codex-evaluation

Name: codex-evaluation
Availability: InStock
Author: luckyegg168

Automate eval-driven improvement loops for Codex tasks with deterministic scoring.

Updated Mar 29, 2026

Search Tags:#automation #codex #evaluation #prompt-engineering #llm-judge #quality-improvement #eval-loop

Ready to run

npx skills add https://github.com/luckyegg168/codex-use-cases --skill codex-evaluation

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: codex-evaluation
Source: https://github.com/luckyegg168/codex-use-cases/tree/main/skills/codex-evaluation
Command: npx skills add https://github.com/luckyegg168/codex-use-cases --skill codex-evaluation

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Codex 的評估與改進循環需要穩定的流程與可重現的評分標準。本技能提供基於 eval-driven improvement loop 的結構化工作流，結合確定性評分與 LLM 作為 judge 的評估組件，協助自動化迭代改進。

Core Features & Use Cases

Eval-driven improvement loop: deterministic scoring + LLM-based evaluation for continuous quality enhancement across Codex tasks.
Baseline to convergence workflow: supports baseline eval, iterative improvements, and final reporting with logs and guardrails.
Use Case coverage: iterate on difficult problems, measure progress, and produce concrete improvement records.

Quick Start

Run a baseline evaluation, locate the eval script, and log the initial scores to iteration_log.md to begin the improvement loop.

Frequently Asked Questions about codex-evaluation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I set up an eval-driven improvement loop for Codex workflows?▼

An eval-driven improvement loop automates iterative quality enhancement for Codex tasks by combining deterministic scoring with LLM-as-judge evaluation. You run a baseline evaluation, log initial scores to iteration_log.md, and apply structured multi-dimension scoring to track progress.

What is LLM-as-judge evaluation and how does it score prompt quality?▼

LLM-as-judge evaluation uses a language model to assess prompt outputs across multiple dimensions, providing structured scoring alongside deterministic metrics. This combination enables reproducible quality measurement and continuous improvement for Codex-guided tasks.

Can I use deterministic scoring with LLM-based evaluation for debugging tasks?▼

Yes, deterministic scoring combined with LLM-based evaluation supports debugging, optimization, and validation scenarios within Codex-driven projects. The structured workflow applies guardrails and convergence criteria to ensure safe, reproducible improvements across iterative debugging cycles.

What's the best way to measure progress when iterating on difficult Codex problems?▼

The best way to measure progress is using a structured eval-driven loop with baseline evaluation, multi-dimension scoring, and iteration logging. This produces concrete improvement records and applies convergence criteria to determine when optimization reaches acceptable quality thresholds.

Do I need guardrails for safe reproducible improvements in automated evaluation loops?▼

Yes, guardrails are required for safe, reproducible improvements in automated evaluation loops. The workflow enforces convergence criteria and iteration logging to prevent unbounded changes, ensuring that each scoring iteration produces validated, measurable quality enhancements.