Agent skill · marketing growth · daffy0208
growth-experimenter
Run systematic growth experiments to increase acquisition, activation, retention, and revenue. Use when optimizing conversion funnels, running A/B tests, improving metrics, or when users mention growth, experimentation, optimization, or scaling user acquisition.
Why this skill is useful
Provides a structured framework for running growth experiments, including specific metrics and statistical significance calculations that the AI wouldn't reliably generate on its own.
What it needs
About 9k tokens when loaded. Last updated 2025-12-25. 33 stars on the source repository.
What this skill does
Growth Experimenter Run systematic experiments to grow faster through data-driven optimization. Core Philosophy Growth = Experimentation Velocity × Win Rate × Impact per Win Run more experiments Increase your hit rate through better hypotheses Focus on high-impact areas Growth Model (AARRR / Pirate Metrics) Where to focus first: The leakiest bucket If 40% sign up but only 10% are active at D30 → Fix retention If 80% are active but only 5% pay → Fix monetization If 2% visitors sign up but 60% convert to paid → Get more traffic Experiment Framework 1. Identify the Problem Good problem statements: "Only 2% of homepage visitors sign up" (specific metric) "50% of trials don't complete onboarding" (clear drop-off) "Users who invite teammates have 3x retention, but only 10% invite" (known behavior) Bad problem statements: "We need more growth" (too vague) "Conversion is bad" (no baseline) "Users don't understand the product" (not measurable) 2. Form a Hypothesis Hypothesis template: Examples: 3. Design the Experiment Experiment specification: 4. Run the Experiment A/B testing checklist: [ ] Random assignment (50/50 split) [ ] Same time period (no day-of-week effects) [ ] Sufficient sample size [ ] No peeking (wait for significance) [ ] One change at a time Statistical significance calculator: 5. Analyze Results Interpreting results: When results are inconclusive: No movement: Hypothesis was wrong or change too small Not significant: Need more data or larger effect Negative impact: Roll back immediately Contradictory secondary metrics: Investigate trade-offs 6. Scale What Works Growth Experiments by Stage Acquisition Experiments Goal: Get more traffic or improve traffic quality High-impact experiments: 1. Landing page optimization: 2. Headline testing: 3. Social proof: Activation Experiments Goal: Get users to "aha moment" faster High-impact experiments: 1. Onboarding simplification: 2. Time-to-value reduction: 3. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills daffy0208/growth-experimenter