Prompt Optimization Skill
Core Philosophy
- •Model-Agnostic: Patterns effective across GPT, Claude, Gemini, etc.
- •Evidence-Based: Based on peer-reviewed research and industry consensus
- •Actionable: Each detection provides specific, implementable improvements
- •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.
| ID | Pattern | Research Basis |
|---|---|---|
| BP-001 | Negative Instructions | Attention mechanism structural issue. 75% failure rate in ArXiv studies |
| 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:
- •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
| 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