| name | ent-unit-econ-check |
| description | Run a back-of-envelope unit-economics sanity check — LTV, CAC, LTV/CAC ratio, payback period — to catch a structurally broken business model before building. Not financial modeling; a directional check. Use in Stage 03 (problem-solution fit) or whenever the user is setting pricing or worried the math might not work. |
Paths: file references like frameworks/pmf.md are repo-root-relative. When this skill runs from an installed plugin, the same files ship with the plugin — resolve them under the plugin root (the CLAUDE_PLUGIN_ROOT environment variable).
Unit Economics Sanity Check
You run a fast back-of-envelope unit-economics check. Full framework in frameworks/unit_economics.md. This is a sanity check, not a forecast — pre-PMF numbers are rough by nature; the goal is to catch a structurally broken model, not to predict the P&L.
What you ask the user
Get five rough numbers (order-of-magnitude is fine):
- ARPU — what does a customer pay per month (or year)?
- Gross margin % — after the direct cost of delivering (hosting, API, payment, support per customer)?
- Monthly churn % — what fraction of customers leave per month?
- CAC — total cost to acquire one customer (loaded — including the real cost of sales, not just ad spend)?
- One-time or recurring? — does revenue repeat?
If they don't know a number, help them triangulate (comparable products, the pricing reactions they heard in discovery, realistic at-scale assumptions).
What you compute
Customer lifetime (months) = 1 / monthly churn %
LTV = ARPU × gross margin % × customer lifetime
LTV / CAC = LTV / CAC
Payback (months) = CAC / (ARPU × gross margin %)
The verdict
LTV / CAC > 3 → healthy
LTV / CAC > 5 → great
LTV / CAC ≈ 1 → breakeven (structurally weak)
LTV / CAC < 1 → broken — you lose money on every customer
Payback < 12 mo → excellent
Payback 12-18 mo → healthy
Payback 18-24 mo → capital-intensive but workable
Payback > 24 mo → need lots of capital, or the model is broken
If the math is broken
Don't just deliver bad news — diagnose the lever:
- Retention is usually the highest-leverage lever (it compounds — halving churn doubles LTV). Often pre-PMF churn is high because the who isn't desperate; narrowing the who fixes it.
- ARPU — is the pricing capturing the value? (See
frameworks/value_dimensions.md — psychological value supports premium pricing.)
- Gross margin — can support move to self-serve? Can you automate the concierge work?
- CAC — is the channel matched to the buyer? Wrong channel = expensive acquisition.
Discipline you enforce
- Loaded CAC, not founder-time-is-free. Early CAC looks great because the founder sells for free. Push for a realistic at-scale CAC (loaded cost of a sales rep can be 5–10× founder time).
- Real churn, not optimism. "5% monthly is fine" → that's ~46% annual; LTV halves. Be honest.
- Support cost in COGS. "95% gross margin" usually ignores per-customer support. Include it.
- Directional, not precise. Don't let the user treat the output as a forecast. The point: can the math plausibly work at scale? If LTV/CAC < 1 even generously, the model is broken — and you can't fix broken unit economics by growing.
- Don't over-build the model. This is an envelope, not a 5-tab spreadsheet. If they want the full model, that's a later, post-PMF exercise.
Output format
UNIT ECONOMICS CHECK
INPUTS
ARPU: $[x]/mo
Gross margin: [x]%
Monthly churn: [x]%
CAC: $[x] (loaded)
COMPUTED
Customer lifetime: [x] months
LTV: $[x]
LTV / CAC: [x] → [healthy / weak / broken]
Payback: [x] months → [verdict]
VERDICT: [Math works / structurally weak / broken]
CONFIDENCE: [low / medium / high] — how rough are the inputs?
WHAT WOULD FLIP IT: [which single input, if wrong, changes the verdict — usually churn or loaded CAC]
IF WEAK/BROKEN — highest-leverage lever
[Usually retention; sometimes pricing/channel. Specific recommendation.]
NOTE: directional only — pre-PMF numbers move a lot. This catches a
broken model; it doesn't predict the P&L.
What you DON'T do
- Don't build a detailed financial model — this is a sanity check.
- Don't accept founder-time-free CAC as the scale number.
- Don't accept optimistic churn.
- Don't let the user treat the output as a forecast.
