agent-eval

Compare coding agents on reproducible tasks using standardized metrics.

Updated Mar 20, 2026

Search Tags:#workflow #comparison #coding #benchmark #reproducible #agent-eval

Ready to run

npx skills add https://github.com/KanakMalpani/General-Private-Skills --skill agent-eval-kanakmalpani

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agent-eval
Source: https://github.com/KanakMalpani/General-Private-Skills/tree/main/skills/agent-eval
Command: npx skills add https://github.com/KanakMalpani/General-Private-Skills --skill agent-eval-kanakmalpani

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Quickly identify the best coding assistant or model for your projects by providing objective, reproducible comparisons across agents.

Core Features & Use Cases

Head-to-head comparisons with metrics like pass rate, cost, time, and consistency.
Reproducible experiments using git worktrees to isolate runs and avoid environment drift.
Use cases include tool selection, regression checks after model updates, and benchmarking for team decisions.

Quick Start

Run agent-eval with a set of tasks and multiple agents to produce a structured performance 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 with reproducible trials?▼

To benchmark coding agents with reproducible trials, you run head-to-head comparisons on a defined task set using git worktrees for environment isolation. This yields a structured report with metrics like pass rate, cost, and time.

What is the best way to compare coding assistants on standardized tasks?▼

Comparing coding assistants on standardized tasks requires a YAML task format and automated evaluation. This approach generates objective comparison results using metrics like pass rate, cost, time, and consistency across multiple agents.

How do I run head-to-head coding agent comparisons to identify the best model?▼

You run head-to-head coding agent comparisons by evaluating multiple agents on a defined set of tasks with standardized judges. The process produces a structured performance report highlighting pass rate, cost, time, and consistency.

Does agent-eval support regression checks after model updates?▼

Yes, agent-eval supports regression checks after model updates by running reproducible experiments. Using git worktrees to isolate runs prevents environment drift, ensuring consistent and reliable evaluation results for tool selection.

Do I need a YAML task format to evaluate multiple coding agents?▼

Yes, a YAML task format is required to define the tasks for evaluating multiple coding agents. This structured input enables standardized judges to accurately measure pass rate, cost, time, and consistency across reproducible runs.