Long-horizon LLM agents increasingly rely on retrieved procedural skills as external memory. However, existing skill-augmented RL frameworks typically supervise a bundle of retrieved skills only with trajectory-level outcome rewards, leaving two key questions unanswered: whether each skill was executed and whether it helped task completion. We formulate this as a per-skill credit assignment problem. To address it, we propose SkillRubricsRL, which pairs each skill with an applicability-aware trajectory-level rubric and derives two complementary signals: a fulfillment signal for training correct skill execution and a counterfactual utility signal for retrieval and skill-bank evolution. Across ALFWorld and WebShop, SkillRubricsR achieves 97.7% ALFWorld success, 84.4% WebShop success, and a WebShop score of 92.7, improving over the strongest prior multi-skill baseline by 2.8, 2.1, and 1.6 percentage points, respectively, while increasing skill execution rate by over 50 percentage points. Code is available at https://github.com/gminiheimdallr-a11y/SkillRubricsRL.