Score and benchmark AI agent outputs using multi-rubric evaluation.
npx skills add https://github.com/x-mesh/x-kit --skill x-eval
Please help me install this Agent Skill. Skill: x-eval Source: https://github.com/x-mesh/x-kit/tree/main/x-eval/skills/eval Command: npx skills add https://github.com/x-mesh/x-kit --skill x-eval
This Skill provides a comprehensive solution for evaluating the quality of AI agent outputs, enabling users to score, compare, benchmark, and calibrate agent performance.
To evaluate the quality of an AI agent's output, use the x-eval skill and specify the content and rubric for evaluation. For example, to score a code snippet using the 'code-quality' rubric, you would say: /xm:eval score "function add(a,b){return a+b}" --rubric code-quality
High-intent search queries and answers about installing and using this skill.
To evaluate AI agent output quality, you can use multi-rubric scoring by specifying the content and a predefined or custom rubric. This scores agent outputs against specific criteria to ensure quality and consistency.
Benchmarking AI agent strategies involves comparing different models or prompt strategies on the same task. This process analyzes performance differences by scoring outputs using predefined rubrics and measuring consistency across trials.
Yes, you can conduct A/B testing with prompt variations to assess their impact on AI agent output quality. This involves comparing two outputs to determine which prompt variation produces better results.
Yes, configuration files are required for mode detection and judge composition. These files enable the system to validate AI judge scores against human judgment, mitigating biases and ensuring output consistency across repeated trials.
Multi-rubric scoring may not be suitable if you lack defined evaluation criteria or configuration files for judge composition. Without custom rubrics or calibration data, the scoring system cannot accurately measure agent performance or mitigate biases.
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