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.

ID	Pattern	Research Basis
BP-001	Negative Instructions	Attention focuses on forbidden content, increasing violation probability. Inverse scaling confirmed
BP-002	Vague Instructions	Primary failure cause. 40% of performance variance
BP-003	Missing Output Format	Directly linked to hallucination reduction

P2: High Impact (Should Fix)

Consistent improvement when addressed.

ID	Pattern	Research Basis
BP-004	Unstructured Prompt	"Structure > Length" confirmed
BP-005	Missing Context	"More context = higher accuracy" confirmed
BP-006	Complex Task Without Decomposition	ICLR 2023: 28% error reduction with decomposition

P3: Enhancement (Could Fix)

Incremental improvements in specific contexts.

ID	Pattern	Research Basis
BP-007	Biased Examples	40% of few-shot effectiveness depends on exemplar selection
BP-008	No Uncertainty Permission	Allowing "I don't know" reduces hallucination

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:

• Classify each improvement as Structural, Context Addition, Expressive, or Variance (see Improvement Classification below). Apply only Structural and Context Addition changes.
• 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 (prompt length increase ≤50%, no constraints that limit valid solutions unnecessarily — see references/execution-quality.yaml over_optimization) Output: Final optimized prompt. Clean up temporary files (.claude/.rashomon/step1-*.md, step2-*.md) 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

Classification	Definition	Interpretation
Structural	Prompt structure, clarity, specificity improvements	Prompt writing technique
Context Addition	Project-specific information added from codebase investigation	Information advantage
Expressive	Different phrasing, equivalent substance	Neutral
Variance	Within LLM probabilistic variance	Original prompt sufficient

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
• references/skills.md - Skill-specific optimization (BP adaptation, 9 editing principles, progressive disclosure, grading)