lbo-model

Build leveraged buyout Excel workbooks with IRR and MOIC calculations using openpyxl.

Updated Aug 22, 2026
One-click install
npx skills add https://github.com/vivekgoquest/hermes-agent-stable --skill lbo-model-vivekgoquest
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: lbo-model
Source: https://github.com/vivekgoquest/hermes-agent-stable/tree/main/optional-skills/finance/lbo-model
Command: npx skills add https://github.com/vivekgoquest/hermes-agent-stable --skill lbo-model-vivekgoquest

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires openpyxl.

What problem does it solve? Building a leveraged buyout model in Excel requires dozens of interlinked formulas across Sources & Uses, operating projections, debt schedules, and returns analysis, and a single wrong cell reference breaks the entire model. This Skill produces dynamic, formula-driven LBO workbooks from templates with validated calculations and professional formatting. ## Core Features & Use Cases - Template-Driven Modeling: Populates a provided LBO template or the standard template with Excel formulas, never hardcoded values, so the model updates when assumptions change. - Full LBO Structure: Covers Sources & Uses, operating model, debt schedule with cash sweep waterfalls, returns analysis (IRR/MOIC), and odd-dimension sensitivity tables with a verified base-case center cell. - Banking Formatting Standards: Applies font color conventions (blue inputs, black formulas, purple same-tab links, green cross-tab links) and a restrained blue/grey fill palette. - Use Case: An analyst receives a CIM for a target company and needs an LBO model. Provide the template and assumptions, and the Skill builds each section with checkpoint verification, then recalculates the workbook before delivery. ## Quick Start Build an LBO model in Excel using my attached template with a 10x entry multiple, 60% debt financing, and a 5-year hold, showing IRR and MOIC with a 5x5 sensitivity table.

Frequently Asked Questions about lbo-model

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

FAQPage Schema
How do I build an LBO model in Excel with Python?▼

Use openpyxl to write formula strings like ws["D20"] = "=B5*B6" into a template workbook, then recalculate with a recalc script before delivery. Never hardcode computed values, so the model stays dynamic when inputs change.

How do I calculate IRR and MOIC in an LBO model?▼

IRR uses a cash flow series with negative investment and positive exit proceeds in consecutive periods, or XIRR with dates. MOIC equals total proceeds divided by total investment, formatted as a multiple like 0.00x.

Can I use my own LBO template with this Skill?▼

Yes, providing a template is the preferred path. The Skill copies your template's structure exactly and populates it with your assumptions, preserving its layout, sign conventions, and formatting rather than building from scratch.

Why does my LBO model have circular reference errors?▼

Circularity occurs when interest references ending debt balances that depend on cash flow affected by that interest. Calculate interest on beginning balances instead to break the loop: interest, cash flow, paydown, then ending balance.

Why does my sensitivity table show the same value in every cell?▼

Identical values mean formulas are not varying with the row and column inputs. Use mixed references like $A5 for row inputs and B$4 for column inputs, and verify the center cell equals the model's actual IRR or MOIC.

What are the limitations of building Excel models with openpyxl?▼

openpyxl does not calculate formulas, so the workbook must be recalculated with a separate script before delivery. Excel DATA TABLE functions also do not work, so sensitivity tables require explicit formulas referencing row and column headers.