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Provides rubrics and guidelines for evaluating AI outputs on accuracy, relevance, completeness, helpfulness, clarity, tone appropriateness, and safety. Includes weighting, calibration, and design artifacts.
How this skill is triggered — by the user, by Claude, or both
Slash command
/evaluation:output-quality-rubricsThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Without a rubric, quality evaluation is subjective and inconsistent. A rubric defines what "good" means in concrete, measurable terms — so different evaluators reach the same conclusions.
Without a rubric, quality evaluation is subjective and inconsistent. A rubric defines what "good" means in concrete, measurable terms — so different evaluators reach the same conclusions.
For each dimension, define a scale: Example — Accuracy (1-5):
Not all dimensions matter equally for every use case:
A rubric is only useful if evaluators use it consistently:
npx claudepluginhub owl-listener/ai-design-skills --plugin evaluationBuilds production-grade LLM-as-judge evaluation systems: direct scoring, pairwise comparison, rubric calibration, bias mitigation, and confidence scoring.
Implements LLM-as-judge techniques for evaluating LLM outputs via direct scoring, pairwise comparison, rubrics, and bias mitigation including position and length bias.
Builds a scoring rubric interactively, evaluates an artifact with multiple models in parallel, then autonomously improves it one criterion at a time until a score threshold is met or circuit breaker fires.