evaluation-methodology

A plugin evaluation methodology formalizes quality measurement using a layered rubric and scoring formulas for static analysis, LLM judge scoring, and Monte Carlo simulation. It enables product teams to interpret scores, calibrate thresholds, and assign marketplace badges consistently.

Use the ten scoring dimensions defined in a layered rubric to evaluate plugin quality, combining static analysis, LLM judge scoring, and Monte Carlo simulation. Calculate a composite score using the provided formulas, then interpret results against calibrated thresholds to determine marketplace readiness and badge eligibility.

Calibrate quality thresholds by applying the composite scoring formula across the ten evaluation dimensions, then adjusting badge thresholds based on your marketplace requirements. The methodology provides guidelines for interpreting scores and diagnosing underperforming dimensions to ensure credible badge assignments.

Yes, the evaluation methodology explicitly defines three evaluation layers: static analysis, LLM judge scoring, and Monte Carlo simulation. Each layer contributes to the ten scoring dimensions and the composite scoring formula used to measure plugin quality and determine marketplace badge thresholds.

Yes, the methodology provides guidelines for diagnosing underperforming dimensions by interpreting individual layer scores within the rubric. Developers and evaluators can identify weak areas across the ten scoring dimensions and take targeted improvement steps before marketplace submission.

Use a formal evaluation methodology when you need standardized, credible quality measurement for marketplace readiness, partner communication, or badge assignment. The layered rubric ensures consistent decision-making across static analysis, LLM judge scoring, and Monte Carlo simulation, replacing inconsistent ad hoc testing.