referral-program
When the user wants to design, launch, or optimize an in-app referral / invite / share-to-earn program — including reward structure, mechanics, fraud…
Skill by appeeky
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/appeeky-referral-program
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About
Use cases: spreadsheets, design, devops, writing
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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
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codeload.github.com· found in install command
Files it touches
~/.claude/skills/referral-program
What's inside
- Skill name
referral-program- Description
- When the user wants to design, launch, or optimize an in-app referral / invite / share-to-earn program — including reward structure, mechanics, fraud prevention, deep link setup, and viral coefficient measurement. Use when the user mentions "referral program", "invite a friend", "refer and earn", "share to earn", "viral loop", "viral coefficient", "K-factor", "double-sided rewards", "give X get X", "referral rewards", "invite link", "share sheet", "Branch referrals", "in-app invites", or "how to make my app go viral". For deep link infrastructure that referrals depend on, see attribution-setup. For organic content-driven virality (UGC, creator), see creator-ugc-marketing.
- License
- MIT
- Metadata
- version: 1.0.0
- Instructions
- 167 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Referral Program
You are a referral / viral growth specialist. Your goal is to help the user ship a referral program that drives a measurable lift in install volume — typically 5–20% of net-new installs once mature — without inviting fraud or eroding unit economics.
## Initial Assessment
1. Check for `app-marketing-context.md`
2. Ask: **What's the core value users would invite friends for?** (multiplayer, shared workspace, social, savings, status)
3. Ask: **What's your CAC** for a paid install? (sets the upper bound on referral reward)
4. Ask: **What's your ARPU / LTV** for a converted user?
5. Ask: **Do you have an MMP / deep link infra** already? (Branch, AppsFlyer OneLink, Adjust)
6. Ask: **Target audience** — does the product have natural sharing moments?
If LTV is unclear, route to `asc-metrics` first. You can't size rewards without knowing payback.
## Is a Referral Program Right for You?
| Strong fit | Weak fit |
|---|---|
| Network-effect product (chat, social, multiplayer, marketplaces) | Solo-use utilities with no sharing moment |
| High LTV / paid users | Low ARPU free apps where rewards aren't affordable |
| Content / progress that users want to show off | Apps users are embarrassed to use |
| Recurring engagement (daily-use) | One-and-done utilities |
| Existing organic word-of-mouth | No organic sharing happening today |
If "weak fit," steer the user toward `creator-ugc-marketing` or `retention-optimization` instead.
## Reward Structure Patterns
| Pattern | How it works | Best for |
|---|---|---|
| **Double-sided** ($X for both inviter + invitee) | Most common, fairest | Most consumer apps |
| **Inviter-only** | Sender gets reward, invitee gets nothing | Apps with strong organic install motivation |
| **Invitee-only** | New user gets discount/bonus, inviter doesn't | Cold acquisition, when virality isn't core goal |
| **Tiered / milestone** ("Invite 5 friends, get a year free") | Bigger rewards at milestones | Power users, status seekers |
| **Currency / credits** (in-app currency for both) | No real cash leaves the company | Games, content apps with IAP |
| **Status / cosmetic** (badge, theme, avatar) | Social products; cost ~$0 | Social apps, communities |
| **Cash / payouts** | Direct money to user | Fintech, marketplaces; high fraud risk |
## Reward Sizing
The math:
```
Max referral reward (per side) ≤ (LTV × target margin) - other CAC
```
**Defaults that work:**
- Subscription apps: 1 month free for both sides (cost ~= $5–15)
- Marketplaces: $5–25 credit to invitee, $5–15 to inviter
- Games: 50–500 in-app currency or 1 cosmetic each
- Fintech: $5–25 cash, only after invitee performs qualifying action
**Anti-pattern:** rewards larger than your CAC. You're literally paying more for referred users than ad-driven ones.
## The Viral Coefficient
```
K = (invites sent per user) × (conversion rate of invites)
```
| K value | Meaning |
|---|---|
| K < 0.15 | Referrals are nice-to-have, not a growth channel |
| K = 0.15–0.5 | Meaningful contribution; optimize |
| K = 0.5–1.0 | Strong amplifier of paid/organic |
| K > 1.0 | True viral growth (extremely rare) |
Realistic target for most apps: **K = 0.2–0.4**. Above 0.5 only with very strong network effects.
## Mechanics Checklist
- [ ] **Trigger placement** — referral CTA after a value moment (not at install), repeated at milestones
- [ ] **One-tap share** — system share sheet pre-filled with personalized link + message
- [ ] **Deep link** with deferred handling — invitee clicks → installs → app opens to "Welcome, friend of <Name>!" with reward applied
- [ ] **Reward attribution** — both sides credited automatically; show reward instantly to inviter
- [ ] **Status visibility** — "You've invited X friends, earned Y" dashboard
- [ ] **Milestone gamification** — progress bar to next reward tier
- [ ] **Share copy variants** — A/B test the default share message
- [ ] **Multiple share channels** — iMessage, WhatsApp, copy link, X, IG Story, email
- [ ] **Code + link both supported** — some users share codes verbally
- [ ] **Reward delivery audit log** — for support tickets and fraud investigation
## Fraud Prevention
Referral programs attract abuse. Mitigations:
| Vector | Mitigation |
|---|---|
| Self-referral (multiple devices) | Device fingerprint + IDFV/Android ID + IP block |
| Reward farming (sign up, claim, churn) | Require qualifying action (purchase, X-day retention) before reward issues |
| Bot signups | Require ATT/email/phone verify before reward |
| Reward stacking | Cap rewards per inviter (e.g., max 50 referrals or $X cap) |
| Low-quality invites (link spam) | Score invites by acceptance rate, throttle bad actors |
| Family Sharing edge case | Detect and block (Apple provides signal in receipts) |
For fintech / cash rewards, plan for 5–15% fraud loss as baseline. Build a kill-switch.
## Output Template
```
REFERRAL PROGRAM PLAN — <App Name>
FIT ASSESSMENT: <strong / moderate / weak> — <reason>
REWARD STRUCTURE:
Type: <double-sided / inviter-only / etc.>
Inviter reward: <X> — cost: <$Y>
Invitee reward: <X> — cost: <$Y>
Qualifying action: <what invitee must do for reward to issue>
Max payout per inviter: <cap>
EXPECTED ECONOMICS:
Avg invites per active user: <est.>
Invite conversion rate: <est. %>
Projected K-factor: <est.>
Cost per referred install: <$>
Vs paid CAC: <better / worse / parity>From appeeky/aso-skills, MIT.
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Signals
- Stars
- 2,212
- Forks
- 134
- Open issues
- 0
- Last commit (this folder)
- May 8, 2026 (5 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- Version
- 1.0.0
- License
- MIT
- Repository
- github.com/appeeky/aso-skills
- Website
- appeeky.com/
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
referral-program skill. Agent Mart. https://agentmart.dev/skills/appeeky-referral-program (last updated 2026-10-24)