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sop-dogfooding-continuous-improvement

Testing & Quality
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SOP for running continuous improvement cycles via dogfooding and adversarial validation.

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-continuous-improvement/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-continuous-improvement/. Do not write files or run scripts until I approve.

After I approve, install the complete skill folder, including required referenced files, into that project location. Verify it is discoverable, then tell me its actual invocation name and how to use it. Do not claim it is installed until you have verified it.

Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

STANDARD OPERATING PROCEDURE

Purpose

Provide a repeatable loop for applying our skills to themselves, measuring improvement deltas, and documenting learnings for continuous quality gains.

Trigger Conditions

  • Positive: periodic quality reviews, regression checks after major updates, or requests to improve a specific skill.
  • Negative: single execution runs without improvement goals; ad-hoc debugging tasks.

Guardrails

  • Confidence ceiling: Add Confidence: X.XX (ceiling: TYPE Y.YY) with ceilings {inference/report 0.70, research 0.85, observation/definition 0.95}.
  • Structure-first: Maintain examples/tests demonstrating the improvement loop and convergence criteria.
  • Adversarial validation: Include boundary inputs and noisy cases before claiming convergence (<2% delta across two runs).
  • Evidence logging: Tag artifacts with WHO/WHY and store metrics for trend analysis.

Execution Phases

  1. Plan & Baseline
    • Select the skill and metrics; capture current performance and known gaps.
    • Prepare memory namespace and retrieve prior runs.
  2. Self-Application & Iteration
    • Apply the skill to itself or representative tasks; document findings and fixes.
    • Iterate until improvements plateau.
  3. Adversarial Probing
    • Inject edge cases to test robustness; log false positives/negatives.
  4. Synthesis & Handoff
    • Summarize deltas, remaining risks, and next steps.
    • Update references/resources and state confidence with ceiling.

Output Format

  • Baseline metrics and session goals.
  • Iteration log with findings, fixes, and deltas.
  • Adversarial probe outcomes and adjustments.
  • Confidence statement and follow-up plan.

Validation Checklist

  • Baseline captured with metrics and scope.
  • At least one self-application iteration completed.
  • Adversarial probes executed; deltas measured.
  • References/resources updated with learnings.
  • Confidence ceiling provided; English-only output.

Confidence: 0.71 (ceiling: inference 0.70) - SOP rewritten using Prompt Architect confidence discipline and Skill Forge structure-first dogfooding pattern.