# Growth Engineering > Use when designing growth infrastructure including onboarding funnels, referral mechanics, A/B test instrumentation, and re-engagement loops. Covers activation metrics, funnel mapping, and experimentation design. Do not use for pricing or paywall architecture (use monetization-design) or product copy and naming (use messaging-strategy). - Skill: `tomevault-io/growth-engineering` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/growth-engineering` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/growth-engineering/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/growth-engineering --- # Growth Engineering ## Purpose Design the growth engineering infrastructure for a product feature, including onboarding funnel optimization, referral system mechanics, and A/B test instrumentation. ## Scope Constraints Analyzes product flows, conversion metrics, and experimentation architecture. Does not implement code, modify production systems, or access user data directly. ## Inputs - Product feature being designed - Current onboarding flow (if exists) - Target activation metric ("aha moment") - User acquisition channels - Existing analytics infrastructure ## Input Sanitization No user-provided values are used in commands or file paths. All inputs are treated as read-only analysis targets. ## Procedure ### Progress Checklist - [ ] Step 1: Activation metric defined - [ ] Step 2: Onboarding funnel mapped - [ ] Step 3: Onboarding flow designed - [ ] Step 4: Referral mechanics designed - [ ] Step 5: A/B test infrastructure instrumented - [ ] Step 6: Re-engagement loops designed ### Step 1: Define the Activation Metric Identify the "aha moment" — the action that correlates with long-term retention: - What specific action indicates the user has gotten value? - How quickly should a new user reach this action? (target: under 60 seconds for simple products, under 5 minutes for complex ones) - What's the current activation rate? What's the target? ### Step 2: Map the Onboarding Funnel Trace the path from first visit to activation: - **Entry point -> Sign up -> First action -> Aha moment -> Habit formation** - For each step, measure: conversion rate, drop-off reason, time spent - Identify the highest-drop-off step (this is your bottleneck) - Design interventions for the bottleneck step ### Step 3: Design Onboarding Flow For the onboarding experience: - **Progressive profiling:** Collect only what's needed now, ask for more later - **Value before effort:** Show the user what they'll get before asking them to work - **Checklist pattern:** Visual progress indicator for multi-step onboarding - **Skip option:** Never trap users in onboarding — always allow skipping - **Contextual education:** Teach features at the moment of need, not upfront ### Step 4: Design Referral Mechanics If referral/viral growth is relevant: - **Incentive structure:** What does the referrer get? What does the invitee get? - **Share surface:** Where in the product does sharing feel natural (not forced)? - **Link mechanics:** Deep link to personalized onboarding, attribution tracking - **K-factor modeling:** Users x invites-per-user x conversion-rate = viral coefficient ### Step 5: Instrument A/B Test Infrastructure Design the experimentation layer: - **Feature flag system:** How are experiments gated (LaunchDarkly, Statsig, custom)? - **Assignment:** How are users bucketed (user ID hash, session-based, geo-based)? - **Event tracking:** What events must fire for each experiment variant? - **Statistical rigor:** Sample size calculation, significance threshold, duration estimate ### Step 6: Design Re-engagement Loops For users who don't activate or who churn: - **Trigger events:** What signals indicate a user is at risk? - **Re-engagement channels:** Email, push notification, in-app message - **Timing:** How soon after drop-off, and how many touchpoints? - **Content:** What value reminder or incentive brings them back? > **Compaction resilience**: If context was lost during a long session, re-read the Inputs section to reconstruct what product is being analyzed, check the Progress Checklist for completed steps, then resume from the earliest incomplete step. ## Output Format ```markdown # Growth Engineering Plan ## Activation Metric **"Aha moment":** [Specific action] **Target time-to-activation:** [X minutes] **Current rate:** [X%] → **Target rate:** [Y%] ## Onboarding Funnel | Step | Action | Current Conversion | Target | Intervention | |------|--------|-------------------|--------|-------------| | 1 | Landing page visit | — | — | — | | 2 | Sign up | 12% | 18% | Simplify form | | 3 | First [action] | 65% | 80% | Guided walkthrough | | 4 | Aha moment | 40% | 60% | Reduce steps to value | ## Referral System **Incentive:** [Referrer gets X, invitee gets Y] **Share surfaces:** [Where in the product] **Target K-factor:** [X.XX] **Attribution:** [Link structure and tracking] ## A/B Test Plan | Experiment | Hypothesis | Metric | Variants | Sample Size | Duration | |-----------|-----------|--------|----------|-------------|----------| | Onboarding V2 | Reducing steps increases activation by 20% | Activation rate | 2 | 5,000 | 2 weeks | ## Re-engagement | Trigger | Channel | Timing | Content | |---------|---------|--------|---------| | No login 3 days | Email | Day 3 | Value reminder | | Incomplete onboarding | Push | Day 1 | Resume prompt | ``` ## Handoff - Hand off to monetization-design if paywall placement or pricing tier decisions surface during growth analysis. - Hand off to strategist/analytics-design if analytics instrumentation gaps are identified during funnel mapping. ## Quality Checks - [ ] Activation metric is specific, measurable, and correlated with retention - [ ] Onboarding funnel has conversion rates (actual or estimated) for each step - [ ] Referral incentives are balanced (not so generous they attract fraud, not so stingy they don't motivate) - [ ] A/B tests have statistical rigor (sample size, significance threshold, duration) - [ ] Re-engagement has defined triggers, timing, and content — not just "send emails" - [ ] The skip option is available at every onboarding step ## Evolution Notes --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/dtsong) — claim your Tome and manage your conversions.