A research team from CUHK proposed SLIM, a dynamic skill lifecycle management framework for large model agents. It enables agents to autonomously retain, retire, or expand external skills during reinforcement learning. SLIM outperforms existing methods by 7.1 percentage points on average, achieving 87.5% success in ALFWorld vs 75.0% baseline, and shows strong results in SearchQA tasks.