Most people using AI well have the same problem: the result is good, but it is not reproducible or scalable. The methodology lives in one person's chat history and hidden memory, and what transfers to a colleague is only the prompt and the output, never the judgment behind them.

This session walks through a nine-step method for converting personal expertise into agentic skills: narrating how you actually do the work, surfacing hidden context and decision rules, decomposing a monolithic prompt into modules with real interface contracts, packaging it with guardrails and tests, and finally storing it in a governed repository where skills inherit shared standards instead of restating them. The talk covers how to pick a first candidate process, how to test a method rather than a demonstration, and how individual expertise becomes institutional capability.