Evaluate AI agent performance using multi-dimensional rubrics and LLM-as-judge.
npx skills add https://github.com/rustams/inbound --skill evaluation-rustams
Please help me install this Agent Skill. Skill: evaluation Source: https://github.com/rustams/inbound/tree/main/.cursor/skills/evaluation Command: npx skills add https://github.com/rustams/inbound --skill evaluation-rustams
This Skill addresses the challenge of systematically testing and validating the performance and quality of AI agents, ensuring reliability and continuous improvement.
Use the evaluation skill to build a test framework for agent performance.
High-intent search queries and answers about installing and using this skill.
To evaluate AI agent performance systematically, you can use a framework with multi-dimensional rubrics and LLM-as-judge to run agents against a suite of test cases, ensuring they meet accuracy and efficiency standards before deployment.
Catching regressions in non-deterministic agent systems requires systematic test sets and continuous monitoring to identify performance degradations, utilizing LLM-as-judge and multi-dimensional rubrics to validate context-dependent failures.
LLM-as-judge works for quality assurance by providing scalable assessment of agent outputs against defined performance metrics and multi-dimensional rubrics, enabling systematic testing and continuous monitoring for agent pipelines.
Yes, you can set up quality gates for agent testing workflows without external dependencies, using a standalone framework to run test sets, apply multi-dimensional rubrics, and block deployments if performance metrics fall below defined standards.
You need multi-dimensional rubrics for agent testing when evaluating complex, context-dependent outputs where single metrics fail, allowing you to systematically measure accuracy, completeness, and efficiency across non-deterministic agent systems.
A limitation of using LLM-as-judge for agent performance metrics is handling non-determinism and context-dependent failures, requiring systematic test sets and continuous monitoring to ensure evaluation reliability and catch regressions.
guia-matthieuEvaluate AI agents using a 5-dimension rubric and multiple assessment methods.
chiruu12Monitor production apps for errors and regressions after deployment.
lucasgaldinosAutomate agent output evaluation with rubric-based scoring and self-critique.
northseadlDefine multi-dimensional rubrics and methodologies for evaluating agent systems.
