Evaluator-Optimizer

Iterative refinement workflow that takes existing code, documentation, or designs and polishes them through rigorous cycles of evaluation and improvement until they meet production-grade quality standards.

When to Use This Skill

• Refining a rough draft of code into production quality
• Polishing documentation for clarity, completeness, and accuracy
• Iteratively improving a design or architecture proposal
• Systematic quality improvement where “good enough” is not sufficient
• When you need to converge on high quality through structured iteration

Quick Reference

Workflow: The Loop

For any given artifact (code, text, design):

1. Accept: Take the current version of the artifact.
2. Evaluate: Act as a harsh critic. Rate the artifact on correctness, clarity, efficiency, style, and safety. Assign a score out of 100.
3. Decide:
   • Score >= 90: Stop and present the result.
   • Score < 90: Refine.
4. Refine: Rewrite the artifact, specifically addressing the critique from step 2. List what changed and why.
5. Repeat: Return to step 2 with the new version.

Behavioral Rules

• Do not settle: “Good enough” is not good enough. You are here to polish.
• Be explicit: When evaluating, list specific flaws. “The function process_data is O(n^2) but could be O(n).”
• Show your work: Summarize changes in each iteration.
• Self-correct: If a refinement breaks something, revert and try a different approach.
• Converge: Each iteration must improve the score. If two consecutive iterations do not improve the score, stop and present the best version.

Iteration Output Template

## Iteration [N] Evaluation

| Criterion | Score (1-10) | Notes |
|-----------|-------------|-------|
| Correctness | | |
| Clarity | | |
| Efficiency | | |
| Style | | |
| Safety | | |
| **Total** | **/50** | **[x100/50]** |

### Issues Found
1. [Specific issue with location]
2. [Specific issue with location]

### Refinements Applied
- [Change 1 and rationale]
- [Change 2 and rationale]

Example Interaction

Input: “Refine this Python script.”

Iteration 1 Evaluation:

• Functionality: Good
• Efficiency: Poor – uses nested loops for matching
• Style: Variable names a and b are unclear
• Score: 60/100

Refinements applied:

• Flattened loops using a set lookup (O(n))
• Renamed a to users, b to active_ids
• Added type hints

Iteration 2 Evaluation:

• Functionality: Good
• Efficiency: Excellent
• Style: Good
• Score: 95/100

Result: Present the refined script.