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growth-audit

Analyze web app funnel metrics, identify bottlenecks, and design RICE-scored experiments.

61|9|Updated Mar 9, 2026
Search Tags:#analytics#experiment design#growth#conversion optimization#a/b testing#funnel analysis
One-click install
npx skills add https://github.com/jonathanmalkin/jules --skill growth-audit
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: growth-audit
Source: https://github.com/jonathanmalkin/jules/tree/main/.claude/skills/growth-audit
Command: npx skills add https://github.com/jonathanmalkin/jules --skill growth-audit

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill helps you systematically analyze your web app's growth funnel, identify key bottlenecks, and design effective A/B experiments to improve conversion rates.

Core Features & Use Cases

  • Funnel Analysis: Visualize and quantify user drop-off at each stage of your funnel.
  • Bottleneck Identification: Pinpoint the most impactful areas for improvement.
  • Experiment Design: Create concrete, testable hypotheses and detailed experiment plans, considering traffic realities and implementation effort.
  • Use Case: If your signup rate is low, this Skill will analyze where users are dropping off, suggest the most promising areas to test, and help you design an experiment to increase signups.

Quick Start

Run a growth audit for the last 30 days to identify bottlenecks and suggest experiments.

Frequently Asked Questions about growth-audit

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

FAQPage Schema
How do I conduct a funnel analysis to identify conversion bottlenecks in my web app?▼

Funnel analysis identifies conversion bottlenecks by pulling stage-by-stage metrics, quantifying user drop-off rates, and visualizing where users exit the web app's growth funnel.

How do I design A/B experiments for low traffic web applications?▼

Design A/B experiments for low traffic by formulating testable hypotheses, estimating impact with RICE scoring, and planning deploy-and-compare tests that account for limited visitors and absent feature flagging.

What is RICE scoring and how is it used in growth experiment design?▼

RICE scoring in growth experiment design estimates potential impact by ranking testable hypotheses based on reach, confidence, and effort to prioritize A/B tests yielding the highest conversion improvements.

Can I run conversion optimization experiments without a feature flagging system?▼

You can optimize conversions without feature flagging by designing deploy-and-compare experiments that measure funnel metric changes directly through analytics scripts rather than relying on toggle infrastructure.

What's the best way to prioritize growth experiments for a web application?▼

Prioritize growth experiments by analyzing funnel metrics to pinpoint bottlenecks, calculating conversion drop-off rates, and applying RICE scoring to rank testable hypotheses by estimated impact and effort.

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