when-optimizing-prompts-use-prompt-architectlisted

# Prompt Architect - Evidence-Based Prompt Engineering ## Overview Comprehensive framework for analyzing, creating, and refining prompts for AI systems (Claude, GPT, etc.). Applies structural optimization, self-consistency patterns, and anti-pattern detection to transform prompts into highly effective versions. ## When to Use This Skill - Creating new prompts for AI systems - Existing prompts produce poor results - Inconsistent AI outputs - Need to improve prompt clarity - Applying evidence-based prompt engineering - Optimizing agent instructions - Building prompt libraries ## Theoretical Foundation ### Evidence-Based Techniques 1. **Chain-of-Thought (CoT)**: Explicit reasoning steps 2. **Self-Consistency**: Multiple reasoning paths 3. **ReAct**: Reasoning + Acting pattern 4. **Program-of-Thought**: Structured logic 5. **Plan-and-Solve**: Decomposition strategy 6. **Role-Playing**: Persona assignment 7. **Few-Shot Learning**: Example-based instruction ### Prompt Structure Principles ``` [System Context] → [Role Definition] → [Task Description] → [Constraints] → [Format Specification] → [Examples] → [Quality Criteria] ``` ## Phase 1: Analyze Current Prompt ### Objective Identify weaknesses and improvement opportunities ### Agent: Researcher **Step 1.1: Structural Analysis** ```javascript const promptAnalysis = { components: { hasSystemContext: checkForContext(prompt), hasRoleDefinition: checkForRole(prompt), hasTaskDescription: checkForTask(prompt),