prompt-optimization
Analyzes and optimizes prompts using BP-001~008 patterns and 3-step flow (detect, optimize, balance). Use when "optimize this prompt", "review prompt quality", "analyze prompt issues", or creating/reviewing rashomon skill content.
What this skill does
# 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)
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