| name | error-diagnostics-smart-debug |
| description | Diagnoses errors from logs, traces, stack output, and symptoms, then maps likely causes to targeted verification steps. |
| license | MIT |
| metadata | {"imported_from":"codex-regular","source_repository":"https://github.com/sickn33/antigravity-awesome-skills","source_path":"~/.codex/skills/error-diagnostics-smart-debug/SKILL.md","imported_at":"2026-05-09"} |
Use this skill when
- Working on error diagnostics smart debug tasks or workflows
- Needing guidance, best practices, or checklists for error diagnostics smart debug
Do not use this skill when
- The task is unrelated to error diagnostics 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
const analysis = await aiAnalyze({
error: "Payment processing timeout",
frequency: "5% of checkouts",
environment: "production"
});
const sentryData = await getSentryIssue("CHECKOUT_TIMEOUT");
const ddTraces = await getDataDogTraces({
service: "checkout",
operation: "process_payment",
duration: ">5000ms"
});
span.setAttribute('debug.queryCount', queryCount);
span.setAttribute('debug.paymentMethodId', methodId);
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
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
