error-diagnostics-smart-debug workflow skill. Use this skill when the user needs working with error diagnostics smart debug and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
error-diagnostics-smart-debug workflow skill. Use this skill when the user needs working with error diagnostics smart debug and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-awesome-skills/skills/error-diagnostics-smart-debug from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Context, Output Format, Limitations.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
Working on error diagnostics smart debug tasks or workflows
Needing guidance, best practices, or checklists for error diagnostics smart debug
The task is unrelated to error diagnostics smart debug
You need a different domain or tool outside this scope
Use when provenance needs to stay visible in the answer, PR, or review packet.
Use when copied upstream references, examples, or scripts materially improve the answer.
Operating Table
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
SKILL.md
Starts with the smallest copied file that materially changes execution
Supporting context
SKILL.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
Workflow
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
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.
Error pattern recognition
Stack trace analysis with probable causes
Component dependency analysis
Imported Workflow Notes
Imported: 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.
Imported: 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)
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
Imported: 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)
Examples
Example 1: Ask for the upstream workflow directly
Use @error-diagnostics-smart-debug-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Example 2: Ask for a provenance-grounded review
Review @error-diagnostics-smart-debug-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Example 3: Narrow the copied support files before execution
Use @error-diagnostics-smart-debug-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Example 4: Build a reviewer packet
Review @error-diagnostics-smart-debug-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Imported Usage Notes
Imported: Example: Minimal Debug Session
// Issue: "Checkout timeout errors (intermittent)"// 1. Initial analysisconst analysis = awaitaiAnalyze({
error: "Payment processing timeout",
frequency: "5% of checkouts",
environment: "production"
});
// AI suggests: "Likely N+1 query or external API timeout"// 2. Gather observability dataconst sentryData = awaitgetSentryIssue("CHECKOUT_TIMEOUT");
const ddTraces = awaitgetDataDogTraces({
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
Best Practices
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
Treat generated examples as scaffolding; adapt them to the concrete task before execution.
Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.
Troubleshooting
Problem: The operator skipped the imported context and answered too generically
Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/error-diagnostics-smart-debug, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.
Problem: The imported workflow feels incomplete during review
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Problem: The task drifted into a different specialization
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better.
Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
Related Skills
@00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
@00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
Additional Resources
Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/n/a
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
assets/n/a
Imported Reference Notes
Imported: 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
Imported: 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.
