Builds discounted cash flow (DCF) valuation models in Excel with free cash flow projections, WACC calculations, and sensitivity analysis. Targets investment banking and corporate finance workflows. Use when asked to create a DCF model, calculate enterprise value, value a company, or build a valuation model. Trigger with "create a DCF model", "build a valuation", "calculate enterprise value", or "value this company". Make sure to use whenever the user needs company valuation or DCF analysis in Excel.
Installation
Details
Usage
After installing, this skill will be available to your AI coding assistant.
Verify installation:
npx agent-skills-cli listSkill Instructions
name: excel-dcf-modeler description: | Builds discounted cash flow (DCF) valuation models in Excel with free cash flow projections, WACC calculations, and sensitivity analysis. Targets investment banking and corporate finance workflows. Use when asked to create a DCF model, calculate enterprise value, value a company, or build a valuation model. Trigger with "create a DCF model", "build a valuation", "calculate enterprise value", or "value this company". Make sure to use whenever the user needs company valuation or DCF analysis in Excel. allowed-tools: "Read,Write,Edit,Glob,Grep,Bash(npx:*),AskUserQuestion" model: inherit version: "2.0.0" author: "Jeremy Longshore jeremy@intentsolutions.io" license: "Proprietary" compatible-with: claude-code tags: [dcf, valuation, financial-modeling, excel, investment-banking]
Excel DCF Modeler
Table of Contents
- Overview — Prerequisites — Instructions — Output — Examples — Error Handling — Resources
Overview
Generates professional 4-sheet DCF valuation models following investment banking standards. Automates free cash flow projections, terminal value calculations, and sensitivity analysis so analysts can produce IB-grade valuations from natural language inputs instead of building Excel models from scratch.
Prerequisites
- Node.js 18+
@negokaz/excel-mcp-serverMCP server configured- Claude Code 1.0+
Instructions
Step 1: Gather Inputs
Use AskUserQuestion to collect:
Required:
- Company name and ticker symbol
- Base year revenue (most recent fiscal year)
- Revenue growth rates for Years 1-5 (e.g., 15%, 12%, 10%, 8%, 6%)
- EBITDA margin %
- Tax rate % (default: 21% for US)
Optional (use defaults if not provided):
- D&A as % of revenue (default: 5%)
- CapEx as % of revenue (default: 4%)
- NWC as % of revenue (default: 10%)
- Terminal growth rate (default: 2.5%)
- WACC / discount rate (default: 10%)
- Net debt amount (default: $0)
- Shares outstanding (for per-share value)
Step 2: Validate Inputs
Before building, verify:
- Revenue growth rates are 0-30%
- EBITDA margin is positive
- Tax rate is 0-40%
- Terminal growth < WACC (model breaks if g >= WACC)
- WACC is 7-15%
If validation fails, explain the issue and ask for corrected inputs.
Step 3: Build 4-Sheet Model
Use the Excel MCP server to create:
Sheet 1 - Assumptions: Company info, revenue growth rates, profitability metrics, working capital, CapEx, tax rate, terminal growth, WACC. Color-code: blue for inputs, black for formulas.
Sheet 2 - FCF Projections (5 years): Revenue -> EBITDA -> EBIT -> NOPAT -> add back D&A -> subtract CapEx -> subtract Change in NWC -> Unlevered Free Cash Flow. All formulas link to Assumptions sheet. No hard-coded values.
Sheet 3 - Valuation: PV of each year's FCF, terminal value via Gordon Growth Model, PV of terminal value, enterprise value, equity value, per-share value.
Sheet 4 - Sensitivity Analysis: Two-way table: WACC (rows, +/-2% from base) vs Terminal Growth (columns, 1.5%-3.5%). Output: Enterprise Value at each combination. Apply conditional formatting (green=high, red=low).
Step 4: Format Professionally
- Currency format for monetary values
- Percentage format for rates (1 decimal)
- Freeze top row and left column
- Bold headers, cell borders
- Conditional formatting on sensitivity table
Step 5: Return Results
Report enterprise value, equity value, per-share value, terminal value as % of EV (flag if >80%), key assumptions used, and brief commentary on reasonableness vs industry.
Output
.xlsxfile with 4 sheets: Assumptions, FCF Projections, Valuation, Sensitivity Analysis- Summary text with enterprise value, equity value, and key metrics
- Warnings for any concerning assumptions (e.g., terminal value >80% of EV)
Examples
Basic DCF Request
User: "Create a DCF model for Tesla"
Response: Gathers inputs via questions, builds 4-sheet model.
Results:
- Enterprise Value: $847.3B
- Terminal Value: 68% of EV
- Implied per share: $243
Saved to: Tesla_DCF_Model.xlsx
Minimal Inputs
User: "Build a DCF but I don't have all the numbers"
Response: Uses industry-average assumptions for the company's sector.
All defaults documented in Assumptions sheet for easy adjustment.
Error Handling
| Scenario | Response |
|---|---|
| Terminal growth >= WACC | Explain mathematical issue, ask for corrected values |
| Missing critical inputs | Build with industry defaults, document clearly |
| Terminal value >80% of EV | Flag as warning, recommend revisiting growth assumptions |
| Negative FCF in projection | Flag concern, suggest reviewing margin/CapEx assumptions |
Edge Cases
- If user provides no company name, use "Company" as placeholder
- If revenue is in different currencies, note currency in all outputs
- If user wants multiple scenarios, create separate columns (Base/Bull/Bear)
Resources
- ${CLAUDE_SKILL_DIR}/references/REFERENCE.md - DCF best practices, industry assumptions, common mistakes
More by jeremylongshore
View allmanaging-environment-configurations: This skill enables Claude to manage environment configurations and secrets across different deployments using the environment-config-manager plugin. It is invoked when the user needs to generate, update, or retrieve configuration settings for various environments (e.g., development, staging, production). Use this skill when the user explicitly mentions "environment configuration," "secrets management," "deployment configuration," or asks to "generate config files". It helps streamline DevOps workflows by providing production-ready configurations based on best practices.
Automatically manages PostgreSQL backups with pgBackRest and Wasabi S3 storage when working with FairDB databases Activates when you request "fairdb backup manager" functionality.
generating-smart-commits: This skill generates conventional commit messages using AI analysis of staged Git changes. It automatically determines the commit type (feat, fix, docs, etc.), identifies breaking changes, and formats the message according to conventional commit standards. Use this when asked to create a commit message, write a Git commit, or when the user uses the `/commit-smart` or `/gc` command. It is especially useful after changes have been staged with `git add`.
generating-docker-compose-files: This skill enables Claude to generate Docker Compose configurations for multi-container applications. It leverages best practices for production-ready deployments, including defining services, networks, volumes, health checks, and resource limits. Claude should use this skill when the user requests a Docker Compose file, specifies application architecture involving multiple containers, or mentions needs for container orchestration, environment variables, or persistent data management in a Docker environment. Trigger terms include "docker-compose", "docker compose file", "multi-container", "container orchestration", "docker environment", "service definition", "volume management", "network configuration", "health checks", "resource limits", and ".env files".
