AI Skills

"For the past few years, artificial intelligence has been discussed almost exclusively in terms of models. Bigger models, faster models, smarter models. More recently, the focus shifted to agents, systems capable of planning, reasoning, and acting autonomously. Yet the real leap in usefulness does not happen at the model level, nor at the agent level. It happens one layer above, at the level of Skills."

"A Skill is an applied, reusable unit of procedural knowledge that allows an AI system to reliably perform a specific task from start to finish. In practical terms, a Skill is an intelligent application that transforms user intent into execution. A Skill has a clearly defined purpose. It encapsulates domain-specific know-how. It follows a repeatable procedure. And it produces a concrete, usable result."

"To understand why Skills matter, it helps to look at the modern AI stack. At the foundation are models. They provide raw intelligence such as language understanding, reasoning, perception, and pattern recognition. They are powerful, but fundamentally generic. Above them sit agents. Agents function like an operating system. They plan tasks, break problems into steps, decide which tools or models to use, and manage execution flow. They are good coordinators, but coordination alone does not equal expertise."

Models provide raw, generic intelligence while agents coordinate planning and tool use. Skills sit above agents as the application layer that encodes domain procedures and delivers concrete outcomes. A Skill encapsulates purpose, domain-specific know-how, and a repeatable procedure to perform tasks from start to finish. Skills transform user intent into execution, producing usable results such as contract risk analysis, SaaS comparisons, pricing strategies, or financial reports. Users interact with Skills rather than models or agents, making Skills the primary interface through which AI delivers operational value.

Read at TNW | Artificial-Intelligence