使い方
- インストールコマンドをコピーし、自分の環境へ導入する前にソースを確認します。
- 必要なツール、認証情報、対応環境を原文で確認します。
- 入力と成果物を指定し、出力を資料と照合します。
npx skills add https://github.com/wshobson/agents --skill debugging-strategies開始前の確認
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- スター数や導入数は活動の指標であり、独立した品質・安全性試験ではありません。
リポジトリの原文
見出し、例、コード、表、リンク、参照画像を含む原文を表示しています。
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
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## 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 messagesPhase 2: Gather Information
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## 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
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## 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 servicesPhase 4: Test & Verify
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## 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 dataBefore 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
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## 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 loadPattern 2: Performance Issues
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## 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, 0xPattern 3: Production Bugs
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## 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 stagingBest Practices
- Reproduce First: Can't fix what you can't reproduce
- Isolate the Problem: Remove complexity until minimal case
- Read Error Messages: They're usually helpful
- Check Recent Changes: Most bugs are recent
- Use Version Control: Git bisect, blame, history
- Take Breaks: Fresh eyes see better
- Document Findings: Help future you
- 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
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## 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)
