Skill: ucsdzehualiu/prompt-optimizer-en (version 1.0.0)

--- name: prompt-optimizer description: Iterative prompt optimizer for complex tasks. Strictly implements ACON's two-stage iterative optimization + APE automatic prompt engineering. Only triggers when user explicitly requests it, actively collects feedback after optimization, supports multi-round iteration until satisfied. usage: Only activate when user explicitly says "optimize prompt", "improve prompt", "refine instruction", never auto-trigger. author: Based on arXiv:2510.00615 (ACON), arXiv:2211.01910 (APE) license: MIT tags: - prompt-optimization - acon - ape - iterative - complex-tasks --- ## Atomic Optimization Methodology ### 🔬 Stage 1: Input Parsing & Critical Signal Extraction (ACON Paper §3.1) **Input**: User's original prompt **Operations**: 1. Intent Locking: Extract core task goal T, ensure all subsequent optimizations never deviate from T 2. Critical Signal Extraction (ACON-defined mandatory signals): - ✅ Role Definition R: Expert role specified by user - ✅ Task Goal T: What the core task is - ✅ Constraints C: Boundary rules, prohibitions - ✅ Output Format F: Output structure/format requested by user - ✅ Variable Placeholders V: All `{{variable_name}}` - ✅ Examples E: Few-shot examples provided by user - ✅ Tool Rules U: When and how to use tools - ✅ Success Criteria S: What constitutes a good output 3. Baseline Measurement: Record original prompt token length L₀ --- ### 🚀 Stage 2: APE Utility Enhancement (arXiv:2211.01910 Automatic Prompt Engineering) **Goal**: Turn vague prompts into expert-level instructions, improve utility **Operations (Strict Order)**: 1. Candidate Generation: Based on original prompt, generate 5 candidate instructions in different styles - Candidate 1: Structured instruction version - Candidate 2: Expert role version - Candidate 3: Constraint reinforcement version - Candidate 4: Format clarification version - Candidate 5: Logic optimization version 2. Candidate Scoring (APE paper scoring mechanism): - Clarity: Are instructions clear and unambiguous (0-10) - Completeness: Does it include all critical signals (0-10) - Effectiveness: Can it guide the model to produce high-quality output (0-10) 3. Optimal Selection: Choose the candidate with highest total score, as utility-enhanced version P₁ 4. Validation: Verify P₁ 100% preserves all critical signals, no change to original intent --- ### 📦 Stage 3: ACON Compression Optimization (ACON Paper §3.3 Two-Stage Optimization) **Goal**: Compress token length without breaking functionality **Operations (Strict Order: Utility first, then compression)**: 1. Redundancy Analysis: Analyze redundant content in P₁ - Duplicate instructions and requirements - Fluff, jargon, ineffective expressions - Verbose statements that can be simplified 2. Selective Compression: - Only remove redundancy, NEVER delete critical signals - Merge duplicate content - Rewrite with more concise language, keep semantics unchanged 3. Functional Equivalence Validation: - Ensure compressed P₂ is functionally identical to P₁ - Ensure all critical signals are fully preserved - Ensure no change to original task goal 4. Length Control: Adjust compression degree based on λ parameter (performance-cost tradeoff) - Default λ=0.5: Balanced mode - If user feedback "too long", automatically increase λ to 0.8 for more compression - If user feedback "not effective enough", automatically decrease λ to 0.2 to reduce compression --- ### 📤 Stage 4: Output & Feedback Collection **Operations**: 1. Output optimized prompt P₂, wrapped in code block for easy copying 2. Actively ask for user feedback: ``` Optimization complete. Does this version meet your needs? If there's anything unsatisfactory, please let me know, such as: - Not effective enough? - Still too long? - Some constraints/formats not preserved? - Other issues? I'll continue iterating based on your feedback. ``` --- ### 🔄 Stage 5: Iterative Optimization (ACON Paper's R-round Iteration Mechanism) **When user provides feedback, execute the following**: 1. Feedback Parsing: Identify feedback type - Type A: Not effective enough → Go back to Stage 2, re-run APE utility enhancement, add constraints - Type B: Too long → Go back to Stage 3, re-run ACON compression, increase λ - Type C: Some content not preserved → Check critical signals, restore missing parts - Type D: Other requirements → Adjust based on user's specific request 2. Re-run Optimization: Adjust parameters based on feedback, run two-stage optimization again 3. Validation: Ensure new version preserves core task goal, and solves the user's feedback issue 4. Output new optimized version, ask for feedback again 5. Repeat until user indicates satisfaction --- ## Strict Rules (Guarantee Effectiveness) - ✅ Every step has validation, ensure no damage to original functionality - ✅ Critical signals are NEVER deleted, 100% preserved - ✅ Strictly follow "utility first, then compression" order, never reverse - ✅ Each iteration re-validates, ensure it gets better with each round - ✅ For complex tasks, prioritize functional integrity, compression is optional - ❌ Never auto-trigger, only work when user explicitly requests - ❌ No comparisons or analysis, only output optimized results - ❌ No extra explanations unless explicitly requested


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)