systematic-debugging

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Use when debugging, diagnosing, or investigating any bug, test failure, flaky test, race condition, unexpected behavior, build failure, production incident, third-party breakage, root cause analysis, or performance regression before proposing fixes

Code & Development 27 stars 5 forks Updated yesterday MIT

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Skill Content

# Systematic Debugging ## Overview Random fixes waste time and create new bugs. Quick patches mask underlying issues and usually force a second debugging cycle. **Core principle:** ALWAYS find root cause before attempting a permanent fix. Symptom fixes are failure. **Debugging report quality is part of the job.** A report that lists guesses without evidence is not a passing debugging result. ## The Iron Law ``` NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST ``` If you haven't completed Phase 1, you cannot propose fixes. ### Diagnostic Instrumentation Exemption Adding temporary code to collect evidence is **NOT a fix**. The following are explicitly permitted during Phase 1 investigation: - Print/log statements to trace data flow (`fmt.Println`, `console.log`, `print()`, `logging.debug()`) - Temporary breakpoints or debug flags - Probe scripts that exercise a specific code path - Temporary test harnesses to isolate behavior - System commands (`df -h`, `lsof`, `strace`, `tcpdump`) to observe runtime state **Rules:** - Mark all diagnostic code clearly (e.g., `// DEBUG-INVESTIGATION` or `# DIAG`) - Remove or revert all diagnostic instrumentation after root cause is identified - Diagnostic code must not change program behavior — it only observes ## When to Use Use for ANY technical issue: - Test failures - Bugs in production - Unexpected behavior - Performance problems - Build failures - Integration issues - Flaky tests - Race conditions - Configuration drift - Third-...

Details

Author
johnqtcg
Repository
johnqtcg/awesome-skills
Created
4 months ago
Last Updated
yesterday
Language
Python
License
MIT

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