How does this skill help avoid vanity metrics?

The skill implements a metrics hierarchy that prioritizes diagnostic and operational data over surface-level volume stats, ensuring focus on quality and long-term retention.

Can I use this for experiment planning?

Yes, it provides a structured output artifact for experiment plans, including success criteria, baselines, data collection methods, and review cadences.

What industry standards is this skill based on?

It is grounded in established frameworks including Lean Analytics, Marty Cagan's 'Inspired' principles, and AIPMM standards for product management.

Does it support cohort analysis?

Absolutely. The skill includes specific domain context for tracking specific user groups over time to understand how changes affect different segments.

Funnel Analysis & Product Metrics

Name: Funnel Analysis & Product Metrics
Author: sethdford

bysethdford

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Analíticas y Monitorización

Optimizes product performance and user behavior tracking through structured metrics frameworks and experimental design.

This skill empowers Product Managers to move beyond vanity metrics by establishing a robust analytics foundation based on industry standards like Lean Analytics and the AIPMM framework. It facilitates the creation of comprehensive metrics hierarchies—from strategic North Star goals to diagnostic operational data—while guiding users through cohort analysis and experimentation velocity. Whether defining new success criteria or investigating performance shifts, this skill provides the structured guidance needed to drive data-informed product decisions and minimize common analytical anti-patterns.

Características Principales

01Metrics hierarchy development (Strategic, Operational, Diagnostic)

02Leading and lagging indicator identification and tracking

03Structured experiment planning and success criteria definition

04Cohort analysis and user behavior trend monitoring

05Root-cause investigation frameworks for metric anomalies

065 GitHub stars

Casos de Uso

01Defining a North Star metric and its supporting signals for a new feature launch

02Designing a controlled experiment to test a specific conversion rate hypothesis

03Investigating a sudden drop in user retention through structured diagnostic metrics

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add sethdford/claude-skills funnel-analysis

For use in Claude.ai and ChatGPT

Características Principales

01Metrics hierarchy development (Strategic, Operational, Diagnostic)

02Leading and lagging indicator identification and tracking

03Structured experiment planning and success criteria definition

04Cohort analysis and user behavior trend monitoring

05Root-cause investigation frameworks for metric anomalies

065 GitHub stars

Casos de Uso

01Defining a North Star metric and its supporting signals for a new feature launch

02Designing a controlled experiment to test a specific conversion rate hypothesis

03Investigating a sudden drop in user retention through structured diagnostic metrics

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add sethdford/claude-skills funnel-analysis

For use in Claude.ai and ChatGPT