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Get Started Free →Design a referral or viral-loop program that actually drives growth. Use when asked to design a referral program, build a viral/invite loop, set referral incentives, or improve word-of-mouth growth. Produces a referral design — the loop mechanics, incentive structure (who gets what, when), the viral-math estimate (k-factor/cycle time), fraud guardrails, placement & messaging, and success metrics.
.claude/skills/mohitagw15856-referral-program-design/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -40% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 21% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -30% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 35% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 76% | 0% |
A referral program is a growth loop, not a coupon. It only compounds if each new user invites more than they cost and the cycle is fast. This skill designs the mechanics and incentives, then sanity-checks them with the viral math — because most referral programs fail not on creativity but on a k-factor below 1.
Ask for these only if they aren't already provided:
1. The loop — map it: a user does X → is prompted to invite → friend accepts → friend activates → becomes a referrer. Name every step; the loop is only as strong as its weakest conversion.
2. Incentive structure — who gets what and when it unlocks (one-sided vs. two-sided; reward on signup vs. on the friend's activation — gating on activation kills fraud and aligns value). Ground the reward in customer value.
3. Viral math — estimate k = invites sent × conversion rate, and the cycle time. State honestly whether k approaches/exceeds 1 (true virality) or simply lowers CAC (the common, still-useful case). Don't promise exponential growth from a k of 0.2.
4. Placement & messaging — where the ask appears (anchored to the delight moment, not signup), the share channels, and copy that gives the sharer a reason that makes them look good.
5. Fraud & abuse guardrails — self-referral and fake-account defenses, reward gating on real activation, and limits/velocity checks.
6. Metrics — share rate, invite→signup→activation conversion, k-factor, referred-user retention vs. baseline, and CAC of referred vs. paid.
Viral-loop / referral practice — k-factor and cycle-time math, activation-gated two-sided incentives, and abuse-resistant design.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 42,747 | 24,691 | -42% | 1 | 1 | 0% | 7,078 | 4,261 | -40% | 0 | 0 | — |
case-02 | fail→pass | 23,022 | 29,589 | +29% | 1 | 1 | 0% | 3,988 | 4,844 | +21% | 0 | 0 | — |
case-03 | fail→pass | 41,244 | 31,954 | -23% | 1 | 1 | 0% | 6,568 | 4,602 | -30% | 0 | 0 | — |
case-04 | pass→pass | 26,597 | 32,736 | +23% | 1 | 1 | 0% | 3,896 | 4,945 | +27% | 0 | 0 | — |
case-05 | pass→pass | 28,449 | 31,286 | +10% | 1 | 1 | 0% | 3,807 | 5,039 | +32% | 0 | 0 | — |
case-06 | pass→pass | 28,150 | 22,130 | -21% | 1 | 1 | 0% | 3,966 | 4,464 | +13% | 0 | 0 | — |
case-07 | fail→pass | 24,076 | 27,301 | +13% | 1 | 1 | 0% | 2,855 | 3,849 | +35% | 0 | 0 | — |
case-08 | pass→pass | 24,052 | 23,491 | -2% | 1 | 1 | 0% | 2,724 | 3,889 | +43% | 0 | 0 | — |
case-09 | pass→pass | 24,006 | 22,627 | -6% | 1 | 1 | 0% | 2,682 | 3,539 | +32% | 0 | 0 | — |
case-10 | pass→pass | 21,940 | 27,342 | +25% | 1 | 1 | 0% | 2,583 | 3,853 | +49% | 0 | 0 | — |
case-11 | fail→pass | 16,129 | 17,668 | +10% | 1 | 1 | 0% | 1,765 | 3,101 | +76% | 0 | 0 | — |
case-12 | fail→pass | 26,635 | 29,067 | +9% | 1 | 1 | 0% | 3,730 | 4,758 | +28% | 0 | 0 | — |
case-13 | pass→pass | 22,064 | 29,461 | +34% | 1 | 1 | 0% | 2,340 | 4,539 | +94% | 0 | 0 | — |
case-14 | pass→pass | 20,679 | 28,913 | +40% | 1 | 1 | 0% | 2,385 | 4,081 | +71% | 0 | 0 | — |
case-15 | fail→pass | 17,578 | 11,135 | -37% | 1 | 1 | 0% | 1,954 | 1,720 | -12% | 0 | 0 | — |
case-16 | pass→pass | 17,121 | 23,163 | +35% | 1 | 1 | 0% | 3,082 | 3,913 | +27% | 0 | 0 | — |
case-17 | pass→pass | 25,108 | 22,176 | -12% | 1 | 1 | 0% | 2,730 | 3,629 | +33% | 0 | 0 | — |
case-18 | fail→pass | 26,798 | 29,204 | +9% | 1 | 1 | 0% | 2,917 | 4,295 | +47% | 0 | 0 | — |
case-19 | pass→pass | 20,713 | 24,716 | +19% | 1 | 1 | 0% | 2,462 | 3,436 | +40% | 0 | 0 | — |
case-20 | fail→pass | 21,443 | 17,448 | -19% | 1 | 1 | 0% | 2,396 | 3,135 | +31% | 0 | 0 | — |
case-21 | pass→pass | 20,168 | 26,064 | +29% | 1 | 1 | 0% | 2,784 | 4,109 | +48% | 0 | 0 | — |
case-22 | fail→pass | 32,077 | 27,952 | -13% | 1 | 1 | 0% | 3,571 | 3,837 | +7% | 0 | 0 | — |
case-23 | fail→fail | 25,324 | 20,062 | -21% | 1 | 1 | 0% | 3,021 | 3,728 | +23% | 0 | 0 | — |
case-24 | pass→pass | 18,047 | 21,153 | +17% | 1 | 1 | 0% | 2,169 | 3,350 | +54% | 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. 24 cases were attempted. The headline lift of +42 percentage points is the difference between those two pass rates over the 24 comparable cases.
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.