Agent SkillsEronred/aso-skills › referral-program

referral-program

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用于设计、启动和优化应用内推荐/邀请/分享赚钱计划,涵盖奖励结构、反欺诈、深度链接及病毒系数测量。

skills/referral-program/SKILL.md Eronred/aso-skills

Trigger Scenarios

referral program invite a friend viral loop K-factor double-sided rewards

Install

npx skills add Eronred/aso-skills --skill referral-program -g -y
More Options

Use without installing

npx skills use Eronred/aso-skills@referral-program

指定 Agent (Claude Code)

npx skills add Eronred/aso-skills --skill referral-program -a claude-code -g -y

安装 repo 全部 skill

npx skills add Eronred/aso-skills --all -g -y

预览 repo 内 skill

npx skills add Eronred/aso-skills --list

SKILL.md

Frontmatter
{
    "name": "referral-program",
    "metadata": {
        "version": "1.0.0"
    },
    "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."
}

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>

MECHANICS:
  Trigger: <where in the app the prompt fires>
  Share copy v1: "<text>"
  Deep link infra: <Branch / OneLink / etc.>
  Reward delivery: <instant / on qualifying action>

FRAUD CONTROLS:
  - <list>

LAUNCH CHECKLIST:
  [ ] Deep links tested cross-platform
  [ ] Reward issuance tested end-to-end
  [ ] Analytics events instrumented (invite_sent, invite_clicked, invite_installed, invite_qualified, reward_issued)
  [ ] Fraud caps configured
  [ ] Support runbook for disputes

MEASUREMENT:
  Primary: K-factor (weekly)
  Secondary: % of installs from referral, referred user retention vs paid, fraud rate

Tooling

Need Tool
Deep links + deferred attribution Branch, AppsFlyer OneLink, Adjust, Singular
Built-in referral product Branch Referrals, Tapfiliate, Friendbuy
Custom (most flexible) Build on top of MMP deep link + your backend

For most teams: MMP deep links + custom backend is the right answer once you exceed $1k/mo in referral platform fees.

Common Mistakes

  • Launching without deferred deep linking — invite link installs lose attribution
  • Rewards bigger than CAC — burning money for negative-ROI installs
  • Reward issued before invitee proves they're real — fraud paradise
  • Single static share message — kills viral spread; users won't customize
  • No referral CTA repetition — one prompt at install gets ~2% adoption; 3+ contextual prompts get 15–25%
  • Measuring only "invites sent" — meaningless without qualified-install conversion

Cross-Skill Handoffs

  • Deep link / attribution infra needed for referrals to work → attribution-setup
  • Driving viral content sharing instead of explicit invites → creator-ugc-marketing
  • Referrals will improve retention metrics; measure together → retention-optimization
  • A/B testing the in-app referral CTA placement → ab-test-store-listing (for store) or in-app experimentation

Version History

  • e2a7c45 Current 2026-07-24 21:04

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Metadata

Files
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Version
f97c943
Hash
b4f565ba
Indexed
2026-07-24 21:04

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