Self-evolution of large models has long been stuck on a paradox: the more open the task, the less reliable the reward; the easier the task is to verify, the narrower the training distribution. The new paper Skill Self-Play from the Alibaba Qwen team tries to bridge the two ends with "skills". The framework doesn't just let the model make up its own questions. It consists of a problem generator, a solver, and a dynamic skill controller: the controller first selects a skill, the problem generator produces a verifiable task based on it, the solver attempts it, and the system then expands, prunes, or rewrites the skill library based on failed samples, task difficulty, and validity — retaining only the training samples near the model's capability frontier. The skills are only used in training; the final model can still be driven by prompts alone at inference time. The results are more persuasive than the concept. Qwen3-4B's tool-calling composite score rises from 60.2 to 66.7; Qwen3-8B's logical reasoning rises from 23.6 to 32.4; and Ministral-3-8B — originally weak at tool-calling — jumps from 20.7 to 63.6. The code is open-sourced under Apache 2.0. My take: the value of this work is not just another RL recipe, but turning the "skill library" into evolvable training infrastructure. The next phase of model competition may no longer be about who feeds more data, but about who can let tasks, verifiers, and curricula evolve together.