Evaluate agent performance using rubrics and test sets.
npx skills add https://github.com/aldy505/atrium --skill evaluation-aldy505
Please help me install this Agent Skill. Skill: evaluation Source: https://github.com/aldy505/atrium/tree/main/.agents/skills/context-engineering-collection/skills/evaluation Command: npx skills add https://github.com/aldy505/atrium --skill evaluation-aldy505
This Skill addresses the challenge of systematically measuring and improving the performance of AI agents, ensuring their reliability and effectiveness in complex tasks.
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 agent performance, you use a framework of rubrics and test sets to systematically measure factual accuracy, citation accuracy, and source quality. This allows you to compare outputs against defined quality standards.
LLM-as-judge is an evaluation methodology where a language model scores agent outputs. You need it for scalable quality assurance testing when human evaluation is too slow, allowing robust assessment of factual accuracy and completeness.
You set up quality gates by implementing automated checks that execute test sets against common queries. This catches regressions and verifies agents meet performance thresholds for accuracy and helpfulness before deployment.
Yes, you can use test sets and rubrics to measure tool efficiency and source quality. The framework supports evaluating agent performance across multiple dimensions, ensuring systematic measurement of these specific metrics.
The best way to compare agent versions is running a suite of tests against common queries using defined rubrics. This systematically measures factual accuracy and helpfulness, benchmarking the new version against the previous one.
ken-cavanagh-gleanDesign evaluation frameworks with rubrics and LLM-as-judge workflows.
NourcineAbCoordinate specialized AI agents to review GitHub pull requests.
alexsandrocruzTrace backward through call stacks to identify original error triggers.
guanyangEvaluates agent performance using multidimensional rubrics and systematic testing.
