agent-eval
Head-to-head comparison of coding agents (OpenAI Codex, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics
npx skills add mturac/everything-openai-codex --skill agent-eval --agent codex
Same command for any agent — swap --agent for claude-code, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Agent Eval Skill A lightweight CLI tool for comparing coding agents head-to-head on reproducible tasks. Every "which coding agent is best?" comparison runs on vibes — this tool systematizes it. ## When to Activate - Comparing coding agents (OpenAI Codex, Aider, Codex, etc.) on your own codebase - Measuring agent performance before adopting a new tool or model - Running regression checks when an agent updates its model or tooling - Producing data-backed agent selection decisions for a team ## Installation > **Note:** Install agent-eval from its repository after reviewing the source. ## Core Concepts ### YAML Task Definitions Define tasks declaratively. Each task specifies what to do, which files to touch, and how to judge success: ```yaml name: add-retry-logic description: Add exponential backoff retry to the HTTP client repo: ./my-project files: - src/http_client.py prompt: | Add retry logic with exponential backoff to all HTTP requests. Max 3 retries. Initial delay 1s, max delay 30s. judge: - type: pytest command: pytest tests/test_http_client.py -v - type: grep pattern: "exponential_backoff|retry" files: src/http_client.py commit: "abc1234" # pin to specific commit for reproduc
- When to Activate
- Installation
- Core Concepts
- YAML Task Definitions
- Git Worktree Isolation
- Metrics Collected
- Workflow
- 1. Define Tasks
- 2. Run Agents
- 3. Compare Results
- Judge Types
- Code-Based (deterministic)
- Pattern-Based
- Model-Based (LLM-as-judge)
mkdir tasks Write task definitions (see template above) agent-eval run --task tasks/add-retry-logic.yaml --agent codex --agent aider --runs 3 agent-eval report --format table
What does the agent-eval skill do?
Head-to-head comparison of coding agents (OpenAI Codex, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics
How do I install it?
Run `npx skills add mturac/everything-openai-codex --skill agent-eval --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From mturac/everything-openai-codex, a repository with 84 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.
