Why should I run multiple trials per agent?

Since AI models are non-deterministic, running multiple trials (e.g., 3-5) per task is necessary to capture variance and calculate an accurate consistency score.

Can I use my own unit tests to judge success?

Yes, agent-eval supports multiple judge types including deterministic tests (like pytest or npm test), grep-based pattern matching, and LLM-based evaluation.

What metrics does this skill track?

The skill measures the pass rate (success), API cost per task, wall-clock completion time, and consistency across multiple repeated trials.

How does agent-eval isolate different agent runs?

It uses Git worktrees to create fresh, isolated environments for every run, ensuring agents do not interfere with each other or the base repository without the overhead of Docker.

Coding Agent Evaluator

Name: Coding Agent Evaluator
Author: affaan-m

byaffaan-m

•

172,007

•

Security & Testing

Compares and benchmarks AI coding agents like Claude Code and Aider using reproducible tasks and performance metrics.

The agent-eval skill provides a systematic framework for benchmarking AI coding agents on custom codebases and tasks. By utilizing Git worktrees for isolation and YAML-based task definitions, it moves beyond subjective comparisons to offer hard data on pass rates, API costs, execution time, and consistency. This tool is essential for developers and teams looking to validate agent performance before adoption, track regressions after model updates, or select the most cost-effective AI tool for specific programming workflows.

Key Features

01Git worktree isolation for clean and reproducible test environments

02172,007 GitHub stars

03Detailed performance metrics including pass rate, cost, and wall-clock time

04Declarative YAML task definitions for standardized benchmarking

05Head-to-head comparison of agents including Claude Code, Aider, and Codex

06Multi-modal judging using unit tests, grep patterns, and LLM-as-a-judge

Use Cases

01Determining the most cost-effective coding agent for specific team workflows

02Benchmarking different LLM models and coding agents on proprietary codebases

03Regression testing agent performance after a tool or model update

Key Features

01Git worktree isolation for clean and reproducible test environments

02172,007 GitHub stars

03Detailed performance metrics including pass rate, cost, and wall-clock time

04Declarative YAML task definitions for standardized benchmarking

05Head-to-head comparison of agents including Claude Code, Aider, and Codex

06Multi-modal judging using unit tests, grep patterns, and LLM-as-a-judge

Use Cases

01Determining the most cost-effective coding agent for specific team workflows

02Benchmarking different LLM models and coding agents on proprietary codebases

03Regression testing agent performance after a tool or model update