metrics-analytics

Define product metrics, implement AARRR, and design A/B tests.

14|3|Updated Feb 22, 2026

Search Tags:#analytics #metrics #north star metric #a/b testing #kpi #product growth

Ready to run

npx skills add https://github.com/rnavarych/alpha-engineer --skill metrics-analytics

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: metrics-analytics
Source: https://github.com/rnavarych/alpha-engineer/tree/main/plugins/billy-milligan/skills/product/metrics-analytics
Command: npx skills add https://github.com/rnavarych/alpha-engineer --skill metrics-analytics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

This Skill helps you define clear product metrics, design statistically sound A/B tests, and avoid vanity metrics that don't drive real growth.

Core Features & Use Cases

KPI Definition: Establish a North Star Metric and supporting KPIs using frameworks like AARRR.
A/B Testing: Design experiments with proper sample size calculations, confidence levels, and minimum detectable effects.
Data Interpretation: Understand funnel analysis, cohort retention, and differentiate actionable metrics from vanity ones.
Use Case: You're launching a new feature and want to know if it improves user engagement. Use this Skill to define the key event, calculate the required sample size for an A/B test, and set up the experiment to run for a statistically significant duration.

Quick Start

Use the metrics-analytics skill to define the North Star Metric for a new SaaS product.

Frequently Asked Questions about metrics-analytics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I define a North Star Metric and supporting KPIs for my product?▼

To define a North Star Metric, identify the single metric that best captures your product's core value to customers. You can then establish supporting KPIs using the AARRR framework to map the entire user journey from acquisition to revenue.

How do I calculate sample size and confidence intervals for A/B testing?▼

Calculate A/B test sample size by factoring in your baseline conversion rate, minimum detectable effect, and desired confidence level. This ensures your experiment runs long enough to achieve statistically significant results and reliable confidence intervals.

What is the difference between actionable metrics and vanity metrics?▼

Actionable metrics provide clear guidance for product decisions and drive real growth, whereas vanity metrics look impressive but lack actionable insights. This Skill helps you distinguish between them to focus on data that truly improves user engagement.

Can I use PostHog for funnel analysis and event tracking?▼

Yes, this Skill supports PostHog for event tracking and funnel analysis. It provides guidance on setting up event tracking and analyzing cohort retention to understand how effectively users move through your product funnel.

When do I need to run an A/B test for a new feature launch?▼

You need an A/B test for a new feature launch when you want to verify if it statistically improves user engagement. Use this Skill to define the key event, calculate the required sample size, and set up a valid experiment.