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Get Started Free →Model the unit economics of a business — CAC, LTV, payback, contribution margin — from real inputs. Use when asked to calculate unit economics, work out LTV:CAC, find the payback period, or check whether a business model is viable per customer. Produces a computed unit-economics summary (LTV, CAC, ratio, payback, contribution margin) with a verdict and the levers that move it most.
.claude/skills/mohitagw15856-unit-economics/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 4% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -11% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 2% | 0% |
| case-17 | ✗→✓ | ▲ Improved | 39% | 0% |
| case-04 | ✓→✗ | ▼ Worse | 38% | 0% |
A business is only viable if each customer is worth more than it costs to acquire and serve. This skill computes the core unit economics — CAC, LTV, the LTV:CAC ratio, payback period, and contribution margin — from real numbers (not vibes), states a clear verdict against the rule-of-thumb benchmarks, and shows which lever moves the model most.
Ask for these only if they aren't already provided:
1. The numbers — computed, with the formula shown (use the helper script so they're consistent):
| Metric | Value | Benchmark | |---|---|---| | Lifetime (1/churn) | | | | LTV (ARPA × margin ÷ churn) | | | | CAC | | | | LTV : CAC | | ≥ 3:1 healthy | | Payback (months) | | < 12 healthy | | Contribution margin | | |
2. Verdict — healthy / borderline / underwater, in one line, against the benchmarks (LTV:CAC ≥ 3, payback < 12 months).
3. Biggest levers — which input, improved realistically, moves the model most (usually churn or CAC), with the rough effect.
4. Caveats — where the inputs are assumptions vs. measured, and what to validate before betting on this.
scripts/unit_econ.py (stdlib only) computes the model so the numbers are calculated, not estimated:
bash# in.json: {"arpa": 50, "gross_margin": 0.8, "monthly_churn": 0.03, "cac": 400} python3 scripts/unit_econ.py in.json python3 scripts/unit_econ.py in.json --json
SaaS unit-economics practice (David Skok / for Entrepreneurs) — margin-based LTV, LTV:CAC ≥ 3, payback < 12 months.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-05 | pass→pass | 21,496 | 20,289 | -6% | 1 | 1 | 0% | 2,885 | 3,410 | +18% | 0 | 0 | — |
case-01 | fail→fail | 24,702 | 19,086 | -23% | 1 | 1 | 0% | 3,522 | 3,361 | -5% | 0 | 0 | — |
case-02 | fail→pass | 23,047 | 16,404 | -29% | 1 | 1 | 0% | 2,891 | 2,996 | +4% | 0 | 0 | — |
case-03 | fail→fail | 27,542 | 20,445 | -26% | 1 | 1 | 0% | 3,908 | 3,651 | -7% | 0 | 0 | — |
case-04 | pass→fail | 23,323 | 23,875 | +2% | 1 | 1 | 0% | 2,626 | 3,637 | +38% | 0 | 0 | — |
case-06 | pass→pass | 21,759 | 28,973 | +33% | 1 | 1 | 0% | 3,023 | 5,286 | +75% | 0 | 0 | — |
case-07 | pass→pass | 14,782 | 21,445 | +45% | 1 | 1 | 0% | 2,913 | 4,153 | +43% | 0 | 0 | — |
case-08 | fail→fail | 24,128 | 17,491 | -28% | 1 | 1 | 0% | 2,798 | 2,945 | +5% | 0 | 0 | — |
case-09 | fail→pass | 19,742 | 12,909 | -35% | 1 | 1 | 0% | 1,901 | 1,693 | -11% | 0 | 0 | — |
case-10 | pass→pass | 20,404 | 23,455 | +15% | 1 | 1 | 0% | 2,820 | 4,018 | +42% | 0 | 0 | — |
case-11 | fail→pass | 22,680 | 18,521 | -18% | 1 | 1 | 0% | 3,040 | 3,097 | +2% | 0 | 0 | — |
case-12 | pass→pass | 17,170 | 27,806 | +62% | 1 | 1 | 0% | 1,938 | 3,933 | +103% | 0 | 0 | — |
case-13 | fail→fail | 20,689 | 18,375 | -11% | 1 | 1 | 0% | 2,762 | 3,070 | +11% | 0 | 0 | — |
case-14 | pass→pass | 23,073 | 39,920 | +73% | 1 | 1 | 0% | 2,765 | 4,941 | +79% | 0 | 0 | — |
case-15 | pass→fail | 15,994 | 20,588 | +29% | 1 | 1 | 0% | 2,020 | 4,327 | +114% | 0 | 0 | — |
case-16 | pass→pass | 21,778 | 16,318 | -25% | 1 | 1 | 0% | 2,270 | 3,007 | +32% | 0 | 0 | — |
case-17 | fail→pass | 18,555 | 19,902 | +7% | 1 | 1 | 0% | 2,528 | 3,505 | +39% | 0 | 0 | — |
case-18 | pass→pass | 20,124 | 20,804 | +3% | 1 | 1 | 0% | 3,380 | 4,228 | +25% | 0 | 0 | — |
case-19 | pass→pass | 15,605 | 20,415 | +31% | 1 | 1 | 0% | 2,469 | 4,059 | +64% | 0 | 0 | — |
case-20 | fail→fail | 17,896 | 36,625 | +105% | 1 | 1 | 0% | 2,442 | 4,647 | +90% | 0 | 0 | — |
case-21 | pass→pass | 13,105 | 12,495 | -5% | 1 | 1 | 0% | 1,119 | 2,122 | +90% | 0 | 0 | — |
case-22 | pass→pass | 16,410 | 23,688 | +44% | 1 | 1 | 0% | 2,511 | 4,651 | +85% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +9 percentage points is the difference between those two pass rates over the 22 comparable cases. 2 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.