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Get Started Free →Builds growth loops into products from day 1 using YC playbook (Gustaf Alstromer), Casey Winters growth frameworks, and Elena Verna's retention-first approach. Use when adding viral mechanics, instrumenting analytics, creating referral systems, or optimizing for retention over acquisition.
.claude/skills/bilal140202-growth-embedded/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-10 | ✗→✓ | ▲ Improved | 81% | 0% |
| case-01 | ✓→✓ | = Same ✓ | 17% | 0% |
| case-02 | ✓→✓ | = Same ✓ | 62% | 0% |
| case-03 | ✓→✓ | = Same ✓ | 82% | 0% |
| case-04 | ✓→✓ | = Same ✓ | 99% | 0% |
Claude uses this skill when:
The Three Growth Levers:
📈 Acquisition - How users discover you 🎯 Activation - First value experience 🔁 Retention - Users coming back
Priority Order: > "Retention first, activation second, acquisition last. Don't acquire users you can't retain."
Growth Loops:
User gets value → Shares with others → New users → Cycle repeatsExamples:
Types:
Direct Network Effects:
Data Network Effects:
Platform Network Effects:
Double-Sided Incentive:
Referrer gets: [benefit]
Referred gets: [benefit]
Both win: [shared value]Tracking:
javascript// Referral flow generateReferralLink(user) { const link = `app.com/ref/${user.id}`; track('referral_link_generated', { userId: user.id }); return link; } // Attribution onSignup(referralCode) { const referrer = getUserByRefCode(referralCode); track('referral_signup', { referrer: referrer.id, referred: newUser.id }); giveReward(referrer); giveReward(newUser); }
Time to Value: > "Get users to 'aha moment' as fast as possible"
Activation Milestones:
Measure:
Activation Rate = (Users who reached value) / (Total signups)NEW FEATURE
│
├─ Can users share this? ──────YES──→ ADD SHARE FUNCTIONALITY
│ NO ↓
│
├─ Creates network effects? ───YES──→ OPTIMIZE FOR VIRAL LOOPS
│ NO ↓
│
├─ First-time experience? ─────YES──→ OPTIMIZE ACTIVATION
│ NO ↓
│
├─ Repeat usage? ──────────────YES──→ INSTRUMENT RETENTION TRACKING
│ NO ↓
│
└─ STANDARD FEATURE ←──────────────────┘
(Still track basic analytics)markdown# Referral Feature ## Incentive Structure - **Referrer gets:** [reward] - **Referred gets:** [reward] - **Both benefit:** [shared value] ## Implementation
// Generate referral link const link = generateReferralLink(user);
// Track shares track('referral_shared', { channel: 'email', 'social', 'link'], userId: user.id });
// Track conversions track('referral_converted', { referrerId: referrer.id, referredId: newUser.id, timeToConversion: timestamp });
// Reward both giveReward(referrer, 'storage_upgrade'); giveReward(newUser, 'welcome_bonus');
## Metrics
- Link generation rate
- Share rate
- Conversion rate (clicked → signed up)
- Activation rate (signed up → activated)
- K-factor (viral coefficient)markdown# Activation Optimization ## Time to Value - Target: <[X] minutes - Current: [Y] minutes ## Activation Funnel 1. **Signup** (100% of signups) 2. **Profile Setup** ([X]%) 3. **First Action** ([X]%) 4. **Aha Moment** ([X]%) 5. **Habit Formation** ([X]%) ## Drop-off Points - Biggest drop-off: [identify] - Why: [hypothesis] - Fix: [solution] ## Implementation
// Track activation milestones track('activation_milestone', { milestone: 'profile_complete', userId: user.id, timeToComplete: seconds });
// Identify where users drop off if (!completedSetup(user, 24hours)) { sendReminderEmail(user); }
markdown# Growth Metrics Dashboard ## Acquisition - New signups: [X per day] - Channels: [organic, referral, paid] - Cost per acquisition: [$X] ## Activation - Activation rate: [X]% - Time to first value: [X] minutes - Drop-off point: [step in funnel] ## Retention - Day 1: [X]% - Day 7: [X]% - Day 30: [X]% - Retention curve: [improving / flat / declining] ## Referral - K-factor: [X] (viral coefficient) - Referral rate: [X]% of users refer - Conversion rate: [X]% of referred sign up ## Implementation
// Track key events track('user_activated', { userId, timestamp }); track('user_retained_day7', { userId, timestamp }); track('referral_sent', { referrerId, channel });
Acquisition:
Activation:
Retention:
Analytics:
Incentive:
Result: 35% of signups from referrals
Time to Value:
Result: 90%+ retention
Growth Loop:
Result: Viral growth in teams
Problem: Leaky bucket - users leave as fast as they join Fix: Fix retention first, then acquire
Problem: Every user requires paid acquisition Fix: Build sharing into core features
Problem: Users drop off before seeing value Fix: Get to aha moment in < 5 minutes
Gustaf Alstromer: > "Retention first, activation second, acquisition last. Don't pour water into a leaky bucket."
