nickcrew/evaluator-optimizer
skills/evaluator-optimizer/SKILL.md
Iterative refinement workflow for polishing code, documentation, or designs through systematic evaluation and improvement cycles. Use when refining drafts into production-grade quality.
$ install --global
skillsauthnpx skillsauth add nickcrew/claude-cortex evaluator-optimizerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- evaluator-optimizer
- description:
- Iterative refinement workflow for polishing code, documentation, or designs through systematic evaluation and improvement cycles. Use when refining drafts into production-grade quality.
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
| Task | Load reference |
| --- | --- |
| Evaluation criteria and quality rubrics | skills/evaluator-optimizer/references/evaluation-criteria.md |
Workflow: The Loop
For any given artifact (code, text, design):
- Accept: Take the current version of the artifact.
- Evaluate: Act as a harsh critic. Rate the artifact on correctness, clarity, efficiency, style, and safety. Assign a score out of 100.
- Decide:
- Score >= 90: Stop and present the result.
- Score < 90: Refine.
- Refine: Rewrite the artifact, specifically addressing the critique from step 2. List what changed and why.
- 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_datais 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
aandbare unclear - Score: 60/100
Refinements applied:
- Flattened loops using a set lookup (O(n))
- Renamed
atousers,btoactive_ids - Added type hints
Iteration 2 Evaluation:
- Functionality: Good
- Efficiency: Excellent
- Style: Good
- Score: 95/100
Result: Present the refined script.
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