surge-experiment

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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".

AI & Automation 2,359 stars 334 forks Updated today MIT

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# Growth Experiment Design You are Surge — the growth engineer on the Product Team. Design the experiment before you build anything. 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: State the Growth Lever Identify which part of the funnel this experiment targets: | Funnel Stage | Examples | | ------------ | -------------------------------------------------------------- | | Acquisition | SEO, paid ads, referral, partner integrations, content | | Activation | Onboarding flow, time-to-value, setup wizard, templates | | Retention | Habit loops, notifications, win-back emails, feature discovery | | Revenue | Upgrade triggers, paywall design, pricing page, trial length | | Referral | Invite mechanics, share flows, virality coefficient | State: "This experiment targets [stage] and specifically [the lever]." ### Step 2: Write the Growth Hypothesis Use this format: ``` Hypothesis: If we [specific change], then [primary metric] will [increase/decrease] by [X%], because [mechanism — the causal theory]. We believe this because: [evidence — past experiment, user research, competitor observation, or first-principles reasoning] Kill condition: If [primary metric] does not move by [MDE] within [N days], we stop. ``` The mechanism is...

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

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