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.
npx skills add event4u-app/agent-config --skill dcf-modelinggit clone --depth 1 https://github.com/event4u-app/agent-configWrote 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.
[](https://agentmods.dev/skills/event4u-app/agent-config/dcf-modeling)<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.
<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>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.
| Model | Per session | Once 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 |
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.
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
- Confirm the target has ≥3 years of audited or reviewed financials, or a clearly-labelled forecast that names every assumption.
- Note the cognition cluster: this is intrinsic-value cognition, not multiple-arbitrage.
Step 1: Lock the assumption table
- 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.
- Decompose WACC: cost of equity (CAPM — risk-free + β × ERP), cost of debt (after-tax), capital structure target weights.
- 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
- Build a 5-year FCFF row:
EBIT × (1 − t) + D&A − Capex − ΔNWC. - Discount each year by
1 / (1 + WACC)^t. - Compute terminal value at year 5, discount back.
- Sum PV(FCFF) + PV(TV) = enterprise value. Subtract net debt → equity value.
Step 3: Sensitivity grid
- Build a 5×5 grid: WACC ±200 bps × terminal growth ±100 bps (or exit multiple ±2 turns).
- Flag the corner cells where equity value flips sign or moves >25% from base — those are the load-bearing assumptions.
Step 4: Validate
- 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.
- 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.
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.
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.
- 7d ago First seen · 99 lines · 55 tokens per session scan A d4ec8c4e7324
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.
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