What problem does it solve? Developers often waste hours applying guesswork fixes that mask symptoms instead of resolving the underlying defect. This Skill enforces a systematic, evidence-driven debugging methodology so bugs, flaky tests, race conditions, and production issues are fixed at their root cause the first time. ## Core Features & Use Cases - Four-Phase Framework: Structured workflow covering root cause investigation, pattern analysis, hypothesis testing, and verified implementation. - Debugging Techniques Library: Practical guidance on git bisect, differential debugging, instrumentation, rubber duck debugging, and time-travel debugging. - Common Root Cause Catalog: Concrete code examples for race conditions, shared mutable state, incorrect assumptions, off-by-one errors, and timezone issues. - Use Case: When a login endpoint fails intermittently, use this Skill to reproduce the failure, gather log evidence, form a testable hypothesis (e.g., clock skew between servers), and verify the fix against the reproducer before shipping. ## Quick Start Ask the AI to systematically debug the failing test or production error you are seeing, starting with root cause investigation before proposing any fix.