claudegoodies
Skill

dcf-model

From NousResearch

Build institutional-quality DCF valuation models in Excel — revenue projections, FCF build, WACC, terminal value, Bear/Base/Bull scenarios, 5x5 sensitivity tables. Pairs with excel-author. Use for intrinsic-value equity analysis.

Facts

Repository
NousResearch/hermes-agent
Status
Actively maintained
Last commit
Source file
optional-skills/finance/dcf-model/SKILL.md

Source preview

The instructions Claude Code reads when this skill runs.

## Environment

This skill assumes **headless openpyxl** — you are producing an .xlsx file on disk.
Follow the `excel-author` skill's conventions for cell coloring, formulas, named ranges, and sensitivity tables.
Recalculate before delivery: `python /path/to/excel-author/scripts/recalc.py ./out/model.xlsx`.

# DCF Model Builder

## Overview

This skill creates institutional-quality DCF models for equity valuation following investment banking standards. Each analysis produces a detailed Excel model (with sensitivity analysis included at the bottom of the DCF sheet).

## Tools

- Default to using all of the information provided by the user and MCP servers available for data sourcing.

## Critical Constraints - Read These First

These constraints apply throughout all DCF model building. Review before starting:

**Formulas Over Hardcodes (NON-NEGOTIABLE):**
- Every projection, margin, discount factor, PV, and sensitivity cell MUST be a live Excel formula — never a value computed in Python and written as a number
- When using openpyxl: `ws["D20"] = "=D19*(1+$B$8)"` is correct; `ws["D20"] = calculated_revenue` is WRONG
- The only hardcoded numbers permitted are: (1) raw historical inputs, (2) assumption drivers (growth rates, WACC inputs, terminal g), (3) current market data (share price, debt balance)
- If you catch yourself computing something in Python and writing the result — STO
View full source on GitHub →

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