Automatic Question Generation (QG) can make reading-comprehension practice and assessment scalable. For educational use, generated questions should diagnose a target comprehension skill and rely on the passage evidence an expert would use to write the question. Existing answer-aware and skill-conditioned QG methods improve controllability, but often leave these skill-to-question and evidence-to-question dependencies implicit. We present SagE-QG, a Skill-aligned and Evidence-Grounding Framework for Question Generation. SagE-QG exposes evidence-grounded trajectories, optimizes multi-aspect rewards for answerability, skill alignment, and human similarity, and applies Counterfactual Evidence Intervention in reinforcement learning. The intervention removes selected evidence and rewards the Interventional Grounding Gap between full-passage and masked-evidence generations. Experiments on three reading-comprehension QG datasets show that SagE-QG delivers stronger educational quality, especially in skill alignment and answerability, while achieving competitive reference-based results.