Which metrics does this skill help optimize?

It focuses on the full AARRR framework, also known as Pirate Metrics: Acquisition, Activation, Retention, Revenue, and Referral.

Is this skill suitable for low-traffic applications?

Yes, it includes advanced techniques like sequential testing and multi-armed bandits which are specifically designed to reach conclusions faster with less data.

Can it help with the actual implementation of A/B tests?

Yes, it provides code patterns for random assignment, success criteria measurement, and rollout logic for winners.

What is the Growth Experimenter skill?

It is a specialized capability for Claude Code designed to help developers and growth teams run systematic experiments to improve product metrics like acquisition and retention.

How does it handle statistical significance?

The skill includes built-in logic and calculators to determine required sample sizes and p-values to ensure your experiment results are statistically valid.

Growth Experimenter

Name: Growth Experimenter
Author: daffy0208

bydaffy0208

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マーケティングオートメーション

Runs systematic growth experiments and A/B tests to optimize acquisition, activation, retention, and revenue metrics.

概要

Growth Experimenter empowers developers and product teams to apply data-driven optimization strategies directly within their workflow using the AARRR (Pirate Metrics) framework. It provides structured guidance for identifying conversion bottlenecks, forming testable hypotheses, and designing statistically sound A/B tests. From streamlining onboarding flows to building viral referral loops, this skill offers the frameworks and implementation patterns needed to accelerate experimentation velocity and maximize product growth.

主な機能

AARRR (Pirate Metrics) framework integration
Statistical significance and sample size calculators
Hypothesis generation and experiment design templates
Conversion funnel optimization strategies
2 GitHub stars
Multi-armed bandit and cohort analysis techniques

ユースケース

Increasing user retention through automated re-engagement patterns
Optimizing sign-up conversion funnels and landing page headlines
Implementing and analyzing A/B tests for pricing and monetization

概要

主な機能

AARRR (Pirate Metrics) framework integration
Statistical significance and sample size calculators
Hypothesis generation and experiment design templates
Conversion funnel optimization strategies
2 GitHub stars
Multi-armed bandit and cohort analysis techniques

ユースケース

Increasing user retention through automated re-engagement patterns
Optimizing sign-up conversion funnels and landing page headlines
Implementing and analyzing A/B tests for pricing and monetization