dcf-modeling

dcf-modeling is a skill for Claude Code, Codex from event4u-app/agent-config. It costs 55 tokens per session (1,218 once invoked), scanned A, original, MIT.

A discounted cash flow (DCF) valuation method estimates what an investment or company is worth today from its expected future cash flows.

In plain words
What is it for?
It helps compare buy, build, and partner choices; assess acquisition targets; and test how value changes when growth, discount rate, or terminal value assumptions change.
Why use it?
It gives you an intrinsic-value estimate instead of relying only on market multiples or a seller’s projected returns.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It helps compare buy, build, and partner choices; assess acquisition targets; and test how value changes when growth, discount rate, or terminal value assumptions change.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/event4u-app/agent-config/dcf-modeling
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add event4u-app/agent-config --skill dcf-modeling
Clone the repo
git clone --depth 1 https://github.com/event4u-app/agent-config

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for dcf-modeling

README.md
[![agentmods](https://agentmods.dev/badge/skills/event4u-app/agent-config/dcf-modeling/github.svg)](https://agentmods.dev/skills/event4u-app/agent-config/dcf-modeling)
Your own site
<a href="https://agentmods.dev/skills/event4u-app/agent-config/dcf-modeling"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/dcf-modeling/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for dcf-modeling

Your own site · 80×15
<a href="https://agentmods.dev/skills/event4u-app/agent-config/dcf-modeling"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/dcf-modeling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 55 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,218 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00055 $0.01218
Opus 5 $0.00028 $0.00609
Sonnet 5 $0.00011 $0.00244
Haiku 4.5 $0.00006 $0.00122

Measured 7d ago against content hash d4ec8c4e7324, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

dcf-modeling scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

src/skills/dcf-modeling/SKILL.md · 99 lines

How it starts

The opening of the file, as written. The whole thing — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.

dcf-modeling

When to use

  • A buy-build-or-partner decision needs an intrinsic-value anchor, not just a multiple.
  • A board pack asks for sensitivity to discount rate or terminal-growth assumptions.
  • An acquisition target's seller-deck IRR claims need a counter-model.

Do NOT use for revenue forecasting alone, market-sizing, or comp-multiple-only screens — those route elsewhere (see Related Skills).

Procedure

Step 0: Inspect

  1. Confirm the target has ≥3 years of audited or reviewed financials, or a clearly-labelled forecast that names every assumption.
  2. Note the cognition cluster: this is intrinsic-value cognition, not multiple-arbitrage.

Step 1: Lock the assumption table

  1. Pull or estimate the five drivers — revenue growth (per year, declining to terminal), EBIT margin path, tax rate, capex/sales, change in net working capital/sales.
  2. Decompose WACC: cost of equity (CAPM — risk-free + β × ERP), cost of debt (after-tax), capital structure target weights.
  3. Pick a terminal-value method once — either Gordon-growth (FCFF_t+1 / (WACC − g)) or exit-multiple. Naming both inflates spurious precision.

Step 2: Project free cash flow

  1. Build a 5-year FCFF row: EBIT × (1 − t) + D&A − Capex − ΔNWC.
  2. Discount each year by 1 / (1 + WACC)^t.
  3. Compute terminal value at year 5, discount back.
  4. Sum PV(FCFF) + PV(TV) = enterprise value. Subtract net debt → equity value.

Step 3: Sensitivity grid

  1. Build a 5×5 grid: WACC ±200 bps × terminal growth ±100 bps (or exit multiple ±2 turns).
  2. Flag the corner cells where equity value flips sign or moves >25% from base — those are the load-bearing assumptions.

Step 4: Validate

  1. Cross-check implied EV/EBITDA against trading comps. If your DCF prints 22× and the sector trades at 11×, the assumptions are wrong, not the market.
  2. State the two assumptions that drive >50% of the valuation. If you can't name them, the model is undisciplined.

Gotcha

  • Terminal value usually carries 60–80% of total PV. Treating TV as a footnote is the most common DCF malpractice.
  • WACC sensitivity is non-linear near WACC ≈ g; the Gordon formula explodes. Cap displayed cells; don't pretend the corner is a real number.
  • Forecasted FCFF that grows faster than revenue forever implies infinite margin expansion — the model will silently smuggle it in unless you bound EBIT margin at a stated ceiling.
  • Synergies in an M&A DCF belong in a separate column. Comingling them with standalone FCFF is how acquirers overpay.

Read the full file on GitHub · 99 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 7d ago First seen · 99 lines · 55 tokens per session scan A d4ec8c4e7324

Subscribe to this mod's changes

dcf-modeling is a skill published in the GitHub repository event4u-app/agent-config (10 stars, last pushed today), licensed MIT. It adds 55 tokens to every session and 1,218 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.