advanced-debugging

Race conditions and flaky tests in TypeScript/JavaScript can be debugged by instrumenting code with structured logging and timing markers, then isolating test environments using Git worktrees to compare outcomes across parallel explorations. This Skill automates instrumentation and log analysis to pinpoint timing-dependent failures.

Instrumentation adds DEBUG logs and timing markers automatically to your code. Run the instrumentation script on target files to inject tracking points, then analyze collected traces to identify bottlenecks, state changes, and execution flow. This creates standardized logs for hypothesis-driven debugging.

Yes. Git worktrees let you explore multiple debugging hypotheses in parallel without branching. This Skill handles worktree setup and cleanup automatically, letting you test different instrumentation strategies and fixes side-by-side while keeping your main branch untouched.

Hypothesis-driven debugging structures your investigation: generate testable theories about the root cause, instrument the code to capture relevant state and timing data, then analyze logs to validate or refute each hypothesis. This narrows search space in complex codebases where bugs emerge only under specific conditions.

Yes. The Skill integrates with Playwright-based browser automation for debugging browser context issues. Instrumentation and log analysis extend to browser-side errors, timing problems, and test flakiness, enabling end-to-end debugging across Node.js and browser environments.

Instrumentation captures errors, stack traces, performance bottlenecks, race conditions, and hard-to-reproduce bugs. Standardized DEBUG logs with timing and state tracking reveal execution flow, variable changes, and async operation ordering—critical for diagnosing issues that don't surface consistently.