Product OS

Your AI product team — I teach you product thinking while we improve your metrics together.

Product OS helps you understand why users leave, what to build next, and whether you've found product-market fit. But more importantly — it teaches you the concepts so you can think like a product leader.

Price: $99 (one-time)

Quick Start

npx @aiorg/cli@latest init product-os ~/my-project
cd ~/my-project
claude
> /setup

Requirements

• Claude Code — Get it here
• Node.js 18+ (for CLI)

What Makes Product OS Different

The AARRR Framework (Pirate Metrics)

This is your product growth compass. Every successful product optimizes these 5 metrics:

┌─────────────────────────────────────────────────────────────────┐
│                    PIRATE METRICS (AARRR)                       │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  ACQUISITION     ACTIVATION      RETENTION      REVENUE    REFERRAL
│  ───────────     ──────────      ─────────      ───────    ────────
│  How do users    Do they have    Do they        Do they    Do they
│  find you?       a great first   come back?     pay?       tell
│                  experience?                               others?
│                                                                 │
│  Marketing OS    ← PRODUCT OS FOCUS →           Product OS Marketing
│  territory       Activation + Retention          + Stripe   + Success
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

Product OS focuses on Activation + Retention — the two metrics that determine if your product is actually valuable.

Why Activation + Retention First?

You can buy Acquisition (ads). Revenue comes after value. Referral requires happy users.

But if users don't activate and retain, nothing else matters. You're filling a leaky bucket.

When to Use Product OS

Use Product OS if:

• Users sign up but don't come back
• You don't know why users leave
• You're not sure if you have product-market fit
• You want to learn product thinking

Don't use Product OS if:

• You haven't launched yet → Use Idea OS
• Users activate but churn months later → Use Support Team
• You need more traffic → Use Marketing OS

What I Teach You

1. Activation ("Aha Moment")

What it is: The moment a user first experiences your core value.

NOT: User signed up YES: User completed the action that delivers value

Examples:

• Slack: Sent first message in a channel
• Dropbox: Uploaded first file
• Spotify: Played first song

I'll help you define YOUR activation metric.

2. Retention (Coming Back)

What it is: Users returning to get value again.

NOT: User didn't delete account YES: User actively used the product in [timeframe]

Industry benchmarks (Week 1 retention):

• Excellent: > 60%
• Good: 40-60%
• Needs work: 20-40%
• Critical: < 20%

3. The Magic Number

Every successful product has a "magic number" — an action that predicts retention.

Famous examples:

• Facebook: 7 friends in 10 days → 80% chance of retention
• Slack: 2,000 messages sent → team is hooked
• Dropbox: 1 file in 1 folder on 2 devices → user understands value

I'll help you find YOUR magic number through data analysis.

4. Product-Market Fit (PMF)

The Sean Ellis Test:

"How would you feel if you could no longer use [product]?"

• Very disappointed
• Somewhat disappointed
• Not disappointed

40%+ "Very disappointed" = You have PMF

Adaptive Education System

Product OS adapts to your experience level. On first session, you'll be asked how familiar you are with startup metrics.

Example: Same Metric, Different Explanations

Beginner sees:

ACTIVATION RATE: 23%

What this means: Out of every 100 users who sign up, only 23
experience your product's core value. Think of it like a coffee
shop — people walk in, but only 23% actually order coffee.

Why it matters: Users who don't activate will NEVER come back.
This is your foundation.

Benchmark: 30-40% for B2B SaaS
Status: Below average — this is your #1 priority

Expert sees:

ACTIVATION: 23% (benchmark: 30-40%)

Change your level anytime with /settings level [beginner|intermediate|expert].

What's Included

Setup & Analytics

AARRR Dashboard

Diagnosis & Discovery

User Research

Product Intelligence

PMF & Planning

Session Start Behavior

When you open Product OS, it gives you a personalized briefing:

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
YOUR PRODUCT HEALTH
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

ACQUISITION  ████████░░  312 signups/month   (Marketing OS)
ACTIVATION   ███░░░░░░░  23%                 ⚠️ FOCUS HERE
RETENTION    █████░░░░░  45% week-1          Needs work
REVENUE      ██████░░░░  $2,400 MRR          Healthy
REFERRAL     ██░░░░░░░░  0.3 viral coef.     Low

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

📊 What this means:

Your ACTIVATION (23%) is your biggest problem right now.
Industry average is 30-40%. This means 77% of people who
sign up never experience your product's value.

Good news: Retention (45%) is decent for users who DO activate.
Fix activation, and your overall numbers improve automatically.

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

🎯 Recommended focus: Activation

Options:
1. /activation — Deep dive into where users drop off
2. /diagnose — Full root cause analysis
3. /magic — Find what activated users did differently

What would you like to explore?

Stage-Aware Guidance

< 100 Users (Discovery)

At your stage, forget about metrics.

Focus on:
├── Talk to EVERY user who signs up
├── Talk to EVERY user who leaves
├── Understand the "why" deeply
└── Find your first 10 users who LOVE it

Your goal: Find product-market fit signal (not scale)

Recommended: /interview to set up user conversations

100-1000 Users (Validation)

Now patterns emerge. You can start measuring.

Focus on:
├── Define your activation metric
├── Find your magic number
├── Run Sean Ellis test
└── Start small experiments

Your goal: Validate that your product delivers value

Recommended: /magic to find what predicts retention

> 1000 Users (Optimization)

You have enough data for statistical significance.

Focus on:
├── A/B test improvements
├── Cohort analysis trends
├── Systematic experimentation
└── PMF score tracking

Your goal: Optimize the proven path to value

Recommended: /experiment to design your first A/B test

Data Structure

Product OS stores data in .product-os/ in your project:

.product-os/
├── config.json           # Settings and metrics definitions
├── data/
│   ├── users.db          # User data (SQLite)
│   └── events.db         # Event tracking
├── analysis/
│   ├── funnels/          # Funnel analysis results
│   ├── cohorts/          # Cohort analysis results
│   └── experiments/      # A/B test results
├── research/
│   ├── interviews/       # Interview notes
│   └── surveys/          # Survey results
├── ideas/
│   ├── backlog/          # Feature ideas
│   └── validated/        # Validated ideas
└── reports/              # Generated reports

Shared Context

Product OS reads and writes to the shared context layer (~/.aiorg/projects/):

What it reads:

• Idea OS validation score
• Target customer from Idea OS
• Business stage

What it writes:

• PMF score (0-100)
• PMF status (searching | approaching | achieved)
• Sean Ellis score
• Activation rate
• Weekly retention

Other kits (Marketing OS, Support Team) read this to understand your product health and adapt their recommendations.

Related Kits

Before Product OS:

• Idea OS — Validate your idea before building
• SaaS Dev Team — Build your product

After PMF:

• Marketing OS — Scale user acquisition
• Support Team — Retain activated users long-term

FAQ

Q: What's the difference between Product OS and Support Team?

Product OS: Users leave before activating (product/onboarding problem) Support Team: Users leave after activating (retention/engagement problem)

If users never experience your value, that's Product OS. If they experience it but still leave, that's Support Team.

Q: How do I know if I have PMF?

Run /pmf for a full assessment. Key indicators:

• 40%+ "very disappointed" on Sean Ellis survey
• Organic growth (users referring others)
• High activation rate (>50%)

Q: What if I don't have user data?

Product OS works with qualitative data too. Run /interview to structure user conversations and analyze patterns manually.

Q: I'm not a product person — will I understand this?

That's exactly why we built the adaptive education system. Set your level to "beginner" and every concept gets explained with real-world analogies. You'll learn product thinking while improving your metrics.