Issue Detector

Root cause analysis structures problems into four layers—Surface, Functional, Emotional, and Fundamental—then applies Why-Why analysis to trace each issue to its deepest origin. This hierarchical decomposition reveals why users truly struggle, not just surface symptoms.

Unfounded assumptions are beliefs about users treated as facts without evidence. This Skill automatically detects 10+ such assumptions within persona data, distinguishing them from validated facts to prevent building solutions on false premises.

Yes. Automation via Claude Code and Python scripts processes Phase 1 persona outputs to generate phase 2 deliverables including hierarchical issue analysis, Why-Why root-cause findings, and feasibility assessments in structured markdown formats.

Why-Why analysis iterates five times through each issue to expose fundamental causes beneath functional or emotional layers. This prevents treating symptoms instead of root problems, guiding solution design toward actual user needs.

The Skill produces issue_analysis.md and assumptions_list.md documents containing hierarchical problem breakdowns, Why-Why analysis results, solvability assessments, and detected unfounded assumptions for downstream product development.

Yes. The Skill requires Phase 1 persona analysis output as input. It processes persona data through structured problem decomposition, so foundational persona work must exist before running issue detection and assumption identification.