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Get Started Free →Design a referral program that drives real word-of-mouth growth. Use when asked to build a referral or refer-a-friend program, create an incentive/reward structure, or turn happy users into a growth channel. Produces the incentive design (who gets what, when), the mechanics and trigger moment, fraud guardrails, and the unit-economics check — a program that pays back, not one that just burns budget.
.claude/skills/mohitagw15856-referral-program/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -17% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 3% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 10% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -1% | 0% |
| case-14 | ✗→✓ | ▲ Improved | -8% | 0% |
A referral program turns happy customers into a growth channel — but most fail because the incentive is wrong, the ask comes at the wrong moment, or the economics don't work. This skill designs one that does: the right reward for both sides, the trigger at peak satisfaction, fraud guardrails, and a payback check so it's growth, not a giveaway.
Ask for these only if they aren't already provided:
1. Incentive design — who gets what, and when it pays out:
| Side | Reward | Triggers when | Why this reward | |---|---|---|---| | Referrer | | (e.g. friend's first purchase) | | | Referred friend | | (e.g. on signup) | |
Double-sided usually beats one-sided. Reward the outcome you want (paid conversion), not just a click/signup.
2. The ask moment & mechanics — when to prompt (right after the aha moment / a great experience), where (in-product, email, post-purchase), and the share flow (unique link/code, how it's tracked, how rewards are granted). Keep it one or two clicks.
3. The message — a short, shareable framing the referrer would actually send (helping a friend, not spamming for a kickback).
4. Fraud & abuse guardrails — self-referral, fake accounts, reward farming; the checks (reward on real conversion, limits, verification).
5. Unit-economics check — total reward cost per successful referral vs. CAC and LTV. The program must acquire customers below your other channels' CAC (or clearly cheaper than paid) to be worth running. State the breakeven.
6. Measure & iterate — participation rate, referrals per advocate, conversion of referred users, and referral CAC vs. payback. What to tune.
Referral/viral-growth practice (double-sided incentives, trigger-at-aha, referral CAC vs. LTV, fraud guardrails).
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 37,319 | 36,314 | -3% | 1 | 1 | 0% | 6,241 | 5,167 | -17% | 0 | 0 | — |
case-02 | fail→fail | 37,481 | 23,893 | -36% | 1 | 1 | 0% | 5,810 | 4,275 | -26% | 0 | 0 | — |
case-03 | pass→fail | 33,482 | 20,614 | -38% | 1 | 1 | 0% | 4,868 | 4,444 | -9% | 0 | 0 | — |
case-04 | fail→pass | 28,320 | 27,609 | -3% | 1 | 1 | 0% | 3,801 | 3,922 | +3% | 0 | 0 | — |
case-05 | pass→pass | 28,094 | 26,531 | -6% | 1 | 1 | 0% | 3,679 | 3,803 | +3% | 0 | 0 | — |
case-06 | fail→pass | 27,948 | 26,661 | -5% | 1 | 1 | 0% | 3,494 | 3,832 | +10% | 0 | 0 | — |
case-07 | pass→pass | 28,309 | 29,604 | +5% | 1 | 1 | 0% | 3,963 | 4,048 | +2% | 0 | 0 | — |
case-08 | pass→pass | 20,539 | 20,725 | +1% | 1 | 1 | 0% | 2,993 | 3,471 | +16% | 0 | 0 | — |
case-09 | fail→pass | 25,089 | 26,129 | +4% | 1 | 1 | 0% | 4,132 | 4,092 | -1% | 0 | 0 | — |
case-10 | fail→fail | 23,904 | 26,328 | +10% | 1 | 1 | 0% | 2,995 | 4,185 | +40% | 0 | 0 | — |
case-11 | pass→pass | 24,477 | 24,052 | -2% | 1 | 1 | 0% | 3,772 | 3,653 | -3% | 0 | 0 | — |
case-12 | pass→pass | 29,434 | 26,391 | -10% | 1 | 1 | 0% | 3,723 | 3,740 | +0% | 0 | 0 | — |
case-13 | fail→fail | 34,742 | 30,432 | -12% | 1 | 1 | 0% | 4,479 | 4,305 | -4% | 0 | 0 | — |
case-14 | fail→pass | 33,037 | 24,081 | -27% | 1 | 1 | 0% | 4,311 | 3,980 | -8% | 0 | 0 | — |
case-15 | fail→fail | 33,516 | 27,949 | -17% | 1 | 1 | 0% | 3,961 | 3,950 | -0% | 0 | 0 | — |
case-16 | fail→fail | 60,514 | 30,402 | -50% | 1 | 1 | 0% | 4,208 | 4,314 | +3% | 0 | 0 | — |
case-17 | fail→fail | 30,226 | 27,747 | -8% | 1 | 1 | 0% | 4,015 | 4,026 | +0% | 0 | 0 | — |
case-18 | fail→pass | 26,345 | 29,235 | +11% | 1 | 1 | 0% | 3,546 | 4,753 | +34% | 0 | 0 | — |
case-19 | pass→fail | 24,608 | 39,838 | +62% | 1 | 1 | 0% | 3,355 | 6,827 | +103% | 0 | 0 | — |
case-20 | pass→fail | 19,566 | 30,689 | +57% | 1 | 1 | 0% | 2,482 | 4,740 | +91% | 0 | 0 | — |
case-21 | pass→pass | 29,668 | 23,240 | -22% | 1 | 1 | 0% | 3,294 | 3,818 | +16% | 0 | 0 | — |
case-22 | pass→pass | 28,137 | 26,052 | -7% | 1 | 1 | 0% | 3,145 | 3,536 | +12% | 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 +14 percentage points is the difference between those two pass rates over the 22 comparable cases. 4 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.