Microsoft just open-sourced SkillOpt! A framework for training agent skills like neural networks: SkillOpt treats a plain markdown file as the trainable parameter of a frozen LLM agent, applying the same optimization discipline used in weight training: learning rates, validation gates, batch sizes, and epoch schedules. The analogy maps precisely. The skill document is the parameter. Trajectory-derived edits are the gradient direction. An edit budget is the learning rate. A held-out split is the validation check. Here's how it works. A frozen model runs tasks with the current skill and produces scored trajectories. A separate optimizer model analyzes failures in minibatches, proposes structured add/delete/replace edits, and ranks them under a budget cap. If the candidate skill improves performance on a held-out split, the edit is accepted. If not, it's rejected and stored so the optimizer avoids repeating failed changes. The deployed output is a single best_skill. md file, typically 300 to 2,000 tokens. No weight changes, no extra inference-time calls. The learned rules are compact and readable. These read like rules a thoughtful engineer would write after a day with the benchmark, except they were discovered automatically. Learn more: Paper: arxiv.org/abs/2605.23904 GitHub: github.com/microsoft/SkillOp… SkillOpt isn't the first system to treat skills as something you can optimize. Hermes Agent independently built the same idea through a combination of skill_manage, Curator, and an optimization loop called GEPA that scores, mutates, and promotes skill documents across runs. Two teams, different architectures, same conclusion: the skill file is the highest-leverage thing to optimize in a frozen-model agent. I wrote a deep dive on how the Hermes agent works and covered all of these topics briefly. The article is quoted below.