As Anthropic Skills, xAI Grok Skills, and Alibaba's QwenAgentWorld all push reusable skill documents into the agent engineering stack, the real bottleneck has shifted from "can we write skills" to "can we pick the right skills." arXiv 2606.32025's SkillComposer formalizes the composition problem as task-conditioned skill sequence prediction, jointly answering three dimensions — subset, count, and order — through a constrained autoregressive decoder that lets structural dimensions emerge in a single decoding pass. On SkillsBench, GPT-5.2-Codex gains +23.1pp over a no-skill baseline, and Gemini-3-Pro-Preview gains +18.2pp; both exceed the Top-3 retrieval upper bound while using fewer prompt tokens. The implication: agent inference budgets need to be reallocated from "reading the prompt" to "making the decision."