What did last year's skills lose when the model changed underneath them, the instructions or the thing the instructions were compensating for? A skill aimed at a weaker model often encodes the workaround rather than the task, so it reads as noise to a stronger one and you delete the wrong half. Turning a skill into validation the agent runs on its own output, rather than prose telling it to be careful, is hard to judge from a description. If a skill can't survive a model swap, it was probably describing the model rather than the job.