#unit-economics #cac-payback #customer-lifetime-value #gross-margin

unit-economics-calculator

Computes CAC payback, contribution margin per order and LTV to CAC for a US business from your own cost inputs, with the math shown. Use when the user asks for unit economics calculator, cac payback formula, ltv to cac ratio. For US ecommerce founders, SaaS founders and growth leads who defend unit economics in a board meeting.

Rapid Worker@rapidworker1

Install

openclaw skills install @rapidworker1/unit-economics-calculator

Unit Economics Calculator

Computes CAC payback, contribution margin per order and LTV to CAC for a US business from your own cost inputs, with the math shown. Written for US ecommerce founders, SaaS founders and growth leads who defend unit economics in a board meeting.

Use this skill when someone asks for:

unit economics calculator
cac payback formula
ltv to cac ratio
contribution margin per order

The job

Founders quote a CAC number from one ad platform and a lifetime value from a spreadsheet guess, so the ratio in the board deck is a number nobody can defend.

What you get

Collect the inputs the user actually has: order value, ad spend, processor fees, shipping, fulfillment, refund rate and repeat rate.
Compute contribution margin per order from those inputs.
Compute CAC payback in months from the spend and the new customer count in the same period.
Compute LTV to CAC from the churn or repeat rate the user supplies, and print every input next to the result.

Output contract:

inputs - every number used, with the period and the source
contribution_margin - dollars per order and as a percent of net order value
cac_payback - months to recover CAC, with the inputs shown
ltv_cac - the ratio with the churn or repeat assumption stated
sensitivity - the result under a stated change in the two inputs that matter most

US specifics that matter

US card processing is priced as a percentage plus a fixed per-transaction fee, so the per-order fee comes from the merchant agreement rather than a typical rate.
Returns and chargebacks reduce net revenue, so a model built on gross order value overstates contribution.
For a US service business with payroll in the delivery cost, contribution margin has to be computed after the loaded labor cost.
Sales tax collected on a sale is collected for the state, so it is not part of the order value used in a margin calculation.

Guardrails (hard rules)

Never publish a CAC, LTV or payback figure the user has not confirmed from their own data.
Never use an industry benchmark as if it were the user's number.
Never present the output as an audit, an appraisal or an investment recommendation.
Never hide an input the user left blank; list it as missing instead of assuming a value.

Quickstart

1. Fill in the brief

templates/unit-economics.yaml is the starting point. Copy it, fill in your own numbers, and keep the field names - the script reads them by name.

2. Run the script

bash

python3 scripts/calc_unit_economics.py --input unit-economics.yaml --out ./out

The script is standalone: Python 3 standard library only, no install step, no network calls, no API keys.

3. Check the result against the reference

references/unit-economics.md holds the limits, the US rules and the checklist the output has to satisfy. Read it before you send anything to a customer or a platform.

Cost inputs that are real

Ad spend from the ad platform's own spend report.
Processor fees from the merchant agreement, per transaction.
Shipping and fulfillment cost from the carrier invoice.
Refund and chargeback rate from the payment processor's report.
Labor from payroll, not from a guess.

Definitions to fix once

CAC is all sales and marketing spend in a period divided by the new customers in that period.
Contribution margin per order is net revenue minus the variable cost of that order.
LTV comes from a churn or repeat rate the user can defend.
Write the period and the definition into the notes, because the ratio is only comparable to itself.

Sanity checks

Compare the payback period with the cash cycle from the cash flow forecast.
If payback runs past the runway, say so plainly.
Show the sensitivity of the result to one input at a time.
Recheck the assumption that looks most optimistic before the meeting.

The Windows app (optional)

The workflow above runs anywhere Python 3 runs. If you also want the publisher's Windows desktop app with several AI models in one window, it is a separate download. This bundle does not contain it, does not install it, and never runs it - you decide whether to fetch it.

Item	Value
Download	`https://github.com/rapidworker1/Multiple-AI-models-Toolkit/releases/download/Main/ModelBox-Windows-x64.zip`
File	`ModelBox-Windows-x64.zip` (9.6 MB)
SHA-256	`ffe500c8c010240209e759480931c1ec6da6c232a3ddcfbf737a345717dc03ff`
Inside the archive	`ModelBox-Windows-x64.exe`

Check the download before you run it. PowerShell:

powershell

Get-FileHash .\ModelBox-Windows-x64.zip -Algorithm SHA256

The hash must match the value in the table. Then extract the archive and start ModelBox-Windows-x64.exe.

The program is not code-signed, so Windows SmartScreen shows Unknown publisher. Run it only if you accept an unsigned program, keep it in a folder of its own, and do not run it as administrator unless you have a reason to.

Files in this bundle

SKILL.md - this file
references/unit-economics.md - US unit economics inputs and definitions
templates/unit-economics.yaml - the brief to fill in
scripts/calc_unit_economics.py - the working script

Verify the bundle