Casey Winters: > "The best growth loops are built into the product, not bolted on."
Elena Verna: > "Acquisition is a tax on poor retention."
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 19,283 | 12,922 | -33% | 1 | 1 | 0% | 3,701 | 4,339 | +17% | 0 | 0 | — |
case-02 | pass→pass | 14,178 | 9,807 | -31% | 1 | 1 | 0% | 2,249 | 3,647 | +62% | 0 | 0 | — |
case-03 | pass→pass | 13,925 | 13,828 | -1% | 1 | 1 | 0% | 2,395 | 4,361 | +82% | 0 | 0 | — |
case-04 | pass→pass | 11,045 | 9,948 | -10% | 1 | 1 | 0% | 1,856 | 3,702 | +99% | 0 | 0 | — |
case-05 | pass→pass | 8,377 | 6,780 | -19% | 1 | 1 | 0% | 1,652 | 3,257 | +97% | 0 | 0 | — |
case-06 | pass→pass | 15,525 | 12,526 | -19% | 1 | 1 | 0% | 2,566 | 4,100 | +60% | 0 | 0 | — |
case-07 | pass→pass | 14,431 | 14,195 | -2% | 1 | 1 | 0% | 2,477 | 4,507 | +82% | 0 | 0 | — |
case-08 | pass→pass | 13,439 | 11,467 | -15% | 1 | 1 | 0% | 2,375 | 4,115 | +73% | 0 | 0 | — |
case-09 | pass→pass | 15,893 | 13,784 | -13% | 1 | 1 | 0% | 2,834 | 4,628 | +63% | 0 | 0 | — |
case-10 | fail→pass | 13,986 | 13,947 | -0% | 1 | 1 | 0% | 2,493 | 4,506 | +81% | 0 | 0 | — |
case-11 | pass→pass | 13,628 | 13,569 | -0% | 1 | 1 | 0% | 2,247 | 4,404 | +96% | 0 | 0 | — |
case-12 | pass→pass | 8,542 | 8,678 | +2% | 1 | 1 | 0% | 1,536 | 3,475 | +126% | 0 | 0 | — |
case-13 | pass→pass | 10,631 | 12,873 | +21% | 1 | 1 | 0% | 2,072 | 4,423 | +113% | 0 | 0 | — |
case-14 | pass→pass | 10,314 | 7,752 | -25% | 1 | 1 | 0% | 1,858 | 3,293 | +77% | 0 | 0 | — |
case-15 | pass→pass | 4,516 | 3,458 | -23% | 1 | 1 | 0% | 820 | 2,634 | +221% | 0 | 0 | — |
case-16 | pass→pass | 6,259 | 5,747 | -8% | 1 | 1 | 0% | 1,211 | 2,918 | +141% | 0 | 0 | — |
case-17 | pass→pass | 14,154 | 9,096 | -36% | 1 | 1 | 0% | 2,559 | 3,856 | +51% | 0 | 0 | — |
case-18 | pass→pass | 14,425 | 15,101 | +5% | 1 | 1 | 0% | 2,766 | 4,425 | +60% | 0 | 0 | — |
case-19 | pass→pass | 17,201 | 13,695 | -20% | 1 | 1 | 0% | 3,226 | 4,540 | +41% | 0 | 0 | — |
case-20 | pass→pass | 3,719 | 3,355 | -10% | 1 | 1 | 0% | 746 | 2,539 | +240% | 0 | 0 | — |
case-21 | pass→pass | 8,680 | 5,868 | -32% | 1 | 1 | 0% | 1,972 | 3,414 | +73% | 0 | 0 | — |
case-22 | pass→pass | 9,294 | 7,328 | -21% | 1 | 1 | 0% | 1,824 | 3,510 | +92% | 0 | 0 | — |
case-23 | pass→pass | 7,702 | 6,434 | -16% | 1 | 1 | 0% | 1,795 | 3,451 | +92% | 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. 23 cases were attempted. The headline lift of +4 percentage points is the difference between those two pass rates over the 23 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.