SkillAdm: Rubric-Guided, Skill-Specific Admission for Online Test-Time Skill Learning

Online test-time skill learning (TTSL) enables frozen LLM agents to acquire reusable skills through interaction, continuously reusing and updating them over sequential task streams. Since a skill distilled from limited experience is not necessarily beneficial, skill admission—determining whether a candidate skill improves task execution before activating it for reuse—is critical for reliable online TTSL. Existing admission methods face two key challenges: coarse, oracle-free evaluation and validation limited to existing tasks. To address these, we propose SkillAdm, an online TTSL framework for continual agent self-improvement that acquires verified environment knowledge, constructs task-specific, progress-aware rubrics for oracle-free skill distillation, and performs two-stage utility assessment over both skill-specific validation tasks and relevant future verification tasks. Experiments across three benchmarks and three LLM backbones show that SkillAdm consistently outperforms the oracle-free baseline ASI and even surpasses oracle-assisted baselines using ground-truth outcomes.