사용 방법
- 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
- 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
- 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/affaan-m/ecc --skill agent-eval시작 전 확인
- 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
- 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서
원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.
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 (Claude Code, 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 reproducibilityGit Worktree Isolation
Each agent run gets its own git worktree — no Docker required. This provides reproducibility isolation so agents cannot interfere with each other or corrupt the base repo.
Metrics Collected
| Metric | What It Measures |
|---|---|
| Pass rate | Did the agent produce code that passes the judge? |
| Cost | API spend per task (when available) |
| Time | Wall-clock seconds to completion |
| Consistency | Pass rate across repeated runs (e.g., 3/3 = 100%) |
Workflow
1. Define Tasks
Create a tasks/ directory with YAML files, one per task:
bash
mkdir tasks
# Write task definitions (see template above)2. Run Agents
Execute agents against your tasks:
bash
agent-eval run --task tasks/add-retry-logic.yaml --agent claude-code --agent aider --runs 3Each run:
- Creates a fresh git worktree from the specified commit
- Hands the prompt to the agent
- Runs the judge criteria
- Records pass/fail, cost, and time
3. Compare Results
Generate a comparison report:
bash
agent-eval report --format tableTask: add-retry-logic (3 runs each)
┌──────────────┬───────────┬────────┬────────┬─────────────┐
│ Agent │ Pass Rate │ Cost │ Time │ Consistency │
├──────────────┼───────────┼────────┼────────┼─────────────┤
│ claude-code │ 3/3 │ $0.12 │ 45s │ 100% │
│ aider │ 2/3 │ $0.08 │ 38s │ 67% │
└──────────────┴───────────┴────────┴────────┴─────────────┘Judge Types
Code-Based (deterministic)
yaml
judge:
- type: pytest
command: pytest tests/ -v
- type: command
command: npm run buildPattern-Based
yaml
judge:
- type: grep
pattern: "class.*Retry"
files: src/**/*.pyModel-Based (LLM-as-judge)
yaml
judge:
- type: llm
prompt: |
Does this implementation correctly handle exponential backoff?
Check for: max retries, increasing delays, jitter.Best Practices
- Start with 3-5 tasks that represent your real workload, not toy examples
- Run at least 3 trials per agent to capture variance — agents are non-deterministic
- Pin the commit in your task YAML so results are reproducible across days/weeks
- Include at least one deterministic judge (tests, build) per task — LLM judges add noise
- Track cost alongside pass rate — a 95% agent at 10x the cost may not be the right choice
- Version your task definitions — they are test fixtures, treat them as code
Links
- Repository: github.com/joaquinhuigomez/agent-eval
