lbo-model

SKILL.md preview

---
name: lbo-model
description: Build leveraged buyout models in Excel — sources & uses, debt schedule, cash sweep, exit multiple, IRR/MOIC sensitivity. Pairs with excel-author. Use for PE screening, sponsor-case valuation, or illustrative LBO in a pitch.
version: 1.0.0
author: Anthropic (adapted by Nous Research)
license: Apache-2.0
platforms: [linux, macos, windows]
metadata:
  hermes:
    tags: [finance, valuation, lbo, private-equity, excel, openpyxl, modeling]
    related_skills: [excel-author, pptx-author, dcf-model, 3-statement-model]
---

## Environment

This skill assumes **headless openpyxl** — you are producing an .xlsx file on disk.
Follow the `excel-author` skill's conventions for cell coloring, formulas, named ranges, and sensitivity tables.
Recalculate before delivery: `python /path/to/excel-author/scripts/recalc.py ./out/model.xlsx`.

---

## TEMPLATE REQUIREMENT

**This skill uses templates for LBO models. Always check for an attached template file first.**

Before starting any LBO model:
1. **If a template file is attached/provided**: Use that template's structure exactly - copy it and populate with the user's data
2. **If no template is attached**: Ask the user: *"Do you have a specific LBO template you'd like me to use? If not, I can use the standard template which includes Sources & Uses, Operating Model, Debt Schedule, and Returns Analysis."*
3. **If using the standard template**: Copy `examples/LBO_Model.xlsx` as your starting point and populate it with the user's assumptions

**IMPORTANT**: When a file like `LBO_Model.xlsx` is attached, you MUST use it as your template - do not build from scratch. Even if the template seems complex or has more features than needed, copy it and adapt it to the user's requirements. Never decide to "build from scratch" when a template is provided.

---

## CRITICAL INSTRUCTIONS — READ FIRST

Use Python/openpyxl. Write formula strings (`ws["D20"] = "=B5*B6"`), then run the `excel-author` skill's `recalc.py` helper before delivery.

### Core Principles
* **Every calculation must be an Excel formula** - NEVER compute values in Python and hardcode results into cells. When using openpyxl, write `cell.value = "=B5*B6"` (formula string), NOT `cell.value = 1250` (computed result). The model must be dynamic and update when inputs change.
* **Use the template structure** - Follow the organization in `examples/LBO_Model.xlsx` or the user's provided template. Do not invent your own layout.
* **Use proper cell references** - All formulas should reference the appropriate cells. Never type numbers that should come from other cells.
* **Maintain sign convention consistency** - Follow whatever sign convention the template uses (some use negative for outflows, some use positive). Be consistent throughout.
* **Work section by section, verify with user at each step** - Complete one section fully, show the user what was built, run the section's verification checks, and get confirmation BEFORE moving to the next section. Do NOT build the entire model end-to-end and then present it — later sections depend on earlier ones, so catching a mistake in Sources & Uses after the returns are already built means rework everywhere.

### Formula Color Conventions
* **Blue (0000FF)**: Hardcoded inputs - typed numbers that don't reference other cells
* **Black (000000)**: Formulas with calculations - any formula using operators or functions (`=B4*B5`, `=SUM()`, `=-MAX(0,B4)`)
* **Purple (800080)**: Links to cells on the **same tab** - direct references with no calculation (`=B9`, `=B45`)
* **Green (008000)**: Links to cells on **different tabs** - cross-sheet references (`=Assumptions!B5`, `='Operating Model'!C10`)

### Fill Color Palette — Professional Blues & Greys (Default unless user/template specifies otherwise)
* **Keep it minimal** — only use blues and greys for cell fills. Do NOT introduce greens, yellows, reds, or multiple accents. A professional LBO model uses restraint.
* **Default fill palette:**

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