The previous fully-supervised and weakly-supervised camouflaged object segmentation tasks required a significant amount of annotated data for supervised training to enable models to segment camouflaged objects effectively. However, models like the Segment Anything Model (SAM), which falls under the category of Promptable Segmentation models, can achieve excellent segmentation performance on unseen images with just an instance-specific visual prompt. Nevertheless, for complex scenarios like camouflaged objects, SAM may not perform well even with an instance-specific prompt. Furthermore, the question arises: Is an instance-specific prompt necessary? In more realistic scenarios, where only a task-generic task description is provided as a universally applicable text prompt, how can we improve segmentation across various datasets under the camouflaged object segmentation task?