agent-eval
An experimental framework for testing and evaluating the performance of AI coding agents on your framework.
Install & Use
Copy this prompt and send it to your AI assistant (Claude / Cursor / TRAE / Codex / WorkBuddy etc.) to auto-install:
Help me install this AI Skill: agent-eval. It is used for: An experimental framework for testing and evaluating the performance of AI coding agents on your framework. Full Skill content: https://321skill.com/skills/agent-eval/raw/index.md Read that page and install it.
The prompt includes a link to the full Skill content. You can also view the full content.
When developing a frontend framework, how do you ensure AI agents can use it effectively? @vercel/agent-eval is built to solve this problem. It allows you to create controlled experiments to quantify an agent's success rate and performance on your framework, enabling scientific evaluation of documentation quality, API changes, or the impact of different models.
To use it, simply initialize a project via a straightforward CLI command, create specific evaluation tasks (Evals), and then run the experiments. Each evaluation task includes a prompt for the agent (PROMPT.md) and a set of tests (EVAL.ts) to verify success or failure. The framework automatically runs experiments, collects results, and supports result reuse, helping you save costs.
This tool is ideal for framework developers, developer advocates, or any team needing to systematically verify the compatibility of AI agents with their tech stack. It helps you move from vague guesses to data-driven decisions—for example, validating whether a newly released MCP server actually improves an agent's code generation accuracy.
It's recommended to start with the --dry and --smoke flags to preview and quickly validate setups, avoiding unnecessary API call costs. Also, make full use of its fingerprint caching mechanism to reuse existing results when iterating on experiment designs, boosting efficiency.
Key Features
Unlike generic AI agent testing tools, it is deeply integrated with frontend framework evaluation scenarios. It allows for precise verification of whether the code generated by an agent adheres to framework specifications and can be successfully built by writing specific test code (EVAL.ts), providing a complete closed-loop evaluation solution from prompt to validation.
Limitations
Primarily focused on evaluating AI agents' coding capabilities within specific frameworks. It is not suitable for testing agents on general conversation, creative writing, or other non-code-generation tasks.
FAQ
Which AI models can this tool test?
In theory, it can test any coding agent accessible via API calls, such as Claude, GPT, etc., depending on your experiment configuration.
Is running experiments expensive?
Cost depends on the number of experiments you run, the models called, and whether result caching is enabled. Using `--dry` for previews and fingerprint cache reuse can significantly control costs.
Installation guide for AI assistants
If your AI coding assistant (Claude Code, Cursor, TRAE etc.) can see this page, send it this message to auto-install:
Visit https://321skill.com/skills/agent-eval/raw/index.md to read the original Skill definition (Markdown format) for agent-eval, and install it according to the instructions.
Raw Markdown URL for AI: /skills/agent-eval/raw/index.md