growth

name	growth
description	Growth engine for ID8Labs. Systematic experimentation and optimization to scale products through data-driven decisions, retention focus, and sustainable acquisition channels.
version	1.0.0
mcps	["Supabase","Perplexity"]
subagents	[]
skills	["analytics-tracking"]

ID8GROWTH - Growth Engine

Purpose

Scale your launched product through systematic experimentation. Growth is not magic—it's methodology.

Philosophy: Retention beats acquisition. One channel mastered beats five attempted. Data over intuition.

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When to Use

• Product is launched and has initial users
• User needs to grow user base
• User asks "how do I get more users?"
• User wants to improve retention
• User needs help with analytics
• User wants to optimize conversion
• Project is in LAUNCHING or GROWING state

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Commands

/growth <project-slug>

Run full growth analysis and planning.

Process:

1. BASELINE - Understand current metrics
2. MODEL - Map growth mechanics
3. DIAGNOSE - Find bottlenecks
4. HYPOTHESIZE - Generate experiments
5. PRIORITIZE - ICE scoring
6. EXECUTE - Run experiments
7. LEARN - Analyze and iterate

/growth metrics

Audit current analytics and define key metrics.

/growth funnel

Analyze conversion funnel and identify drop-offs.

/growth experiment <hypothesis>

Design a specific growth experiment.

/growth retention

Deep dive on retention and engagement.

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Growth Philosophy

Solo Builder Reality

What Works	What Doesn't
Focused effort on one channel	Spray-and-pray multi-channel
Retention optimization	Endless acquisition
Organic/content marketing	Expensive paid acquisition
Personal touch	Automated spam
Slow compounding	Viral hacks

Growth Priorities

Stage 1: Pre-PMF (< 100 users)

• Focus: Finding users who love it
• Metric: Qualitative feedback, NPS
• Don't worry about: Scale

Stage 2: Early Traction (100-1000 users)

• Focus: Retention and activation
• Metric: Day 1/7/30 retention
• Don't worry about: Growth rate

Stage 3: Growth (1000+ users)

• Focus: Scalable acquisition
• Metric: CAC, LTV, growth rate
• Now optimize: Everything

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Process Detail

Phase 1: BASELINE

Establish current state:

Metric	Value	Source
Total users	{N}	Database
Active users (DAU/WAU/MAU)	{N}	Analytics
Activation rate	{%}	Funnel
Retention (D1/D7/D30)	{%}	Cohort
Conversion (free→paid)	{%}	Funnel
Revenue (MRR/ARR)	${X}	Payments
NPS	{score}	Survey

If no tracking:

• Set up analytics first
• Use analytics-tracking skill
• Minimum: Sign-ups, activation, retention

Phase 2: MODEL

Map your growth mechanics:

ACQUISITION
How do users find you?
├── Organic search
├── Social/content
├── Referrals
├── Paid (if any)
└── Direct

ACTIVATION
What's the "aha moment"?
├── First action completed
├── Value received
└── Setup finished

RETENTION
Why do they come back?
├── Core value loop
├── Notifications
├── Habit formation
└── New content/features

REVENUE
How do you monetize?
├── Subscription
├── Usage-based
├── One-time
└── Freemium conversion

REFERRAL
How do they spread it?
├── Word of mouth
├── Built-in sharing
├── Incentivized referral
└── Social proof

Phase 3: DIAGNOSE

Find the bottleneck:

Stage	Benchmark	Your Rate	Status
Visitor → Sign-up	2-5%	{%}	{OK/LOW}
Sign-up → Activated	20-40%	{%}	{OK/LOW}
Activated → Day 7	20-30%	{%}	{OK/LOW}
Day 7 → Day 30	50-70%	{%}	{OK/LOW}
Free → Paid	2-5%	{%}	{OK/LOW}

Diagnosis framework:

1. Compare to benchmarks
2. Identify biggest drop-off
3. That's your focus

Phase 4: HYPOTHESIZE

Generate experiment ideas:

