Skin lesion segmentation is a medical image analysis task that involves automatically delineating lesion boundaries from dermatoscopic or clinical images. It plays a critical role in the early detection of skin cancer like melanoma. Low contrast and fuzzy boundaries are the main challenges, especially for limited training images. To tackle these challenges with limited annotation data, we propose a controllable prompt generation agent to activate the skin lesion segmentation capability in vision foundation models for medical image analysis. Specifically, we reduce the pixel-level action space to the grid level for efficient search. With Convolutional Neural Networks as the backbone, the agent performs spatial reasoning over an image to simultaneously find the prompt coordinate and its label using a policy function, and provides the selected prompt points for vision foundation model. The interaction process will be terminated within a fixed iteration number. For optimization, we propose asymmetric rewards aligned with the value function and introduce them into proximal policy optimization to save computation and memory cost. Interestingly, better performance is achieved with fewer prompt points than the threshold number, along with some background points. Thus, the proposed agent is referred to as the Prompt Efficient Generation agent. Experimental results on public benchmark skin lesion segmentation datasets show that PEG outperforms state-of-the-art methods, and the IoU improvement is at least 4% compared with SAM.