Marketplace

sop-dogfooding-continuous-improvement

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

allowed_tools: Read, Write, Edit, Bash, Glob, Grep, Task, TodoWrite
model: sonnet

$ Instalar

git clone https://github.com/DNYoussef/context-cascade /tmp/context-cascade && cp -r /tmp/context-cascade/skills/quality/sop-dogfooding-continuous-improvement ~/.claude/skills/context-cascade

// tip: Run this command in your terminal to install the skill


name: sop-dogfooding-continuous-improvement description: SOP for running continuous improvement cycles via dogfooding and adversarial validation. allowed-tools:

  • Read
  • Write
  • Edit
  • Bash
  • Glob
  • Grep
  • Task
  • TodoWrite model: sonnet x-version: 3.2.0 x-category: quality x-vcl-compliance: v3.1.1 x-cognitive-frames:
  • HON
  • MOR
  • COM
  • CLS
  • EVD
  • ASP
  • SPC

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.