evaluation-methodology

Plugin quality evaluation in PluginEval uses a three-layer framework: static analysis, LLM judge, and Monte Carlo simulations. These layers combine to produce a robust composite score measuring overall plugin and skill quality across ten dimensions.

To calibrate scoring thresholds for a marketplace, apply the anchored rubrics defined in the PluginEval methodology. These rubrics translate raw composite scores into actionable quality badges, ensuring objective and consistent partner evaluations.

Anti-pattern flags in plugin evaluation identify specific design flaws affecting triggering accuracy, orchestration fitness, and output quality. Recognizing these flags provides practical guidance to improve plugins and raise overall quality scores.

To explain quality badges to external partners, reference the anchored rubrics and badge thresholds from the PluginEval methodology. Badges represent a plugin's composite score, translating evaluation results into clear, actionable quality indicators.

Plugin quality is measured across ten distinct scoring dimensions defined within the PluginEval methodology. These dimensions evaluate triggering accuracy, orchestration fitness, and output quality to calculate a comprehensive composite score.

Yes, you can use the PluginEval methodology for skill quality assessment. The framework measures both plugins and skills through its three-layer evaluation process, applying the same ten scoring dimensions and composite formula.