What problem does it solve? Complex bugs, performance bottlenecks, and memory leaks often lead to blind trial-and-error fixes that waste time and introduce regressions. This Skill provides a systematic debugging methodology so issues are reproduced, isolated, and fixed with verifiable evidence. ## Core Features & Use Cases - Structured Four-Step Method: Reproduce and scope the issue (frontend/backend/network), collect evidence via logs and breakpoints, run root-cause analysis (5 Whys, bisection, diff comparison), then fix and verify with regression tests. - Full-Stack Debugging Guidance: Backend techniques include trace_id log correlation, pdb/breakpoint usage, and curl-based API checks; frontend techniques cover DevTools Console, Network panel, React DevTools, and Zustand state inspection. - Performance & Memory Profiling: Use cProfile, pstats, and snakeviz for Python performance analysis, memory_profiler for backend leaks, and Chrome DevTools Performance/Memory panels for frontend rendering and heap snapshot comparison. - Use Case: When an API intermittently returns 500 errors, grep logs by trace_id, bisect the failing code path, identify the root cause, and add a regression test before closing the issue. ## Quick Start Use the debugging skill to systematically diagnose why this API endpoint intermittently returns 500 errors and identify the root cause.
