prompt-optimizer-en

Iterative prompt optimizer for complex tasks. Strictly implements ACON's two-stage iterative optimization + APE automatic prompt engineering. Only triggers w...

Security Scan

The skill's declared purpose (iterative prompt optimization) matches its runtime instructions and it requests no credentials or installs; nothing in the bundle suggests hidden or unrelated access.

Purpose & Capability

The name/description (prompt optimizer implementing ACON/APE) align with the SKILL.md: all operations are prompt parsing, candidate generation, scoring, compression, and iterative refinement. There are no extra credentials, binaries, or config paths requested that would be unrelated to prompt optimization.

Instruction Scope

Instructions stay within the stated purpose and explicitly forbid auto-triggering and extra analysis. One operational ambiguity: several validation steps require the agent to judge 'functional equivalence' and 'effectiveness' which are subjective and grant the agent discretion in deciding when a change is acceptable. This is expected for a prompt-optimization skill but means outputs should be reviewed by the user rather than assumed correct.

Install Mechanism

Instruction-only skill with no install spec and no code files; nothing is written to disk or downloaded by the skill itself.

Credentials

No environment variables, credentials, or config paths are requested. The skill does not request unrelated secrets or cloud credentials.

Persistence & Privilege

always is false and user-invocable only. The skill does not request persistent presence or modification of other skills or agent-wide settings.

Assessment

This skill appears coherent and does only prompt transformation work, but remember: (1) it will see the full prompt you give it, so do not include secrets or sensitive data in prompts; (2) its validations (functional equivalence, effectiveness scoring) are subjective — review optimized prompts before using them in high-stakes contexts; and (3) because it rewrites prompts automatically when you request iterations, keep a copy of your original prompt if you need to revert or audit changes.

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

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🚀 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

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📦 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

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📤 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.
   

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🔄 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

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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