As AI agents evolve, the distinction between Tools and Skills becomes crucial for handling complex workflows. Tools are simple, atomic functions, while Skills are structured capabilities that combine multiple steps, validation, and domain knowledge. This article explains why agents need Skills, how to write effective ones, and the often-overlooked importance of validation. Using a stock analysis skill as a case study, it demonstrates how a well-designed skill can guide a model through a multi-step process reliably. The post also covers the technical side: how agents load skill lists, present them to the model, and manage the skill acquisition and usage lifecycle. For developers building production-grade agents, understanding this distinction is key to creating systems that can handle real-world complexity without constant human intervention.
Explore why AI agents need Skills beyond Tools, how to design them effectively, and the critical role of validation in skill development.