debugging-strategies – Coding skill for AI agents

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

## 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 the problem

3. **Document steps**
   - Write down exact steps
   - Note environment details
   - Capture error messages

Phase 2: Gather Information

## Information Collection

1. **Error Messages**
   - Full stack trace
   - Error codes
   - Console/log output

2. **Environment**
   - OS version
   - Language/runtime version
   - Dependencies versions
   - Environment variables

3. **Recent Changes**
   - Git history
   - Deployment timeline
   - Configuration changes

4. **Scope**
   - Affects all users or specific ones?
   - All browsers or specific ones?
   - Production only or also dev?

Phase 3: Form Hypothesis

## Hypothesis Formation

Based on gathered info, ask:

1. **What changed?**
   - Recent code changes
   - Dependency updates
   - Infrastructure changes

2. **What's different?**
   - Working vs broken environment
   - Working vs broken user
   - Before vs after

3. **Where could this fail?**
   - Input validation
   - Business logic
   - Data layer
   - External services

Phase 4: Test & Verify

## Testing Strategies

1. **Binary Search**
   - Comment out half the code
   - Narrow down problematic section
   - Repeat until found

2. **Add Logging**
   - Strategic console.log/print
   - Track variable values
   - Trace execution flow

3. **Isolate Components**
   - Test each piece separately
   - Mock dependencies
   - Remove complexity

4. **Compare Working vs Broken**
   - Diff configurations
   - Diff environments
   - Diff data

Before selecting debugging tools or performing bisect, differential, trace, or memory analysis, read references/tools-and-techniques.md.

Debugging Patterns by Issue Type

Pattern 1: Intermittent Bugs

## Strategies for Flaky Bugs

1. **Add extensive logging**
   - Log timing information
   - Log all state transitions
   - Log external interactions

2. **Look for race conditions**
   - Concurrent access to shared state
   - Async operations completing out of order
   - Missing synchronization

3. **Check timing dependencies**
   - setTimeout/setInterval
   - Promise resolution order
   - Animation frame timing

4. **Stress test**
   - Run many times
   - Vary timing
   - Simulate load

Pattern 2: Performance Issues

## Performance Debugging

1. **Profile first**
   - Don't optimize blindly
   - Measure before and after
   - Find bottlenecks

2. **Common culprits**
   - N+1 queries
   - Unnecessary re-renders
   - Large data processing
   - Synchronous I/O

3. **Tools**
   - Browser DevTools Performance tab
   - Lighthouse
   - Python: cProfile, line_profiler
   - Node: clinic.js, 0x

Pattern 3: Production Bugs

## Production Debugging

1. **Gather evidence**
   - Error tracking (Sentry, Bugsnag)
   - Application logs
   - User reports
   - Metrics/monitoring

2. **Reproduce locally**
   - Use production data (anonymized)
   - Match environment
   - Follow exact steps

3. **Safe investigation**
   - Don't change production
   - Use feature flags
   - Add monitoring/logging
   - Test fixes in staging

Best Practices

1. Reproduce First: Can't fix what you can't reproduce

2. Isolate the Problem: Remove complexity until minimal case

3. Read Error Messages: They're usually helpful

4. Check Recent Changes: Most bugs are recent

5. Use Version Control: Git bisect, blame, history

6. Take Breaks: Fresh eyes see better

7. Document Findings: Help future you

8. Fix Root Cause: Not just symptoms

Common Debugging Mistakes

• Making Multiple Changes: Change one thing at a time
• Not Reading Error Messages: Read the full stack trace
• Assuming It's Complex: Often it's simple
• Debug Logging in Prod: Remove before shipping
• Not Using Debugger: console.log isn't always best
• Giving Up Too Soon: Persistence pays off
• Not Testing the Fix: Verify it actually works

Quick Debugging Checklist

## When Stuck, Check:

- [ ] Spelling errors (typos in variable names)
- [ ] Case sensitivity (fileName vs filename)
- [ ] Null/undefined values
- [ ] Array index off-by-one
- [ ] Async timing (race conditions)
- [ ] Scope issues (closure, hoisting)
- [ ] Type mismatches
- [ ] Missing dependencies
- [ ] Environment variables
- [ ] File paths (absolute vs relative)
- [ ] Cache issues (clear cache)
- [ ] Stale data (refresh database)