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skills/rmyndharis/antigravity-skills/debugging-toolkit-smart-debug

debugging-toolkit-smart-debug

1
rmyndharis/antigravity-skills·Developer Tools·Audit passed·Snapshot 489b5bb0c3f1
Installs
0

Summary

Use when working with debugging toolkit smart debug

SKILL.md

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.

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:

    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

    Related skills

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