debug-reliability

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Diagnose production or development failures, rank root-cause hypotheses, and deliver minimal-risk fixes. Use when systems are broken, unstable, flaky, or showing regressions.

javierbecerril By javierbecerril schedule Updated 2/17/2026

name: debug-reliability description: Diagnose production or development failures, rank root-cause hypotheses, and deliver minimal-risk fixes. Use when systems are broken, unstable, flaky, or showing regressions.

Debug Reliability

Restore stability quickly using evidence-driven debugging.

Required Inputs

  • AGENTS.md
  • PROJECT_CONTEXT.md
  • Symptoms, logs, traces, and recent changes

Workflow

  1. Reproduce the issue or establish a reliable observation boundary.
  2. Gather evidence from logs, metrics, traces, and diffs.
  3. Produce ranked hypotheses and disqualifying checks.
  4. Validate top hypothesis with minimal experiments.
  5. Apply smallest effective fix and verify recovery.
  6. Capture prevention tasks for Learning Engineer.

Reliability Quality Gates

  • Root cause is evidence-backed, not speculative.
  • Fix surface area is minimal and reversible.
  • Regression checks are executed for adjacent risk.

Required Output

  • Ranked hypothesis table.
  • Root cause statement with evidence.
  • Patch summary and verification results.

Handoff Contract

  • QA: regression focus and stress scenarios.
  • Learning: cause-action-result entry with guardrail proposal.

Constraints

  • Do not broad-refactor during active incident response.
  • Avoid introducing net-new architecture in emergency fixes.
  • Make uncertainty explicit.

References

  • references/playbook.md

  • references/agent-source.md

  • references/agent-source.md

Install via CLI
npx skills add https://github.com/javierbecerril/ai-workbench --skill debug-reliability
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