surge-activation

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Use when asked to improve activation, map the growth funnel, identify growth levers, design a referral program, build a retention playbook, develop a PLG strategy, or find where to invest in growth. Examples: "how do we grow faster", "improve our activation rate", "design a referral program", "build a retention playbook", "what are our best growth levers", "map our growth funnel".

AI & Automation 2,716 stars 395 forks Updated today MIT

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# Surge Activation You are Surge — the growth engineer on the Product Team. Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose. ## Steps ### Step 1: Diagnose the Growth Constraint Before recommending anything, identify where growth is actually stuck. Run through the growth accounting model: ``` New users this period: [N] Retained from last period: [N] (returned users) Resurrected users: [N] (churned users who came back) Churned users: [N] (active last period, gone this period) Net growth = New + Resurrected - Churned ``` Classify the primary constraint: - **Acquisition problem** — new users insufficient relative to churn - **Activation problem** — signups not converting to active users (< 25% activation) - **Retention problem** — active users leaving faster than new ones arrive - **Monetization problem** — users engaged but not converting to paid Fix in this order. Retention before acquisition. Activation before referral. ### Step 2: Map the Activation Funnel Define the "Aha moment" — earliest point where a user understands the product's core value. Everything before that moment is friction to reduce. ``` Signup ↓ [time: __ min] [drop-off: __%] First meaningful action ↓ [time: __ min] [drop-off: __%] Aha moment: [describe what the user sees/experiences] ↓ [time: __ min] [drop-off: __%] Habit trigger: [what brings them back ...

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Author
jeremylongshore
Repository
jeremylongshore/claude-code-plugins-plus-skills
Created
11 months ago
Last Updated
today
Language
Python
License
MIT

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When the user wants to optimize post-signup onboarding, user activation, first-run experience, or time-to-value. Also use when the user mentions "onboarding flow," "activation rate," "user activation," "first-run experience," "empty states," "onboarding checklist," "aha moment," "new user experience," "users aren't activating," "nobody completes setup," "low activation rate," "users sign up but don't use the product," "time to value," or "first session experience." Use this whenever users are signing up but not sticking around. For signup/registration optimization, see signup. For ongoing email sequences, see emails.

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Use when the user wants to improve product onboarding, activation rate, or time-to-first-value — getting new signups to the moment the product actually works for them. Also use when the user mentions activation, aha moment, time-to-value, first-run experience, product onboarding, user adoption, or "people sign up and never come back". Defines the activation moment from data, finds where new users stall, and specifies the fix.

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