techwavedev Use this skill when
- Working on debugging toolkit smart debug tasks or workflows
- Needing guidance, best practices, or checklists for debugging toolkit smart debug
Do not use this skill when
- The task is unrelated to debugging toolkit smart debug
- You need a different domain or tool outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open
resources/implementation-playbook.md.
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
// 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:
- Issue Summary: Error, frequency, impact
- Root Cause: Detailed diagnosis with evidence
- Fix Proposal: Code changes, risk, impact
- Validation Plan: Steps to verify fix
- Prevention: Tests, monitoring, documentation
Focus on actionable insights. Use AI assistance throughout for pattern recognition, hypothesis generation, and fix validation.
Issue to debug: $ARGUMENTS
AGI Framework Integration
Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills
Memory-First Protocol
Retrieve prior error resolutions and debugging strategies. The hybrid search excels here — BM25 finds exact error codes/stack traces while vectors find semantically similar past issues.
# Check for prior debugging/diagnostics context before starting
python3 execution/memory_manager.py auto --query "error patterns and debugging solutions for Debugging Toolkit Smart Debug"
Storing Results
After completing work, store debugging/diagnostics decisions for future sessions:
python3 execution/memory_manager.py store \
--content "Root cause: memory leak from unclosed DB connections in pool — fixed with context manager" \
--type error --project <project> \
--tags debugging-toolkit-smart-debug debugging
Multi-Agent Collaboration
Store error resolutions so any agent encountering the same issue retrieves the fix instantly instead of re-debugging.
python3 execution/cross_agent_context.py store \
--agent "<your-agent>" \
--action "Debugged and resolved critical issue — root cause documented for future reference" \
--project <project>
Self-Annealing Loop
When this skill resolves an error, store the fix in memory AND update the relevant directive. The system gets stronger with each resolved issue.
BM25 Exact Match
Error codes, stack traces, and log messages are best found via BM25 keyword search. The hybrid system automatically uses exact matching for these patterns.