agent-skills-eval
A test runner for Agent Skills that quantifies and evaluates skill effectiveness.
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-skills-eval. It is used for: A test runner for Agent Skills that quantifies and evaluates skill effectiveness. Full Skill content: https://321skill.com/skills/agent-skills-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.
In the Agent Skills ecosystem, after writing a SKILL.md file for a skill, how do you prove it actually improves the model's performance on a specific task? agent-skills-eval addresses this core pain point. It is a standalone testing framework specifically designed to evaluate the real-world effectiveness of Agent Skills.
Usage is straightforward. Via a CLI command, you specify the folder containing the skill, a target model to be tested, and a judge model for scoring. It then automatically runs the evaluation. The core process is: for the same set of test prompts, it runs two tests—one 'with the skill loaded' and one 'without the skill'—then the judge model scores and compares the two outputs.
This tool is ideal for all developers, product managers, and researchers who create and use Agent Skills. Whether an agent developer wants to verify the effectiveness of a self-developed skill, or a team lead needs to assess the quality of a third-party skill, they can use it to obtain data-driven decision-making evidence.
It is recommended to introduce this tool early in skill development to establish benchmarks. Note that the objectivity of the evaluation results depends heavily on the choice of the judge model and the design quality of the test cases (evals). Therefore, careful design of the test set and selection of an appropriate judge model are required.
Key Features
Unlike simple functional testing tools, it employs an A/B comparison test between 'with skill' and 'without skill' to directly quantify the performance improvement brought by the skill. It provides an objective report scored by a judge model, rather than relying on subjective perception.
Limitations
The accuracy of the evaluation results depends on the judgment capability of the judge model and the design quality of the test cases. It is not suitable for assessing a skill's performance in untested new domains or edge cases.
FAQ
What types of skills can agent-skills-eval test?
It can evaluate the effectiveness of any skill written according to the Agent Skills open standard (agentskills.io).
Must I use OpenAI's models?
No. While it defaults to OpenAI API compatibility, through an adaptation layer, it also supports any service compatible with the OpenAI chat interface, such as Anthropic, Groq, or local models.
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-skills-eval/raw/index.md to read the original Skill definition (Markdown format) for agent-skills-eval, and install it according to the instructions.
Raw Markdown URL for AI: /skills/agent-skills-eval/raw/index.md