Reusable "skills" for coding agents: how to design them so they do not drift (clipnotebook.com)

A recent discussion highlights the importance of designing reusable "skills" for coding agents to combat issues like prompt drift, which can compromise the reliability and effectiveness of AI-driven coding workflows. Unlike simple prompts, skills are structured workflows with clearly defined inputs, outputs, and verification processes, ensuring that agent actions remain consistent and reviewable over time. This trend is driven by the need for stable and predictable AI behaviors that mimic engineering outputs rather than rely on variable interactions or lucky results. For the AI/ML community, the focus on developing skills emphasizes the creation of a rigorous framework around coding tasks, such as generating tests or refactoring code. By treating skills as function-like contracts with hard boundaries, adherence to strict scope, and defined success criteria, developers can mitigate the risks associated with AI-generated outputs. The implications are profound—this approach not only enhances code quality and maintainability but also fosters better collaboration between AI agents and human engineers by implementing reliable review processes and performance metrics.