---

name: prompt-optimization description: Model-agnostic prompt analysis and optimization patterns based on 2025-2026 research. Use when analyzing prompts for issues or generating optimized versions. Provides 8 patterns (BP-001 through BP-008) and 3-step optimization flow.

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

Pattern Detection

P1: Critical (Must Fix)

High confidence research evidence for negative impact.

P2: High Impact (Should Fix)

Consistent improvement when addressed.

P3: Enhancement (Could Fix)

Incremental improvements in specific contexts.

3-Step Optimization Flow

Step 1: Initial Analysis

Input: Target prompt Process: Detect patterns (BP-001 through BP-008) Output: .claude/.rashomon/step1-analysis.md

Contents:

• Detected issues by severity
• Location in prompt
• Original prompt preserved

Step 2: Optimization

Input: Step 1 analysis Process:

• Evaluate precision contribution
• Consolidate redundant improvements
• Apply in priority order (P1 > P2 > P3) Output: .claude/.rashomon/step2-optimized.md

Contents:

• Before/after for each change
• Rationale
• Optimized prompt

Step 3: Balance Adjustment

Input: Step 2 output Process:

• Reference references/execution-quality.yaml
• Confirm all critical aspects are preserved
• Confirm constraints are proportionate Output: Final optimized prompt

CRITICAL: Clean up temporary files after completion.

Conditional Application

BP-004 (Unstructured)

Apply 4-block pattern IF:

• Prompt longer than 3 sentences
• Contains multiple distinct instructions
• Has implicit section boundaries

Skip when:

• Single simple instruction
• Already clearly structured
• Structure would add unnecessary verbosity

BP-006 (Decomposition)

Decompose IF:

• 3+ distinct objectives
• Sequential dependencies
• Each step can be quality-checked

Key Insight: Goal is EVALUABLE GRANULARITY with QUALITY CHECKPOINTS, not decomposition itself.

Improvement Classification

Principle: Distinguish between prompt writing improvements (Structural) and information additions (Context Addition).

Reference: references/execution-quality.yaml for detailed criteria.

References

• references/patterns.yaml - Detailed pattern definitions
• references/execution-quality.yaml - Quality evaluation criteria