Generate and execute YAML-configured test cases for Relevance AI agents.
npx skills add https://github.com/RelevanceAI/relevance-builder-kit --skill eval-relevanceai
Please help me install this Agent Skill. Skill: eval Source: https://github.com/RelevanceAI/relevance-builder-kit/tree/main/.claude/skills/eval Command: npx skills add https://github.com/RelevanceAI/relevance-builder-kit --skill eval-relevanceai
This Skill provides a structured framework for automatically generating, executing, and analyzing comprehensive test cases for Relevance AI agents, enhancing validation efficiency and reliability.
Use the eval skill to generate test cases for your agent, then run platform evaluations to assess its readiness and robustness.
High-intent search queries and answers about installing and using this skill.
Automate AI agent testing by generating test cases based on agent configurations and tools, then executing evaluation batches to capture success rates and rule compliance metrics. This structured framework validates agent behavior across multiple scenarios to ensure reliability before deployment.
Validate agent behavior before deployment by running platform evaluations that execute detailed test batches against realistic scenarios. This process captures performance metrics, rule compliance, and success rates to systematically assess agent readiness and robustness.
Yes, you need YAML-configured test case rules to run platform evals on AI agents. The YAML configuration defines the deterministic testing parameters and simulation support required to automatically generate and execute meaningful validation scenarios.
Yes, you can monitor ongoing production quality for deployed AI agents by continuously executing evaluation batches. This captures performance metrics and rule compliance over time, ensuring sustained reliability and safety in real-world applications.
Deterministic testing for agent validation requires predefined YAML configurations and simulation support, meaning tests are constrained to configured rules and scenarios. Complex, unpredictable real-world interactions may not be fully captured by these deterministic test cases.
Yes, the eval skill works directly with Relevance AI agents by retrieving agent configurations and tools to generate meaningful test scenarios. It validates behavior by executing evaluation batches across multiple quality metrics and simulation environments.
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