# Dcf Model

Build a DCF valuation in Excel: FCF projections, WACC, terminal value, scenarios...

- Repository: https://github.com/ginlix-ai/LangAlpha (2232 stars)
- Category: Documents
- License: Apache-2.0
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add ginlix-ai/LangAlpha --skill dcf-model -g -a claude-code -y`
Codex: `npx skills add ginlix-ai/LangAlpha --skill dcf-model -g -a codex -y`
Cursor: `npx skills add ginlix-ai/LangAlpha --skill dcf-model -g -a cursor -y`
Gemini CLI: `npx skills add ginlix-ai/LangAlpha --skill dcf-model -g -a gemini-cli -y`

## Description

Build a DCF valuation in Excel: FCF projections, WACC, terminal value, scenarios, sensitivity grids, reverse DCF. Triggers on build a DCF, what is it worth, intrinsic value, fair value, price target from cash flows.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# DCF Model Builder

Builds an institutional-quality DCF as a live Excel workbook: four sheets, three scenarios, three sensitivity grids, and a valuation a portfolio manager can argue with. A model that computes correctly and says nothing about the stock is half the job, so the construction rules and the judgement rules below are one document.

Evidence labels, source tiers, staleness, the readiness posture and the intake limits: `.agents/skills/research-conventions/SKILL.md`, read before the first figure enters the workbook.

- `recalc.py` reports errors, the case selector does not move the model, or the valuation looks wrong: read `.agents/skills/dcf-model/TROUBLESHOOTING.md`.
- The revenue build needs the drivers of a particular sector, or the company is a bank, an insurer, a miner or a REIT: read `.agents/skills/dcf-model/references/sector-drivers.md`.

## Tools

- **fundamentals MCP**: `get_financial_statements`, `get_financial_ratios`, `get_growth_metrics`, `get_historical_valuation`; diluted shares come from here
- **macro MCP**: `get_treasury_rates`, `get_market_risk_premium`
- **`get_company_overview`**: share price, analyst consensus, earnings surprises, revenue segmentation
- **`yf_fundamentals` MCP**: `compare_valuations`, whose per-symbol record carries the `beta` field
- **`yf_analysis` MCP**: `get_growth_estimates` for consensus growth, one record per `period` (`0q`, `+1q`, `0y`, `+1y`, `+5y`, `-5y`)
- Web search and fetch, and user-provided data, as supplements

## Step 0: Before you build

The question budget, the shape of each question and what to do when no answer comes back are in `.agents/skills/research-conventions/references/intake.md`.

The forks that usually matter here: whether the user has an existing model to extend rather than a new build, the horizon, and whether the base case should follow guidance, consensus, or your own view.

**Three anchors are required, not optional.** A DCF without them produces a number nobody can act on:

The rest: https://github.com/ginlix-ai/LangAlpha/blob/HEAD/plugins/langalpha_research/skills/dcf-model/SKILL.md