pm-root-cause

Name: pm-root-cause
Availability: InStock
Author: ljucask

Diagnose product anomalies through structured root-cause analysis workflows.

1|Updated May 28, 2026

Search Tags:#root cause #hypothesis testing #funnel analysis #5 whys #product analytics #anomaly diagnosis

Ready to run

npx skills add https://github.com/ljucask/pureinn-product-development --skill pm-root-cause

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pm-root-cause
Source: https://github.com/ljucask/pureinn-product-development/tree/main/skills/pm-root-cause
Command: npx skills add https://github.com/ljucask/pureinn-product-development --skill pm-root-cause

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps product teams diagnose unexpected product behavior by separating symptoms from root causes and preventing premature conclusions.

Core Features & Use Cases

Structured Anomaly Investigation: Guides teams through measurement validation, segmentation analysis, change discovery, candidate cause mapping, and root-cause drilling.
Evidence-Based Diagnosis: Separates confirmed evidence from untested hypotheses using methods like 5 Whys, fishbone analysis, and differential diagnosis.
Use Case: When a product metric drops, churn spikes, or a feature is not adopted, use this Skill to identify likely causes, define confirmation tests, and determine corrective actions.

Quick Start

Describe the product anomaly you need to investigate, including the metric change, timing, and affected area, and use the pm-root-cause skill to run a structured diagnosis.

Frequently Asked Questions about pm-root-cause

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

FAQPage Schema

How do I find the root cause of a conversion drop in my product funnel?▼

To find the root cause of a conversion drop, you need structured anomaly diagnosis using funnel analysis, segmentation, and hypothesis testing to separate symptoms from actual causes. This approach guides you through measurement validation, change discovery, and candidate cause mapping to prevent premature conclusions.

What is the best way to diagnose a sudden churn spike?▼

Diagnosing a sudden churn spike requires evidence-based anomaly investigation that separates confirmed evidence from untested hypotheses. Using methods like 5 Whys and differential diagnosis, you rank potential causes, define confirmation tests, and determine corrective actions rather than jumping to conclusions.

How do I investigate why a new feature has low adoption?▼

Investigating low feature adoption involves running a structured diagnostic workflow that includes segmentation analysis and root-cause drilling. You map candidate causes, apply hypothesis ranking, and validate tests to identify why users are not engaging with the feature.

What is hypothesis testing used for in product analytics?▼

Hypothesis testing in product analytics is used to validate or reject candidate causes during anomaly diagnosis. By ranking hypotheses and running confirmation tests, teams ensure that identified root causes are backed by confirmed evidence rather than assumptions.

When do I need to use 5 Whys analysis for product anomalies?▼

You need 5 Whys analysis when drilling into candidate causes during product anomaly investigation to move past surface-level symptoms. It is used alongside fishbone analysis and differential diagnosis to separate confirmed evidence from untested hypotheses.

Can I use this approach for operational incidents and retention declines?▼

Yes, structured anomaly diagnosis applies to operational incidents, retention declines, churn spikes, and feature adoption issues. The workflow handles any unexpected product behavior by tracking evidence, performing segmentation analysis, and validating corrective actions.