skills.rest
jayhjenkins
jayhjenkinsCOMMUNITY
Explore all skills by this author →
GitHub

root-cause-diagnosis

Diagnose metric anomaly root causes using 4-dimension segmentation and hypothesis testing.

1|2|Updated Dec 3, 2025
Search Tags:#data-analysis#decision-making#root-cause#metrics-diagnosis#4-dimension segmentation#intrinsic-extrinsic#hypothesis-table
One-click install
npx skills add https://github.com/jayhjenkins/ProductOSv0.2 --skill root-cause-diagnosis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: root-cause-diagnosis
Source: https://github.com/jayhjenkins/ProductOSv0.2/tree/main/.claude/skills/metrics-analysis/root-cause-diagnosis
Command: npx skills add https://github.com/jayhjenkins/ProductOSv0.2 --skill root-cause-diagnosis

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Root cause diagnosis provides a systematic method to identify why metrics change unexpectedly by applying 4-dimension segmentation (People, Geography, Technology, Time), differentiating intrinsic vs extrinsic factors, and using a hypothesis table to evaluate potential causes.

Core Features & Use Cases

  • 4-D segmentation guidelines to narrow down affected segments by People, Geography, Technology, and Time.
  • Intrinsic vs Extrinsic factor analysis to distinguish internal changes from external events.
  • Hypothesis Table Method to structure, predict, and test multiple causes against observed data.
  • Stakeholder consultation and evidence-gathering workflow to support root cause resolution.

Quick Start

Run root-cause-diagnosis on the latest metric incident to surface the top 2-3 suspected causes and generate a hypothesis table.

Frequently Asked Questions about root-cause-diagnosis

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

FAQPage Schema
How do I diagnose the root cause of a sudden metric anomaly?▼

Diagnose metric anomalies by applying 4-dimension segmentation across People, Geography, Technology, and Time to isolate affected user segments and identify likely causes.

What is the hypothesis table method for root cause analysis?▼

The hypothesis table method structures root cause analysis by listing potential causes, predicting their expected data patterns, and testing them against observed metric changes to validate suspected causes.

How do I distinguish intrinsic vs extrinsic factors during data analysis?▼

Differentiate intrinsic vs extrinsic factors by evaluating whether metric changes stem from internal product modifications or external events, using evidence-based workflows to isolate the true root cause.

What is the best way to structure metric diagnosis for unexpected data changes?▼

Structure metric diagnosis using a systematic workflow of data collection, 4-dimension segmentation, and hypothesis testing to surface the top suspected causes for unexpected metric changes.

Can I use 4-dimension segmentation for gradual metric drops across different platforms?▼

Yes, 4-dimension segmentation effectively diagnoses gradual metric changes by narrowing down affected segments across user demographics, geographies, technology platforms, and specific time windows.

Related Skills

Explore More Data & Analytics Skills→
wachawowachawo

drilldown-analyzer

Decomposes aggregate metric data into dimensional segments to identify root causes.

Community
Intermediate
yuki-aigcyuki-aigc
59

alert-rca

Identify alert root causes by analyzing multi-source data and reconstructing incident timelines.

Community
Advanced
axiomhqaxiomhq
59

detect-anomalies

Identify anomalies in Axiom datasets using statistical analysis on logs, metrics, and traces.

Official
Advanced
jayhjenkinsjayhjenkins
1

metric-diagnosis

Analyze unexpected metric changes with structured root-cause analysis across 4D segments.

Community
Advanced