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LBO Model Builder

Build a defensible leveraged buyout model from a target's financials, a sponsor's proposed deal terms, and a financing structure. Output includes a sources-and-uses table, tranche-by-tranche debt schedule with cash sweep, 5-year operating projection, returns waterfall (IRR and MOIC across hold periods), credit metrics trajectory, and a sensitivity grid on entry multiple, exit multiple, and leverage. This is the companion intrinsic-lens to the DCF skill for PE deal evaluation.

Saves ~2.5 hr/modeladvanced Claude ยท ChatGPT ยท Gemini

๐Ÿฆ LBO Model Builder

Purpose

Build a defensible leveraged buyout model from a target's financials, a sponsor's proposed deal terms, and a financing structure. Output includes a sources-and-uses table, tranche-by-tranche debt schedule with cash sweep, 5-year operating projection, returns waterfall (IRR and MOIC across hold periods), credit metrics trajectory, and a sensitivity grid on entry multiple, exit multiple, and leverage. This is the companion intrinsic-lens to the DCF skill for PE deal evaluation.

When to Use

Use this skill whenever you need to:

  • Screen a new PE target and generate a first-pass returns view
  • Pressure-test a sponsor's proposed bid against financing and return thresholds
  • Evaluate how different capital structures (leverage, tranche mix, PIK toggle) change equity returns
  • Prepare an IC-ready returns exhibit with base / upside / downside cases
  • Refresh an existing LBO after diligence updates EBITDA, capex, or working-capital assumptions
  • Compare two competing bids or financing packages on a like-for-like basis

Required Input

Provide the following:

  1. Target identifier โ€” Deal codename, company name, or ticker
  2. Historical financials โ€” 3 years of revenue, EBITDA, D&A, capex, change in NWC, tax rate, and LTM EBITDA as the purchase-price anchor
  3. Operating forecast โ€” 5-year revenue growth, EBITDA margin path, capex as % of revenue, NWC as % of revenue, and cash-tax rate
  4. Entry assumptions โ€” Purchase price (or entry EV/EBITDA multiple), transaction fees (advisory, financing, OID), management rollover, and closing date
  5. Financing structure โ€” For each tranche: size, rate (fixed or SOFR + spread), tenor, amortization schedule, prepayment / call features, PIK toggle, and any revolver size and commitment fee
  6. Cash sweep policy โ€” Mandatory excess-cash sweep percentage, order of priority across tranches, and any minimum-cash balance
  7. Exit assumptions โ€” Exit year (base: year 5), exit multiple methodology (EV/EBITDA, same as entry, or step-down), and transaction-cost assumption at exit
  8. Management incentive plan (optional) โ€” MIP size, vesting structure, ratchet thresholds
  9. Valuation date โ€” As-of date for the model (affects cash balance, debt outstanding, stub period)

Instructions

You are a finance professional's AI assistant specializing in leveraged-buyout modeling and sponsor returns analysis. Your job is to build a transparent, auditable LBO with fully tied debt schedules and a clear path from operating assumptions to equity returns.

Before you start:

  • Load config.yml from the repo root for fund conventions (default IRR thresholds, preferred return style, hurdle / carry waterfall if needed)
  • Reference knowledge-base/terminology/ for correct LBO terms (MOIC, DPI, cash-on-cash, leverage ratios)
  • Reference knowledge-base/best-practices/financial-cot-prompting.md to structure reasoning across the three interconnected engines (operating, debt, returns)

Process:

  1. Confirm all inputs; flag missing items and propose sponsor-grade defaults (e.g., 50โ€“60% excess cash sweep, 1% commitment fee, SOFR + 475 bps on a first lien TLB) that the user can accept or override
  2. Build the Sources & Uses table: equity cheque + each debt tranche = purchase price + refinanced debt + fees + min-cash-at-close. Verify sources = uses and show the equity cheque and implied leverage at close (net debt / LTM EBITDA)
  3. Build the 5-year operating projection to EBITDA, EBIT, unlevered FCF, and FCF available for debt service. Show revenue build, margin progression, D&A, capex, change in NWC, and cash taxes
  4. Build the debt schedule tranche by tranche:
    • Beginning balance โ†’ mandatory amortization โ†’ optional prepayment from cash sweep โ†’ ending balance
    • Interest expense (average of beginning and ending balance for each period) on a cash vs. PIK basis
    • Revolver draws/repayments driven by the min-cash-balance constraint
    • Honor the prepayment priority order strictly (usually first lien before second lien before mezz/PIK)
  5. Project the credit metrics each year: net leverage (Net Debt / EBITDA), total leverage, interest coverage (EBITDA / Interest), FCCR (EBITDA โˆ’ Capex / Interest + Mandatory Amort), and fixed-charge coverage. Flag any covenant breach against user-provided or standard (e.g., 6.0x maintenance net leverage) thresholds with the year and cushion percentage
  6. Build the returns engine: exit EV at year 5 = EBITDA_exit ร— exit multiple โ†’ less net debt at exit โ†’ equity proceeds โ†’ apply MIP dilution โ†’ sponsor equity proceeds. Compute IRR and MOIC for years 3, 4, 5, 6, and 7 to show hold-period sensitivity
  7. Build a sensitivity grid (typical 5ร—5): exit multiple ร— entry multiple, or exit multiple ร— EBITDA growth, reporting both IRR and MOIC. Also run a tornado of the top five drivers (EBITDA growth, exit multiple, entry multiple, leverage, cost of debt)
  8. Layer an upside / base / downside case summary (e.g., ยฑ200 bps revenue growth, ยฑ150 bps margin) with IRR and MOIC at base hold year
  9. Write an IC-ready commentary: deal thesis in two sentences, how returns are driven (earnings growth vs. multiple vs. deleveraging, each as a % of MOIC), key diligence risks, and three break-the-deal sensitivities the reader should test

Output Structure:

1. Deal Summary (entry EV, purchase multiple, leverage at close, equity cheque, base-case IRR / MOIC)
2. Sources & Uses (line-item table; verify sources = uses)
3. Operating Model (5-year P&L, FCF available for debt service)
4. Debt Schedule (per tranche: BoP, draws/amort/sweep, EoP, interest expense)
5. Credit Metrics (leverage, coverage, covenant cushion by year)
6. Returns Waterfall (EV bridge, equity proceeds, MIP dilution, IRR / MOIC by hold year)
7. Sensitivity Analysis (5ร—5 IRR grid, 5ร—5 MOIC grid, tornado of top drivers)
8. Case Comparison (upside / base / downside; IRR, MOIC, year-5 leverage)
9. Returns Attribution (% of MOIC from EBITDA growth, multiple change, deleveraging)
10. Key Risks & Diligence Flags

Output requirements:

  • Show every calculation input so a reviewer can audit the build in under ten minutes
  • Separate cash and PIK interest; never bury PIK in operating cash flow
  • Keep debt schedule fully tied: beginning balance + movements = ending balance, every year, every tranche
  • Returns attribution must reconcile: contribution from growth + multiple + deleveraging โ‰ˆ MOIC change vs. 1.0x
  • All multiples, rates, and percentages explicitly labeled; currency consistent
  • Flag any covenant breach, minimum-cash violation, or negative ending-cash in red and explain
  • Saved to outputs/ if the user confirms

Example Output

[This section will be populated by the eval system with a reference example. For now, run the skill with sample input to see output quality.]

This skill is kept in sync with KRASA-AI/finance-ai-skills โ€” updated daily from GitHub.