What problem does it solve? Debugging often devolves into guesswork and random code changes. This Skill replaces that with a structured scientific method—reproduce, gather evidence, hypothesize, test—so you find root causes faster across JavaScript, TypeScript, Python, and Go codebases. ## Core Features & Use Cases - Systematic Four-Phase Process: Reproduce the bug, gather environment and error information, form hypotheses, and verify fixes with binary search and isolation techniques. - Language-Specific Tooling: Ready-to-use debugger setups for Chrome DevTools, VS Code, pdb/ipdb, Delve, plus profiling with cProfile, pprof, and the Performance API. - Pattern Playbooks: Targeted strategies for intermittent bugs, performance bottlenecks, memory leaks, and production-only failures, including git bisect workflows and heap snapshot comparison. - Use Case: A flaky test fails only in CI. Apply the intermittent-bug pattern: add timing logs, check for race conditions in async code, then use git bisect to locate the regressing commit. ## Quick Start Ask the AI to help debug a specific error by pasting the stack trace and describing the expected versus actual behavior.