agent-eval
🤖 AI Summary
**agent-eval** is a CLI tool that runs head-to-head comparisons of coding agents (e.g., Claude Code, Aider) on reproducible YAML-defined tasks, scoring each agent's output against success criteria to produce data-backed performance metrics for tool selection and regression testing.
How to Install
Claude Code:
git clone --depth 1 https://github.com/affaan-m/ECC.git && cp ECC/skills/agent-eval ~/.claude/skills/agent-eval -r# 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 reproducibility
```
### Git 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 3
```
Each run:
1. Creates a fresh git worktree from the specified commit
2. Hands the prompt to the agent
3. Runs the judge criteria
4. Records pass/fail, cost, and time
### 3. Compare Results
Generate a comparison report:
```bash
agent-eval report --format table
```
```
Task: 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 build
```
### Pattern-Based
```yaml
judge:
- type: grep
pattern: "class.*Retry"
fil
Details
| Category | AI/ML → ml |
| Source | affaan-m/ECC |
| SKILL.md | View on GitHub → |
| Repo Stars | ★ 220.5K |
| Est. per Skill | N/A (shared across 121 skills from this repo) |
| Difficulty | Intermediate |
| Risk Level | N/A |
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