Agent-Skills.md

Agent Skills: Evaluator-Optimizer

Iterative refinement workflow for polishing code, documentation, or designs through systematic evaluation and improvement cycles. Use when refining drafts into production-grade quality.

UncategorizedID: NickCrew/claude-cortex/evaluator-optimizer

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NickCrew/claude-cortex
NickCrewLicense: MIT
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pnpm dlx add-skill https://github.com/NickCrew/claude-cortex/tree/HEAD/skills/evaluator-optimizer

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skills/evaluator-optimizer/SKILL.md

Skill Metadata

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):

  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.