For each bottleneck, generate 3-5 hypotheses:

If we [change]
Then [metric] will [improve/increase/decrease]
Because [reasoning]

Example:

If we add an onboarding checklist
Then activation rate will increase by 20%
Because users will know what to do next

Phase 5: PRIORITIZE

ICE Scoring:

Experiment	Impact	Confidence	Ease	Score
{exp 1}	{1-10}	{1-10}	{1-10}	{avg}
{exp 2}	{1-10}	{1-10}	{1-10}	{avg}

Definitions:

• Impact: How much will this move the metric?
• Confidence: How sure are we it will work?
• Ease: How easy is it to implement?

Rule: Do highest ICE score first.

Phase 6: EXECUTE

For each experiment:

1. Define hypothesis clearly
2. Define success metric
3. Define sample size needed
4. Implement change
5. Run for sufficient time
6. Analyze results
7. Document learnings

Minimum experiment duration:

• High traffic: 1-2 weeks
• Low traffic: 2-4 weeks
• Statistical significance matters

Phase 7: LEARN

After each experiment:

Question	Answer
Did it work?	{Yes/No/Inconclusive}
What was the lift?	{X}%
Why did it work/fail?	{reasoning}
What did we learn?	{insight}
What's next?	{next experiment}

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Framework References

Growth Loops

frameworks/growth-loops.md - Viral, content, flywheel mechanics

Analytics

frameworks/analytics.md - Metrics, tracking, dashboards

Acquisition

frameworks/acquisition.md - Channels, CAC, scale

Retention

frameworks/retention.md - Engagement, churn, habit

Optimization

frameworks/optimization.md - A/B testing, CRO

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Output Templates

Growth Model

templates/growth-model.md - Growth strategy document

Metrics Dashboard

templates/metrics-dashboard.md - KPI tracking structure

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Tool Integration

MCPs

Supabase:

• Query user data for analysis
• Cohort analysis
• Funnel tracking

Perplexity:

• Research growth tactics
• Find benchmarks
• Competitor analysis

Skills

analytics-tracking:

• Set up tracking
• Define events
• Create dashboards

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Handoff

After completing growth analysis:

1. Save outputs:

   • Growth model → docs/GROWTH_MODEL.md
   • Metrics → docs/METRICS.md

2. Log to tracker:

   /tracker log {project-slug} "GROWTH: Analysis complete. Focus: {bottleneck}. Top experiment: {experiment}."
   

3. Update state:

   /tracker update {project-slug} GROWING
   

4. Next steps:

   • Execute top-priority experiments
   • Review results weekly
   • When stable, transition to ops

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Key Metrics Cheat Sheet

AARRR Funnel

Stage	What to Track
Acquisition	Traffic, channels, CAC
Activation	Sign-up rate, onboarding completion
Retention	DAU/MAU, D1/D7/D30, churn
Revenue	MRR, ARPU, LTV
Referral	K-factor, invite rate

Benchmarks

Metric	Poor	OK	Good	Great
D1 retention	<10%	10-20%	20-30%	>30%
D7 retention	<5%	5-10%	10-20%	>20%
D30 retention	<2%	2-5%	5-10%	>10%
Free→Paid	<1%	1-2%	2-5%	>5%
NPS	<0	0-30	30-50	>50

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Anti-Patterns

Anti-Pattern	Why Bad	Do Instead
Vanity metrics	Don't drive business	Focus on actionable metrics
Too many experiments	No learnings	One experiment at a time
No hypothesis	Can't learn	Always have clear hypothesis
Short experiments	Inconclusive	Run to significance
Ignoring retention	Leaky bucket	Fix retention first
Copying others	Context matters	Adapt to your situation

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Quality Checks

Before finalizing growth plan:

• Baseline metrics established
• Biggest bottleneck identified
• Hypotheses are testable
• Experiments are prioritized
• Success metrics defined
• Realistic timeline set
• Learning process planned