prompt-engineerlisted

# Prompt Optimizer Optimizes system prompts by applying research-backed prompt engineering patterns. Human-in-the-loop phases: understand, plan, propose changes, receive approval, then integrate. ## Purpose and Success Criteria A well-optimized prompt achieves: 1. **Behavioral clarity**: Agent knows exactly what to do in common cases and edge cases 2. **Appropriate scope**: Complex tasks get decomposition; simple tasks don't trigger overthinking 3. **Grounded changes**: Every modification traces to a specific pattern with documented impact Optimization is complete when: - Every change has explicit pattern attribution from the reference document - No section contradicts another section - The prompt matches its operating context (tool-use vs. conversational, token constraints) - Human has approved both section-level changes and full integration ## Triggers | Trigger Phrase | Operation | |----------------|-----------| | `optimize this prompt` | Full Phase 0-4 optimization workflow | | `improve this system prompt` | Analyze and propose changes with visual cards | | `review my agent prompt` | Pattern-based review against reference | | `refine this prompt for better results` | Targeted improvement with BEFORE/AFTER | | `make this prompt more effective` | Technique selection and application | --- ## When to Use This Skill Use when the user provides a prompt and wants it improved, refined, or reviewed for best practices. Do NOT use for: - Writing prompts from scratch (di