Stacktrace Log Triage

Source mapping: Tier 1 critical skill derived from Kotlin_Spring_Developer_Pipeline.md (SK-15).

Mission

Turn noisy failure evidence into a ranked diagnosis with explicit confidence. Always distinguish symptom, proximate cause, and true root cause.

Gather Evidence First

• Read the full stack trace, not only the top frame.
• Read the surrounding log lines before and after the first failure.
• Capture timestamps, correlation ids, request ids, thread names, and active profiles when available.
• Read recent relevant code or config changes if they are available.
• Reuse repository constraints from project-context-ingestion when possible.

Triage Workflow

1. Find the earliest meaningful failure signal.
2. Walk the Caused by: chain until it stops getting more specific.
3. Classify the incident:
   • DI and context startup
   • configuration binding
   • serialization or validation
   • SQL or migration
   • HTTP or timeout
   • security
   • concurrency or locking
   • classpath or version mismatch
4. Separate what failed from why it failed.
5. Rank hypotheses when evidence is incomplete.
6. Propose a quick mitigation and a proper fix separately.

Diagnostic Rules

• Treat BeanCreationException, InvocationTargetException, and similar wrappers as transport, not diagnosis.
• Treat the first user-code frame after framework wrappers as high-signal evidence.
• Use log chronology, not stack depth alone.
• If the logs are truncated, say so and lower confidence.
• Prefer an explanation grounded in the observed code path or configuration over pattern-matching from memory.

Advanced Signal Patterns

• Check suppressed exceptions, root-cause logging one or two lines above the stack trace, and companion failures on adjacent threads. The most useful clue is often not in the main stack at all.
• Distinguish hard failures from saturation patterns: connection pool exhaustion, thread pool starvation, event-loop blocking, retry storms, and deadlock retries often show up as secondary symptoms first.
• For SQL failures, inspect vendor codes and SQL state when available. They are often more actionable than the wrapper exception type.
• For startup failures after version or deploy changes, compare classpath and configuration drift before assuming a code bug.
• For intermittent failures, weigh time correlation and concurrency context heavily. A race rarely leaves a clean single-thread narrative.
• For OOM or GC-related incidents, plain stack traces are weak evidence. Thread dumps, heap symptoms, and allocation context matter more.
• If metrics exist, correlate the first error spike with latency, pool saturation, or downstream dependency degradation rather than treating logs in isolation.
• If the symptom appears in reactive or coroutine code, verify whether context propagation broke logging correlation before dismissing missing IDs or misleading thread names.

Expert Heuristics

• The first fix after triage should reduce uncertainty as well as reduce pain. Prefer mitigations that also sharpen diagnosis.
• If several candidate causes fit, rank them by evidence, blast radius, and reversibility of the proposed mitigation.
• If the trace points to a framework wrapper, move outward to code and config that changed recently, then inward again through the causal chain.
• Always state what evidence would falsify the leading hypothesis.

Output Contract

Return these sections:

• Root cause: confirmed or most likely cause in plain language.
• Confidence: confirmed, high, medium, or low.
• Evidence: the log line, exception, config fact, or code path supporting the diagnosis.
• Hotfix: the safest immediate containment or unblocking step.
• Long-term fix: the proper code or configuration correction.
• Reproduction and verification: how to reproduce, test, and monitor the fix.

Safety Rules

• Label hypotheses as hypotheses.
• Do not recommend risky schema changes, version upgrades, or broad refactors as a hotfix unless the incident truly leaves no safer option.
• Do not claim certainty when the logs are incomplete.
• Do not ignore alerting or monitoring implications after the fix.

Kotlin-Specific Checks

• Watch for platform-type NullPointerException.
• Watch for coroutine stack traces that obscure the original call path.
• Watch for missing Kotlin compiler plugins causing runtime behavior gaps.
• Watch for Jackson plus Kotlin constructor issues that surface as generic deserialization errors.

Quality Bar

A good run of this skill gives the user a path from failure evidence to safe remediation. A bad run repeats the top exception, ignores the Caused by: chain, or offers a dangerous hotfix without explaining the risk.