Anthropic researchers propose a shift from creating specialized AI agents to developing modular "skills" that provide domain-specific expertise. These skills are simple, organized folders of code and instructions that allow a general model to perform complex tasks without cluttering its memory. By using code as a universal interface, agents can execute consistent workflows in fields like finance or life sciences. This architecture leverages progressive disclosure, ensuring the model only accesses relevant data when necessary for a specific job. Ultimately, this framework enables continuous learning and allows both technical and non-technical users to share and scale institutional knowledge effortlessly. These portable units of capability transform AI from a general tool into a bespoke expert tailored to any professional environment.