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evaluator-optimizer

evaluator-optimizer

github.com/nickcrewAgent workflows6269 installsUpdated 17w ago
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About evaluator-optimizer

evaluator-optimizer is a skill for iteratively improving existing code, documentation, and designs. It guides agents through scored evaluation and targeted revisions until the work meets its stopping criteria.

Key features

  1. 01Evaluation across five quality criteria
  2. 02Score-based stop or refine decisions
  3. 03Targeted revisions with change summaries
  4. 04Loop for checking refinements

Use cases

  1. 01Polishing a rough code draft
  2. 02Improving documentation clarity and accuracy
  3. 03Revising a design or architecture proposal

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/nickcrew/claude-cortex --skill evaluator-optimizer

After installing, type/evaluator-optimizerin Claude Code to confirm it loaded.

Bundled filesreferences/evaluation-criteria.md

Details

From the project README

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

TaskLoad reference
Evaluation criteria and quality rubricsskills/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.

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