experiment-tracker

Design, track, and analyze A/B tests with statistical rigor.

2|Updated Feb 25, 2026

Search Tags:#product management #data analysis #experimentation #a/b testing #hypothesis validation #statistical rigor

Ready to run

npx skills add https://github.com/elihuvillaraus/skills --skill experiment-tracker-elihuvillaraus

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: experiment-tracker
Source: https://github.com/elihuvillaraus/skills/tree/main/experiment-tracker
Command: npx skills add https://github.com/elihuvillaraus/skills --skill experiment-tracker-elihuvillaraus

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill streamlines the entire process of designing, executing, and analyzing experiments, ensuring data-driven decisions and rigorous validation for product development.

Core Features & Use Cases

Experiment Design: Creates statistically valid A/B tests with clear hypotheses and success criteria.
Execution Tracking: Manages experiment lifecycles, monitors data quality, and ensures safe rollouts.
Data-Driven Insights: Provides rigorous statistical analysis and actionable recommendations.
Use Case: Launch a new feature by having the Experiment Tracker design an A/B test, monitor its performance, and provide a go/no-go recommendation based on statistical significance.

Quick Start

Use the experiment-tracker skill to design an A/B test for a new checkout flow with a hypothesis that it will increase conversion by 5%.

Frequently Asked Questions about experiment-tracker

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

FAQPage Schema

How do I design an A/B test with clear hypotheses and statistical rigor?▼

You design an A/B test by defining a clear hypothesis, setting success criteria, and applying statistical rigor to ensure data-driven validation for product development.

What is the best way to track experiment execution and monitor data quality?▼

The best way to track experiment execution is to manage the experiment lifecycle systematically, actively monitoring data quality and ensuring safe feature rollouts throughout the process.

How do I get a go/no-go recommendation for a feature rollout based on statistical significance?▼

You get a go/no-go recommendation by applying rigorous statistical analysis to the experiment data, translating results into actionable, data-driven insights for your feature rollout.

Can I use this approach to manage an entire portfolio of product experiments?▼

Yes, you can manage a portfolio of product experiments through systematic experiment portfolio management, which captures learning and tracks statistical analysis across multiple feature validations.

Does experiment tracking work for validating new features without historical data?▼

Experiment tracking works for new features by focusing on hypothesis validation and statistical rigor, establishing success criteria and data-driven decision-making processes even for new feature rollouts.