Learn how SkillSelect invitation rounds rank EOIs, what points you need, why delays happen, and how to improve your Australia PR prospects with Aspire Square.

Explains the Sept 30 timeline for Subclass 189 invites, the 65-point minimum, and why updating your SkillSelect profile matters.

Reusable skill libraries are becoming important infrastructure for large language model (LLM) agents, yet existing selection methods often treat skills as retrievable documents and return fixed top-k lists. This paper presents SkillSelect-Serve, a budget-controllable and QoS-aware framework that formulates agent skill selection as Skill Service Recommendation and Composition. SkillSelect-Serve represents raw skills as structured Skill Services with functional descriptions, dependencies, context cost, risk, and QoS-related attributes. A local Micro-Agent Requirement Planner converts natural-language tasks into structured service requirements, while a shared discovery backbone retrieves candidate services from a large registry. The framework then performs dual-granularity utility modeling with skill-level marginal suitability estimation and bundle-level calibration for coverage, redundancy, cost, and risk trade-offs. Experiments on 35,353 skills and 586 task queries show that SkillSelect-Serve consistently improves same-budget bundle recall and mean utility over fixed top-k retrieval baselines.

If you have been checking your SkillSelect account regularly and hoping to see that long-awaited NSW nomination email, then this announcement is important.