growth-engine

Name: growth-engine
Availability: InStock
Author: LazyIsEfficient

Automates growth experimentation with A/B and multivariate tests, scoring experiments and generating weekly scorecards.

15|3|Updated Apr 9, 2026

Search Tags:#marketing #playbook #statistics #growth #experimentation #ab-testing

Ready to run

npx skills add https://github.com/LazyIsEfficient/agentic-os --skill growth-engine-lazyisefficient

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: growth-engine
Source: https://github.com/LazyIsEfficient/agentic-os/tree/main/.claude/skills/growth-engine
Command: npx skills add https://github.com/LazyIsEfficient/agentic-os --skill growth-engine-lazyisefficient

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Automates growth experimentation for marketing teams. It orchestrates hypothesis-driven tests, collects data, and evaluates results to enable evidence-based decision making.

Core Features & Use Cases

Hypothesis-driven experiments: Define hypotheses, variables, and multi-variant tests to learn what works.
Statistical analysis & living playbook: Compute bootstrap CI and Mann-Whitney U tests; auto-promote winners to a living playbook.
Weekly scorecards & pacing alerts: Generate cross-channel summaries and monitor pacing against targets, with next-step suggestions.

Quick Start

Create a new experiment with a hypothesis, a variable, and multiple variants, then log the first data point to begin the scoring process.

Frequently Asked Questions about growth-engine

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

FAQPage Schema

How do I automate A/B testing and multivariate experiments for marketing growth?▼

Automating A/B testing and multivariate experiments for marketing growth requires orchestrating hypothesis-driven tests, logging data points, and scoring results to promote winners to a living playbook. You define hypotheses, variables, and variants, then log data points to begin automatic statistical scoring.

What statistical methods are used for growth experimentation analysis?▼

Growth experimentation analysis uses bootstrap confidence intervals and Mann-Whitney U tests to evaluate results. These statistics, powered by numpy and scipy, compute experiment scores to determine winning variants and promote them to a living playbook for evidence-based decision making.

Can I run batch mode growth experiments with multiple variants?▼

Batch mode growth experiments support up to 10 variants with automatic playbook updates. You define multiple variants for a single hypothesis and variable, log data points across variants, and the system scores each variant and automatically promotes winners to the living playbook.

How do I generate weekly scorecards and pacing alerts across marketing channels?▼

Weekly scorecards and pacing alerts are generated automatically across marketing channels by logging experiment data points. The system creates cross-channel summaries, monitors pacing against targets, and produces next-step suggestions to guide evidence-based growth decisions.

Do I need numpy and scipy to run statistical analysis on growth experiments?▼

Numpy and scipy are required dependencies to run statistical analysis on growth experiments. These libraries provide the computational foundation for bootstrap confidence intervals and Mann-Whitney U tests used to score experiments and evaluate statistical significance.

What is a living playbook for growth experimentation and how does it work?▼

A living playbook for growth experimentation is an automatically updated record of winning test variants. When experiments score successfully through statistical analysis, winners are auto-promoted to the playbook, creating an evolving knowledge base of evidence-based marketing strategies.