lbo-model

Create Excel-based leveraged buyout models with debt schedules and IRR/MOIC analysis.

Updated Jun 15, 2026

Search Tags:#financial-modeling #lbo #excel-modeling #private-equity #debt-schedule #sources-uses #moic-irr

Ready to run

npx skills add https://github.com/faramirezs/talking_printer --skill lbo-model-faramirezs

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: lbo-model
Source: https://github.com/faramirezs/talking_printer/tree/main/hermes-agent/optional-skills/finance/lbo-model
Command: npx skills add https://github.com/faramirezs/talking_printer --skill lbo-model-faramirezs

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Leveraged buyout modeling in Excel is complex and time-consuming. This skill provides a structured template to produce cash-flow models with sources & uses, debt schedules, cash sweeps, exit multiples, and IRR/MOIC sensitivity, enabling finance teams to evaluate deals quickly and consistently.

Core Features & Use Cases

LBO structure templates: sources & uses, operating model, debt schedule, cash sweep, and returns analysis
Sensitivity analysis: IRR and MOIC sensitivity to exit multiples and debt terms
Template-driven execution: Works with excel-author; headless openpyxl; includes recalc steps for dynamic updates

Quick Start

Open the LBO model template, enter your assumptions, and generate a ready-to-deliver Excel model.

Frequently Asked Questions about lbo-model

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build a leveraged buyout model in Excel with a debt schedule and IRR sensitivity?▼

A leveraged buyout model in Excel is built using a template-driven workflow that writes formulas for sources & uses, debt schedules, cash sweeps, and IRR/MOIC sensitivity. This process applies correct sign conventions and includes a recalc step for dynamic updates.

Can I automatically generate sources and uses and cash sweep formulas in an Excel LBO model?▼

Yes, an Excel LBO model can automatically generate sources & uses and cash sweep formulas using Python and openpyxl. This template-driven execution writes the formulas directly into the spreadsheet and runs a built-in recalc step to ensure dynamic updates.

What is the best way to calculate MOIC and IRR sensitivity to exit multiples for private equity screening?▼

The best way to calculate MOIC and IRR sensitivity for private equity screening is by using a structured LBO template that models varying debt structures and exit multipliers. This generates pitch-ready scenarios and evaluates sponsor-case valuations quickly and consistently.

Does this LBO Excel modeling approach work with openpyxl for headless spreadsheet generation?▼

Yes, this LBO Excel modeling approach works with openpyxl for headless spreadsheet generation. It leverages Python to write formulas, apply sign conventions, and execute recalc steps without needing the Excel desktop application open.

Do I need Microsoft Excel installed to run recalc steps on a generated leveraged buyout model?▼

No, you do not need Microsoft Excel installed to run recalc steps on a generated leveraged buyout model. The workflow uses headless openpyxl to write formulas and apply dynamic updates programmatically through Python.

How do I evaluate different debt structures and exit multipliers for a sponsor-case LBO valuation?▼

You evaluate different debt structures and exit multipliers for a sponsor-case LBO valuation by entering assumptions into a structured Excel template. The model calculates returns analysis, including IRR and MOIC sensitivity, across various debt terms and exit scenarios.