# Sop Dogfooding Continuous Improvement

> SOP for running continuous improvement cycles via dogfooding and adversarial validation.

- Skill: `majiayu000/sop-dogfooding-continuous-improvement-2` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add majiayu000/sop-dogfooding-continuous-improvement-2`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/sop-dogfooding-continuous-improvement-2/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/majiayu000/sop-dogfooding-continuous-improvement-2

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## 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.