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dcf-model

Construct DCF valuation models in Excel with openpyxl and MCP data sources.

2|Updated Jun 8, 2026
Search Tags:#financial modeling#Excel#valuation#DCF#DCF analysis
One-click install
npx skills add https://github.com/vikrant-project/devil-agent-ai-platform --skill dcf-model-vikrant-project
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dcf-model
Source: https://github.com/vikrant-project/devil-agent-ai-platform/tree/main/agent_core/optional-skills/finance/dcf-model
Command: npx skills add https://github.com/vikrant-project/devil-agent-ai-platform --skill dcf-model-vikrant-project

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill automates the process of building and analyzing Discounted Cash Flow (DCF) valuation models for equity analysis, saving users time and ensuring accuracy in financial projections.

Core Features & Use Cases

  • Automated DCF Model Creation: Generate institutional-quality DCF models in Excel, with revenue projections, FCF build, WACC, terminal value, and sensitivity tables.
  • Customizable Scenarios: Support for Bear, Base, and Bull cases with detailed sensitivity analysis.
  • Data Validation: Robust data validation at each step to ensure accuracy.
  • Use Case: Ideal for financial analysts, investors, and corporate finance teams who need to quickly create and validate DCF models for investment analysis.

Quick Start

Use the dcf-model skill to create a DCF valuation model for a company using the provided data file 'company-financials.xlsx'.

Frequently Asked Questions about dcf-model

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

FAQPage Schema
How do I automate building a DCF valuation model in Excel?▼

Yes, you can build customizable Bear, Base, and Bull case scenarios for DCF analysis. The model supports detailed sensitivity analysis across these cases, allowing you to validate financial outcomes and test valuation limits against varying revenue and margin assumptions.

How does automated financial data validation work during DCF analysis?▼

Financial data validation works by incorporating robust checks at each step of the DCF analysis process. This ensures the accuracy of revenue, margin, and cash flow projections sourced from MCP data feeds before calculating the final equity valuation.

Do I need an MCP server to run DCF analysis and generate Excel models?▼

Yes, you need MCP server access to retrieve financial data for the DCF analysis. You also need the openpyxl Python library installed to construct and output the institutional-quality Excel valuation models programmatically.

What is the best way to perform sensitivity analysis for equity valuation?▼

The best way to perform sensitivity analysis for equity valuation is to construct DCF models with automated Bear, Base, and Bull case scenarios. This allows you to validate financial projections and evaluate cash flow outcomes against changing institutional assumptions.

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