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sop-dogfooding-quality-detection

Testing & Quality
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SOP for detecting quality regressions during dogfooding runs and turning them into actionable fixes.

QUICK START

How to use this skill

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  1. Open your project in Codex.
  2. Copy the prompt below and paste it into your agent.
  3. Review the proposed files and risks before you approve installation.
Prompt to paste
I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/majiayu000/claude-skill-registry/blob/HEAD/skills/development/sop-dogfooding-quality-detection/SKILL.md

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/sop-dogfooding-quality-detection/. Do not write files or run scripts until I approve.

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STANDARD OPERATING PROCEDURE

Purpose

Identify quality regressions and latent issues while dogfooding, ensuring findings are evidenced, prioritized, and fed back into improvement loops.

Trigger Conditions

  • Positive: active dogfooding sessions, regression sweeps after releases, or monitoring new features for emergent issues.
  • Negative: isolated bug triage without self-application or pattern capture.

Guardrails

  • Confidence ceiling: Use Confidence: X.XX (ceiling: TYPE Y.YY) with ceilings {inference/report 0.70, research 0.85, observation/definition 0.95}.
  • Evidence-first: Record file:line, logs, metrics, or reproduction steps for each detected issue.
  • Structure-first: Update examples/tests to reflect newly detected regressions and their fixes.
  • Prioritization: Tag severity and blast radius; block release on critical regressions until resolved or waived with rationale.

Execution Phases

  1. Observation & Capture
    • Monitor outputs, logs, and behaviors during dogfooding; collect anomalies.
    • Normalize entries with severity, location, and reproduction notes.
  2. Validation & Classification
    • Reproduce findings; distinguish false positives and intentional behavior.
    • Map to categories (correctness, performance, UX, security, reliability).
  3. Remediation & Feedback
    • Propose fixes and owners; add tests to prevent recurrence.
    • Feed learnings into pattern retrieval and references.
  4. Confidence & Closure
    • Confirm fixes or document waivers; state residual risk and confidence with ceiling.

Output Format

  • Log of detected issues with evidence and severity.
  • Reproduction steps and validation results.
  • Remediation plan and test updates.
  • Confidence statement using ceiling syntax.

Validation Checklist

  • Evidence captured with location/steps for each issue.
  • False positives filtered; categories assigned.
  • Fixes/tests identified and owners named.
  • Patterns/references updated where applicable.
  • Confidence ceiling provided; English-only output.

Confidence: 0.70 (ceiling: inference 0.70) - SOP rewritten per Prompt Architect confidence discipline and Skill Forge structure-first detection loop.