| name | debugging |
| description | Systematic root-cause debugging: reproduce, investigate, hypothesize, fix with verification. Use when asked to "debug this", "fix this bug", "why is this failing", "troubleshoot", or mentions errors, stack traces, broken tests, flaky tests, regressions, or unexpected behavior. |
Debugging
The Iron Law
Never propose a fix without first identifying the root cause. "Quick fix now, investigate later" is forbidden — it creates harder bugs.
Process
1. Reproduce — make the bug consistent. If intermittent, run N times under stress or simulate poor conditions (slow network, low memory) until it triggers reliably.
2. Investigate — trace backward through the call chain from the symptom. Add diagnostic logging at each component boundary. Compare working vs broken state using a differential table (environment, version, data, timing — what changed?).
3. Hypothesize and test — one change at a time. If a hypothesis is wrong, fully revert before testing the next. Use git bisect to find regressions efficiently.
4. Fix and verify — create a failing test FIRST, then fix. Run the test. Confirm the original reproduction case passes. No completion claims without fresh verification evidence.
Three-Fix Threshold
After 3 failed fix attempts, STOP. The problem is likely architectural, not a surface bug. Step back and question assumptions about how the system works. Read the actual code path end-to-end instead of spot-checking.
Escalation: Competing Hypotheses
When the cause is unclear across multiple components, use Analysis of Competing Hypotheses:
- Generate hypotheses across failure modes: logic error, data issue, state problem, integration failure, resource exhaustion, environment
- Investigate each with evidence: Direct (strong), Correlational (medium), Testimonial (weak)
- Cite evidence with
file:line references
- Rank by confidence. If multiple hypotheses are equally supported, suspect compound causes.
Intermittent Issues
- Track with correlation IDs across distributed components
- Race conditions: look for shared mutable state, check-then-act patterns, missing locks
- Resource exhaustion: monitor memory growth, connection pool depletion, file descriptor leaks
- Timing-dependent: replace arbitrary
sleep() with condition-based polling — wait for the actual state, not a duration
Defense-in-Depth Validation
After fixing, validate at every layer — not just where the bug appeared:
- Entry: does invalid input get caught?
- Business logic: does the fix handle edge cases?
- Environment: does it work across configurations?
- Instrumentation: add logging to detect recurrence
Bug Triage
When multiple bugs exist, prioritize by:
- Severity (data loss > crash > wrong output > cosmetic) separately from Priority (blocking release > customer-facing > internal)
- Reproducibility: always > sometimes > once. "Sometimes" bugs need instrumentation before fixing.
- Quick wins: if a fix is < 5 minutes and unblocks others, do it first
Common Patterns
- Null/undefined access — trace where the value was expected to be set, check all code paths
- Off-by-one — check
< vs <=, array length vs last index, loop boundaries
- Async ordering — missing
await, unhandled promise rejection, callback firing before setup completes
- Type coercion —
== vs ===, string-to-number conversion, truthy/falsy edge cases
- Timezone — always store UTC, convert at display. Check DST transitions.
Anti-Patterns
- Shotgun debugging (random changes without hypothesis) — revert and think instead
- Multiple simultaneous changes — isolate each change or you can't learn what worked
- Fixing the symptom not the cause — the same bug will resurface differently
- Ignoring intermittent failures ("works on my machine") — instrument and reproduce under load instead
