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.

Personalizing large language models requires adapting model behavior to individual users while preserving robustness and deployment-scale efficiency. Existing approaches typically personalize LLMs either at the input level, by retrieving user histories or constructing profile prompts, or at the parameter level, by maintaining user-specific parameter-efficient modules. The former makes personalization sensitive to retrieval quality and prompt design, whereas the latter incurs storage and maintenance costs that grow with the user population. To address these limitations, we propose TAP-PER (Temporal Attentive Prefix for PERsonalization), a prefix-based framework that encodes user preferences as learnable representations, avoiding the serialization of user histories into prompts and replacing heavy per-user adapters with lightweight user-state prefix embeddings. Inspired by personalized recommendation systems, TAP-PER decomposes user modeling into user-state and query-conditioned components, and incorporates temporal signals to capture the evolving nature of user interests. Experiments on six LaMP tasks show that TAP-PER consistently outperforms prompt-based and model-based baselines across classification, rating, and generation settings. Moreover, TAP-PER uses 130× fewer per-user parameters than OPPU and roughly half the total parameter footprint of PER-PCS at the 1,000-user scale, demonstrating scalable personalization without prompt-serialized histories or heavy per-user adapters.