agent-eval

Automate head-to-head evaluation of coding agents on reproducible YAML-defined tasks.

Updated Mar 21, 2026
One-click install
npx skills add https://github.com/hieuck/Pro5ChromeManager --skill agent-eval-hieuck
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: agent-eval
Source: https://github.com/hieuck/Pro5ChromeManager/tree/main/skills/agent-eval
Command: npx skills add https://github.com/hieuck/Pro5ChromeManager --skill agent-eval-hieuck

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This CLI tool standardizes and speeds up the comparison of coding agents by running reproducible tasks, collecting metrics, and producing data-driven reports.

Core Features & Use Cases

  • Declarative YAML task definitions
  • Git worktree isolation for reproducibility
  • Metrics: pass rate, cost, time, consistency
  • Generate comparison reports
  • Use Case: Evaluate whether Claude Code, Aider, or Codex best fits a codebase

Quick Start

Define tasks in tasks/ and run the tool to compare agents across those tasks, then review the generated report.

Frequently Asked Questions about agent-eval

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

FAQPage Schema
How do I benchmark coding agents head-to-head on my codebase?

Benchmark coding agents head-to-head by defining reproducible tasks in YAML and running the CLI tool to generate data-driven comparison reports. It measures pass rate, cost, time, and consistency across agents.

What is git worktree isolation for AI evaluation?

Git worktree isolation for AI evaluation ensures reproducible agent benchmarking by providing each task with a separate, clean working directory. This prevents state leakage and ensures consistent metric collection across runs.

Can I compare Claude Code, Aider, and Codex using declarative YAML task definitions?

Yes, you can compare Claude Code, Aider, and Codex using declarative YAML task definitions. The tool runs these reproducible tasks and generates a comparison report showing which toolchain best fits your project.

What metrics are collected when comparing AI coding assistants?

Metrics collected when comparing AI coding assistants include pass rate, cost, time, and consistency. These measurable performance data points help teams evaluate model updates and new toolchains.

How do I evaluate whether a model update improves coding agent performance?

Evaluate whether a model update improves coding agent performance by running standardized, reproducible tasks with per-task judges. The tool outputs measurable data to compare the updated model against previous versions.

Do I need to install dependencies to run reproducible coding agent benchmarks?

No external dependencies are required to run reproducible coding agent benchmarks. You simply define your tasks in YAML files and execute the CLI tool to review the generated comparison report.