evaluation-methodology

To evaluate plugin quality, this methodology defines a multi-layer framework combining static analysis, LLM judging, and Monte Carlo simulations. It provides anchored rubrics across four dimensions to calculate scores, apply thresholds, and assign badges for consistent assessment.

The best way to calibrate an LLM judge for skill evaluation is to apply the methodology's anchored rubrics across four defined dimensions. This process standardizes qualitative signals, ensuring accurate score blending and reliable badge decisions during assessment.

PluginEval scores are calculated by blending results from static analysis, LLM judge rubrics, and Monte Carlo simulations. Reports interpret these blended scores against predefined thresholds to determine badge criteria and communicate quality results to partners.

You need Monte Carlo simulations for measuring plugin quality when assessing performance variability and robustness under randomized conditions. This methodology uses simulations as one of three core layers to ensure comprehensive scoring and reliable badge decisions.

Yes, you can use these evaluation rubrics to communicate results to stakeholders. The methodology includes specific guidelines for score blending, interpreting reports, and translating qualitative signals into clear badge criteria for partners.

The limitation of using static analysis for skill evaluation is that it only covers one dimension of quality. This methodology requires combining static analysis with LLM judging and Monte Carlo simulations to achieve reliable scoring and comprehensive assessment.