trends (typically 5-year horizon)
- **Historical financials**: 3-5 years of audited P&L, balance sheet, and cash flow to anchor base-case assumptions and establish trend lines
- **Deal structure**: Entry valuation (EV/EBITDA), capital structure (debt tranches, rates, amortization), equity check, and fee load
- **Key value drivers**: Identify 3-6 variables with outsized impact on returns (e.g., organic revenue growth, pricing power, margin expansion levers, customer churn)
- **Comparable benchmarks**: Sector median growth rates, margin profiles, and exit multiples from comps or prior deals for reasonableness checks
- **Exit assumptions**: Target hold period, anticipated exit multiple range, and potential exit routes (strategic sale, secondary, IPO)

## Workflow

1. **Anchor the base case on management's plan with adjustments**
   - Start from management projections; apply a historical-achievement haircut where management has a track record of missing (e.g., revenue growth reduced by the average miss over the last 3 years)
   - Normalize one-time items (add-backs, non-recurring revenue) and confirm recurring vs. non-recurring split
   - Build revenue from the bottom up where possible: units x price, customer count x ARPU, or segment-level detail

2. **Define upside and downside cases by toggling key drivers**
   - Select 3-6 assumption variables that most influence MOIC/IRR (typically: revenue growth, EBITDA margin, capex intensity, working capital days, exit multiple)
   - Upside case: reflect achievable outperformance — successful cross-sell, pricing increases stick, margin expansion from operational improvements
   - Downside case: model realistic stress — customer concentration loss, input cost spike, delayed synergy capture, multiple compression at exit
   - Avoid symmetric offsets; downside tails are typically fatter — weight assumptions accordingly

3. **Build the three-case P&L, balance sheet, and cash flow**
   - Project revenue, COGS, and opex line items for each scenario across the hold period
   - Flow through to EBITDA, apply D&A and interest expense from the debt schedule, compute net income and free cash flow
   - Model working capital changes using days-based assumptions (DSO, DIO, DPO) that flex with revenue
   - Ensure balance sheet balances via a cash sweep or revolver draw mechanism

4. **Construct the returns waterfall for each scenario**
   - Apply exit multiple to terminal EBITDA for each case; deduct net debt at exit to derive equity value
   - Calculate gross MOIC, net MOIC (after fees/carry), and IRR for each scenario
   - Show the equity bridge: entry equity → EBITDA growth contribution → multiple expansion/contraction → debt paydown → exit equity

5. **Run sensitivity tables and return distribution analysis**
   - Build two-variable sensitivity grids: entry multiple vs. exit multiple, revenue CAGR vs. margin at exit, leverage vs. growth
   - Identify the break-even assumptions — what growth rate or margin level is needed to return 1.0x equity
   - Summarize probability-weighted returns if scenario probabilities are assigned (e.g., 25% upside / 50% base / 25% downside)

6. **Compile scenario comparison output**
   - Side-by-side summary table: Revenue CAGR, exit EBITDA, exit EV, net debt at exit, equity value, MOIC, IRR for each case
   - Highlight key swing factors and which assumptions create the widest return dispersion
   - Flag any covenant breach triggers in the downside case (leverage ratio, fixed charge coverage, minimum EBITDA)

## Output

- **Scenario summary table**: Side-by-side base / upside / downside with key financial metrics and returns
- **Detailed P&L and cash flow projections** for each case across the hold period
- **Returns waterfall**: Entry equity → value creation components → exit equity for each scenario
- **Sensitivity grids**: 2-variable tables showing MOIC/IRR across assumption ranges
- **Key risks and mitigants**: Narrative summary of what drives the downside and what protections exist (covenants, earn-outs, reps)
- **Assumption log**: Explicit table of every toggled variable, its value in each case, and the rationale or source

## Quality Checks

- **Historical calibration**: Base-case growth and margin assumptions should be within the range of the company's historical performance unless a specific, documented catalyst justifies deviation
- **Balance sheet integrity**: Confirm assets = liabilities + equity in every period, every scenario; revolver/cash sweep functions correctly
- **Debt schedule consistency**: Interest expense ties to average debt balances; mandatory amortization is reflected; covenant tests are computed correctly [VERIFY specific covenant definitions against the credit agreement]
- **Return math verification**: Cross-check MOIC = exit equity / entry equity; IRR computed using actual cash flow dates (not approximations)
- **Downside plausibility**: Ensure the downside case is a genuine stress, not just base minus 5% — reference sector downturns, customer loss scenarios, or input cost shocks for calibration
- **Assumption sourcing**: Every key assumption should reference a source (management plan, diligence finding, comp data, or sponsor thesis) — mark unsupported assumptions with [VERIFY]
- **Exit multiple discipline**: Exit multiples should be benchmarked against current trading comps and precedent transactions; flag any scenario assuming multiple expansion beyond entry [VERIFY against current market conditions]

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