The MetaSKILLs framework introduces a structured approach to teaching AI agents complex, multi-step skills. It focuses on decomposing high-level goals into reusable primitives to reduce execution errors. This modularity allows developers to scale agent capabilities without retraining core models. Practitioners can now build more reliable autonomous pipelines by treating skills as discrete, composable assets.