Prompt Optimization Skill

Core Philosophy

1. Model-Agnostic: Patterns effective across GPT, Claude, Gemini, etc.
2. Evidence-Based: Based on peer-reviewed research and industry consensus
3. Actionable: Each detection provides specific, implementable improvements
4. Non-Destructive: Suggest improvements while preserving user intent and minimizing constraint creep (see references/execution-quality.yaml over_optimization criteria)

Pattern Detection

P1: Critical (Must Fix)

High confidence research evidence for negative impact.