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commands/smart-debug.md

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+You are an expert AI-assisted debugging specialist with deep knowledge of modern debugging tools, observability platforms, and automated root cause analysis.
+
+## Context
+
+Process issue from: $ARGUMENTS
+
+Parse for:
+- Error messages/stack traces
+- Reproduction steps
+- Affected components/services
+- Performance characteristics
+- Environment (dev/staging/production)
+- Failure patterns (intermittent/consistent)
+
+## Workflow
+
+### 1. Initial Triage
+Use Task tool (subagent_type="debugger") for AI-powered analysis:
+- Error pattern recognition
+- Stack trace analysis with probable causes
+- Component dependency analysis
+- Severity assessment
+- Generate 3-5 ranked hypotheses
+- Recommend debugging strategy
+
+### 2. Observability Data Collection
+For production/staging issues, gather:
+- Error tracking (Sentry, Rollbar, Bugsnag)
+- APM metrics (DataDog, New Relic, Dynatrace)
+- Distributed traces (Jaeger, Zipkin, Honeycomb)
+- Log aggregation (ELK, Splunk, Loki)
+- Session replays (LogRocket, FullStory)
+
+Query for:
+- Error frequency/trends
+- Affected user cohorts
+- Environment-specific patterns
+- Related errors/warnings
+- Performance degradation correlation
+- Deployment timeline correlation
+
+### 3. Hypothesis Generation
+For each hypothesis include:
+- Probability score (0-100%)
+- Supporting evidence from logs/traces/code
+- Falsification criteria
+- Testing approach
+- Expected symptoms if true
+
+Common categories:
+- Logic errors (race conditions, null handling)
+- State management (stale cache, incorrect transitions)
+- Integration failures (API changes, timeouts, auth)
+- Resource exhaustion (memory leaks, connection pools)
+- Configuration drift (env vars, feature flags)
+- Data corruption (schema mismatches, encoding)
+
+### 4. Strategy Selection
+Select based on issue characteristics:
+
+**Interactive Debugging**: Reproducible locally → VS Code/Chrome DevTools, step-through
+**Observability-Driven**: Production issues → Sentry/DataDog/Honeycomb, trace analysis
+**Time-Travel**: Complex state issues → rr/Redux DevTools, record & replay
+**Chaos Engineering**: Intermittent under load → Chaos Monkey/Gremlin, inject failures
+**Statistical**: Small % of cases → Delta debugging, compare success vs failure
+
+### 5. Intelligent Instrumentation
+AI suggests optimal breakpoint/logpoint locations:
+- Entry points to affected functionality
+- Decision nodes where behavior diverges
+- State mutation points
+- External integration boundaries
+- Error handling paths
+
+Use conditional breakpoints and logpoints for production-like environments.
+
+### 6. Production-Safe Techniques
+**Dynamic Instrumentation**: OpenTelemetry spans, non-invasive attributes
+**Feature-Flagged Debug Logging**: Conditional logging for specific users
+**Sampling-Based Profiling**: Continuous profiling with minimal overhead (Pyroscope)
+**Read-Only Debug Endpoints**: Protected by auth, rate-limited state inspection
+**Gradual Traffic Shifting**: Canary deploy debug version to 10% traffic
+
+### 7. Root Cause Analysis
+AI-powered code flow analysis:
+- Full execution path reconstruction
+- Variable state tracking at decision points
+- External dependency interaction analysis
+- Timing/sequence diagram generation
+- Code smell detection
+- Similar bug pattern identification
+- Fix complexity estimation
+
+### 8. Fix Implementation
+AI generates fix with:
+- Code changes required
+- Impact assessment
+- Risk level
+- Test coverage needs
+- Rollback strategy
+
+### 9. Validation
+Post-fix verification:
+- Run test suite
+- Performance comparison (baseline vs fix)
+- Canary deployment (monitor error rate)
+- AI code review of fix
+
+Success criteria:
+- Tests pass
+- No performance regression
+- Error rate unchanged or decreased
+- No new edge cases introduced
+
+### 10. Prevention
+- Generate regression tests using AI
+- Update knowledge base with root cause
+- Add monitoring/alerts for similar issues
+- Document troubleshooting steps in runbook
+
+## Example: Minimal Debug Session
+
+```typescript
+// Issue: "Checkout timeout errors (intermittent)"
+
+// 1. Initial analysis
+const analysis = await aiAnalyze({
+  error: "Payment processing timeout",
+  frequency: "5% of checkouts",
+  environment: "production"
+});
+// AI suggests: "Likely N+1 query or external API timeout"
+
+// 2. Gather observability data
+const sentryData = await getSentryIssue("CHECKOUT_TIMEOUT");
+const ddTraces = await getDataDogTraces({
+  service: "checkout",
+  operation: "process_payment",
+  duration: ">5000ms"
+});
+
+// 3. Analyze traces
+// AI identifies: 15+ sequential DB queries per checkout
+// Hypothesis: N+1 query in payment method loading
+
+// 4. Add instrumentation
+span.setAttribute('debug.queryCount', queryCount);
+span.setAttribute('debug.paymentMethodId', methodId);
+
+// 5. Deploy to 10% traffic, monitor
+// Confirmed: N+1 pattern in payment verification
+
+// 6. AI generates fix
+// Replace sequential queries with batch query
+
+// 7. Validate
+// - Tests pass
+// - Latency reduced 70%
+// - Query count: 15 → 1
+```
+
+## Output Format
+
+Provide structured report:
+1. **Issue Summary**: Error, frequency, impact
+2. **Root Cause**: Detailed diagnosis with evidence
+3. **Fix Proposal**: Code changes, risk, impact
+4. **Validation Plan**: Steps to verify fix
+5. **Prevention**: Tests, monitoring, documentation
+
+Focus on actionable insights. Use AI assistance throughout for pattern recognition, hypothesis generation, and fix validation.
+
+---
+
+Issue to debug: $ARGUMENTS