What problem does it solve? Random fixes and quick patches waste hours and mask underlying issues. This Skill forces a disciplined investigation process so bugs are fixed at their root cause instead of treating symptoms, even under time pressure, exhaustion, or authority pressure. ## Core Features & Use Cases - Four-Phase Process: Root Cause Investigation, Pattern Analysis, Hypothesis Testing, and Implementation, with a hard rule that no fix may be proposed before Phase 1 completes. - Evidence Grading: Every debug conclusion must be tagged [C] Confirmed, [D] Deduced, or [H] Hypothesized so readers can judge confidence levels. - Architecture Escalation: After three failed fixes, the process stops and questions the architecture instead of attempting a fourth patch. - Supporting Techniques: Includes root-cause call-stack tracing, defense-in-depth validation layers, condition-based waiting for flaky tests, and a test-polluter bisection script. - Use Case: A production test fails intermittently. Instead of adding arbitrary sleep timeouts, you trace the data flow backward, form a single hypothesis, create a failing test, and fix the actual source. ## Quick Start Ask the AI to debug a failing test or bug using the systematic-debugging skill and require root cause evidence before any fix.