For a long time, the task execution capabilities of AI agents have mainly relied on text instructions. However, when facing tasks that highly depend on visual perception, such as photo editing and GUI operations, the limitations of pure text have become increasingly apparent. Recently, the openJiuwen community officially released Skill-Omni, the first engineering-validated multimodal Skill paradigm in the industry. It not only upgrades the agent's experience from "being able to understand" to "being able to see," but also opens up a new path for intelligent agents to interact with complex visual tasks.
Traditional Skill paradigms often struggle when dealing with visual tasks due to the lack of intuitive references. For example, in image restoration tasks, an agent may find it difficult to accurately grasp the adjustment scale based solely on textual descriptions like "soft tone." The core innovation of Skill-Omni lies in its ability to convert web screenshots, interface states, and video operation sequences into reusable visual experience assets. By introducing comparison images and key frames, agents can not only understand the operational process but also intuitively grasp the expected "visual standards" of the task, significantly improving the success rate and accuracy of task execution.
In practical applications, developers can easily convert web links or Bilibili video tutorials into multimodal Skills using the built-in auto-generation tools of Skill-Omni. The system automatically filters out interfering information such as advertisements and accurately extracts key screenshots and step logic. This mechanism transforms scattered internet content into a high-quality "experience library" for agents, allowing complex software installation, configuration, or design operations to be performed without starting from scratch, achieving rapid experience accumulation and reuse.
To balance model context load and visual information acquisition, the JiuwenSwarm platform has designed a clever "on-demand reading" mechanism. The system dynamically detects visual support capabilities during runtime and only calls images when the model truly needs them, avoiding resource waste caused by loading a large number of images all at once. This on-demand injection of visual evidence allows agents to refer to "operation demonstrations" in real-time, similar to humans, significantly reducing the possibility of errors during task execution.
The release of Skill-Omni marks the shift of AI agent experience engineering from purely document-driven approaches to a multimodal era that emphasizes both visual and logical aspects. Currently, this paradigm has shown great potential in scenarios such as image processing, GUI automation, and enterprise knowledge base upgrades. Looking ahead, as Skill-Omni explores the Physical AI field, this paradigm is expected to enable intelligent agents to achieve more precise control in the real world by accumulating physical interaction experiences. Currently, Skill-Omni is available out-of-the-box in JiuwenSwarm, providing a solid foundation for developers to build more powerful multimodal agents.
