debugging-strategies

Solid

Master systematic debugging techniques, profiling tools, and root cause analysis to efficiently track down bugs across any codebase or technology stack. Use when investigating bugs, performance issues, or unexpected behavior.

AI & Automation 36,222 stars 3928 forks Updated today MIT

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Skill Content

# Debugging Strategies Transform debugging from frustrating guesswork into systematic problem-solving with proven strategies, powerful tools, and methodical approaches. ## When to Use This Skill - Tracking down elusive bugs - Investigating performance issues - Understanding unfamiliar codebases - Debugging production issues - Analyzing crash dumps and stack traces - Profiling application performance - Investigating memory leaks - Debugging distributed systems ## Core Principles ### 1. The Scientific Method **1. Observe**: What's the actual behavior? **2. Hypothesize**: What could be causing it? **3. Experiment**: Test your hypothesis **4. Analyze**: Did it prove/disprove your theory? **5. Repeat**: Until you find the root cause ### 2. Debugging Mindset **Don't Assume:** - "It can't be X" - Yes it can - "I didn't change Y" - Check anyway - "It works on my machine" - Find out why **Do:** - Reproduce consistently - Isolate the problem - Keep detailed notes - Question everything - Take breaks when stuck ### 3. Rubber Duck Debugging Explain your code and problem out loud (to a rubber duck, colleague, or yourself). Often reveals the issue. ## Systematic Debugging Process ### Phase 1: Reproduce ```markdown ## Reproduction Checklist 1. **Can you reproduce it?** - Always? Sometimes? Randomly? - Specific conditions needed? - Can others reproduce it? 2. **Create minimal reproduction** - Simplify to smallest example - Remove unrelated code - Isolate th...

Details

Author
wshobson
Repository
wshobson/agents
Created
10 months ago
Last Updated
today
Language
Python
License
MIT

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