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nw-investigation-techniqueslisted

Evidence collection methods, problem categorization, analysis techniques, and solution design patterns
nWave-ai/nWave · ★ 541 · AI & Automation · score 84
Install: claude install-skill nWave-ai/nWave
# Investigation Techniques ## Problem Categorization ### Technical Problems | Category | Sub-Category | Common Symptoms | |----------|-------------|-----------------| | System Failures | App crashes, memory leaks, deadlocks, data corruption | Service unavailability, resource exhaustion, integrity errors | | System Failures | Hardware, network, database, security | Connectivity loss, capacity limits, access failures | | Performance | Response time: slow queries, latency, algorithmic inefficiency | High p95/p99, user-reported slowness | | Performance | Throughput: thread pool exhaustion, connection limits, queue backlog | Reduced capacity, growing queues | | Integration | Internal: component comms, data format, version conflicts | Interface errors, serialization failures | | Integration | External: third-party availability, API changes, auth failures | Timeouts, contract violations | ### Operational Problems | Category | Common Symptoms | |----------|-----------------| | Deployment: script failures, config drift, migration errors | Failed releases, environment inconsistencies | | Monitoring: alerting gaps, backup failures, incident response | Missed incidents, slow recovery | | Human factors: communication gaps, knowledge silos, skill gaps | Repeated mistakes, slow onboarding | ## Evidence Collection ### Technical Evidence Sources **Logs**: application (timestamp correlation) | system/infrastructure | database | network traces **Metrics**: performance/resource utilizat