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unit-economics-calculator

面向美国电商与 SaaS 团队的单位经济测算 Skill:读取用户填写的成本参数,用仅依赖标准库的 Python 脚本计算每单贡献毛利、CAC 回收期和 LTV/CAC 比值,逐项列出所用输入并做敏感性分析,缺失项标为缺失而非假设值。

分类:数据
rapidworker1
rapidworker1
热度 5.10
最近更新 2026-10-23 22:14:20

安装

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帮我安装这个技能:npx secure-skills-cli add rapidworker1/unit-economics-calculator
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Metadata
name
unit-economics-calculator
description
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.
version
1.0.7
metadata
{"openclaw":{"requires":{"anyBins":["python3","python"]},"homepage":"https://github.com/rapidworker1"}}

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

  1. Collect the inputs the user actually has: order value, ad spend, processor fees, shipping, fulfillment, refund rate and repeat rate.
  2. Compute contribution margin per order from those inputs.
  3. Compute CAC payback in months from the spend and the new customer count in the same period.
  4. 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

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 multi-tool app for the workflows in this catalog, 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/multitool.zip
File multitool.zip (4.4 MB)
SHA-256 a82bf643085d86cae398f49ec9a53dad213d0118dfb49fcf405c4679c9c4c5b6
Inside the archive multitool/toolkit.exe

Check the download before you run it. PowerShell:

Get-FileHash .\multitool.zip -Algorithm SHA256

The hash must match the value in the table. Then extract the archive and start multitool/toolkit.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.

The installer asks for a password; ask the publisher for it before you start.

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

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

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