We present the system of Team Skywalker for the Sentence-level Strict track of the BAREC Shared Task 2026, which requires predicting one of 19 ordered Arabic readability levels for each sentence. Our system integrates complementary public Arabic pretrained language-model (PLM) experts fine-tuned with classification, regression, and ordinal objectives. Their continuous scores, confidence estimates, and rank-based features are combined using cross-fitted LightGBM meta-models. We further incorporate a cumulative ordinal branch that models level-boundary probabilities and a normalized-text branch that improves robustness to Arabic orthographic variation. Finally, a QWK-aware decoder maps the fused continuous score to one of the 19 readability levels using monotonic thresholds selected exclusively on the public validation set. We use only the official training data for supervision and the public validation labels for model selection. In addition to the final fusion system, we report single-PLM baselines and controlled ablations. The final configuration achieves a QWK of 86.30 on the public validation set and 84.20 on the CodaBench blind evaluation, ranking fourth among the submitted systems.