surge-activation
FeaturedUse 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".
Install
Quality Score: 99/100
Skill Content
Details
- Author
- jeremylongshore
- Repository
- jeremylongshore/claude-code-plugins-plus-skills
- Created
- 11 months ago
- Last Updated
- today
- Language
- Python
- License
- MIT
Integrates with
Bundled in these plugins
Similar Skills
Semantically similar based on skill content — not just same category
surge-experiment
Growth experiment design — structure a growth hypothesis, define metric, baseline, expected lift, and kill condition for a single experiment. Use when asked to "design a growth experiment", "test this growth idea", "experiment framework", "how do we test if this works", or "growth hypothesis".
onboarding
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.
onboarding-activation
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.