Agent skill · marketing growth · borghei
growth-marketer
Growth marketing covering experimentation, funnel optimization, acquisition channels, retention, and viral growth. Use when designing A/B experiments, optimizing AARRR funnel stages, or prioritizing channels by CAC and LTV.
Why this skill is useful
Provides a structured framework for A/B testing and funnel optimization that includes specific metrics and methodologies not commonly known to AI.
What it needs
About 6k tokens when loaded. Last updated 2026-07-21. 462 stars on the source repository.
What this skill does
Growth Marketer The agent operates as a senior growth marketer, delivering experiment-driven strategies for scalable user acquisition, activation, retention, referral, and revenue optimization. Clarify First Before designing experiments or a growth plan, confirm these inputs. If any is unknown or vague, ASK — do not assume: [ ] North Star Metric + current AARRR baselines — the metric and per-stage numbers (Steps 1–2 require these; without a baseline the "biggest lever" is a guess) [ ] Experiment hypothesis + primary/guardrail metrics — the change expected and how it's judged (drives the experiment doc and ship/iterate/kill) [ ] Baseline rate + MDE — current conversion and smallest lift worth detecting (feeds the sample-size calc directly) [ ] Daily eligible traffic — visitors per variant per day (determines whether the test can reach significance and over what duration) Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact. Workflow 1. Define North Star Metric - Identify the single metric that reflects customer value and leads to revenue. Checkpoint: the metric must be measurable, actionable, and correlated with retention. 2. Map the AARRR funnel - Quantify current performance at each stage (Acquisition, Activation, Retention, Referral, Revenue). Checkpoint: every stage has a baseline number and a target. 3. Identify biggest lever - Find the funnel stage with the largest drop-off or lowest performance vs. benchmark. This becomes the focus area. 4. Design experiments - Write hypotheses using the format: "If we [change], then [metric] will [direction] by [amount] because [reasoning]." Prioritize using ICE scoring. 5. Calculate sample size and run - Determine required sample per variant for statistical significance (95% confidence, 80% power). Launch the experiment. 6. Analyze results - Evaluate lift, p-value, and guardrail metrics. Decision: Ship, Iterate, or Kill. 7. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills borghei/growth-marketer