llm_evaluation
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
moai-foundation-thinking
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moai-foundation-thinking
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evaluation
Build evaluation frameworks for agent systems. Use when testing agent performance, validating context engineering choices, or measuring improvements over time.
advanced-evaluation
Design and operate LLM-as-a-Judge evaluation systems using direct scoring, pairwise comparison, rubric calibration, evaluator bias mitigation, confidence scoring, and automated quality assessment. Use when building LLM-as-judge systems, comparing model responses, calibrating rubrics, debugging inconsistent evaluations, or designing A/B tests for prompt or model changes.
skill-comply
Measure whether agents actually follow a skill, rule, command, or agent definition by deriving expected behaviors, running representative scenarios, and comparing observed action timelines against the spec. Use after adding or changing instructions, before publishing skills, or when rules appear to be ignored.
offer-diamond-review
Evaluates Offer Diamonds for BlackBelt coaches using Ed Dale's frameworks. Produces Facebook-ready feedback comments.
