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.