lidge-jun/agent-eval

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

# pinned to v0.1.0 — latest stable commit
pip install git+https://github.com/joaquinhuigomez/agent-eval.git@6d062a2f5cda6ea443bf5d458d361892c04e749b

Core Concepts

YAML Task Definitions

Define tasks declaratively. Each task specifies what to do, which files to touch, and how to judge success:

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:

mkdir tasks
# Write task definitions (see template above)

2. Run Agents

Execute agents against your tasks:

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:

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)

judge:
  - type: pytest
    command: pytest tests/ -v
  - type: command
    command: npm run build

Pattern-Based

judge:
  - type: grep
    pattern: "class.*Retry"
    files: src/**/*.py

Model-Based (LLM-as-judge)

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