arXiv 2605.23904 introduces SkillOpt, a method for converting Agent skill descriptions (typically in .md files) into learnable parameters. The result: Agents can be fine-tuned on their own skill descriptions, with the skills becoming part of the model's weights rather than just prompt context.
The technical details: SkillOpt takes a .md file describing an Agent skill (e.g., "How to write a unit test in Python") and converts it into a "skill embedding" — a continuous vector representation that can be inserted into the model. The skill embedding is trained via a "skill distillation" loss: the model's output with the skill embedding should match the output of a model that has been prompted with the full .md file.
The benefit: the skill becomes part of the model's "parameter memory," not just "context memory." This means: (1) the skill doesn't consume context window space; (2) the skill is always available, even if the .md file is missing; (3) the skill can be combined with other skills, with the model learning to use them together.
The benchmark: on a set of Agent tasks (writing unit tests, generating API documentation, fixing simple bugs), SkillOpt-trained models match the quality of prompt-based skill usage while saving 40-60% of the context window. The skill embeddings are also composable — multiple skill embeddings can be merged to handle complex tasks.
The bigger takeaway: "skill as a parameter" is a new abstraction for Agent systems. The traditional "skill as a .md file" approach has scaling limits (context window overflow, retrieval errors). SkillOpt's "skill as a parameter" approach is more efficient and reliable, and it paves the way for "skill libraries" that can be plugged into Agents at inference time. For the industry, this means Agent vendors will need to invest in "skill embedding" infrastructure, not just "skill management."