dcf-model

Build institutional-quality DCF valuation models in Excel with live formulas.

Updated Apr 9, 2026

Search Tags:#valuation #scenario analysis #dcf #wacc #excel modeling #sensitivity tables #terminal value

Ready to run

npx skills add https://github.com/MarbleSodas/Mavis --skill dcf-model-marblesodas

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dcf-model
Source: https://github.com/MarbleSodas/Mavis/tree/main/optional-skills/finance/dcf-model
Command: npx skills add https://github.com/MarbleSodas/Mavis --skill dcf-model-marblesodas

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires openpyxl, requests, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps you build a professional Discounted Cash Flow (DCF) Excel model for intrinsic equity valuation instead of doing error-prone valuation math manually.

Core Features & Use Cases

Institutional-grade DCF build in Excel: Creates a full DCF workflow (revenue projections, FCF build, WACC, terminal value, equity bridge) with investment-banking style structure.
Scenario modeling (Bear/Base/Bull): Uses a case selector to swap assumption sets and keep projections consistent across scenarios.
Full sensitivity analysis: Produces three bottom-of-sheet sensitivity grids (e.g., WACC vs terminal growth) with live recalculation formulas for every cell.
Professional auditability: Enforces “formulas over hardcodes,” requires source cell comments for inputs, and includes a troubleshooting guide to resolve common Excel errors.

Quick Start

Use the dcf-model skill to produce an Excel DCF model by confirming the retrieved historical inputs and assumptions at each build stage before finalizing projections and valuation outputs.

Frequently Asked Questions about dcf-model

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

FAQPage Schema

How do I build a DCF valuation model in Excel with live formulas?▼

Build a DCF valuation model in Excel by generating revenue projections, constructing free cash flow, calculating WACC and terminal value, and performing equity bridge calculations using live Excel formulas instead of hardcoded Python values.

What is the best way to run scenario analysis for Bear, Base, and Bull valuation cases?▼

Run scenario analysis for Bear, Base, and Bull valuation cases by using a case selector to swap assumption sets, keeping revenue projections and FCF builds consistent across all scenarios within the same Excel model.

Can I generate sensitivity tables for WACC and terminal growth rate in Excel?▼

Generate sensitivity tables for WACC and terminal growth rate by producing bottom-of-sheet sensitivity grids where every cell recalculates the DCF valuation output across assumption combinations using live Excel formulas.

Does openpyxl support headless generation of institutional DCF Excel models?▼

Openpyxl supports headless generation of institutional DCF Excel models by following openpyxl-compatible spreadsheet conventions to programmatically write live formulas, source cell comments, and investment-banking style structure without opening Excel.

Why should DCF valuation outputs use live Excel formulas instead of hardcoded Python results?▼

DCF valuation outputs should use live Excel formulas instead of hardcoded Python results to ensure professional auditability, allowing users to trace calculations, adjust assumptions, and trigger recalculation across sensitivity grids and scenario models.

What historical inputs do I need to start projecting free cash flow for intrinsic equity analysis?▼

To project free cash flow for intrinsic equity analysis, you need to confirm retrieved historical financial inputs and assumption sets at each build stage before finalizing revenue projections, WACC, and terminal value calculations.