Optimizing AI agent skills by reducing redundant documentation tokens (opens in new tab)

Large language models often already possess extensive knowledge of common API references, authentication flows, and standard SDK patterns from their training data. When developers create skills that repeat this information, they consume valuable space within the finite context window of the AI. This redundancy pushes out critical data like conversation history or workspace files, which can actually degrade the quality of the generated output. <a href="