A self-improving skill is a 📝skill document the agent patches in place during use when reality contradicts or extends what's written — refinement woven into operation rather than reserved for a separate review cycle.
When 📝Hermes Agent loads a skill to perform a task and encounters something the skill doesn't anticipate — a tool that has changed shape, an edge case the original author missed, a pitfall worth recording for next time — it updates the skill body and bumps the version. The next run loads the patched skill, not the original. There is no separate review queue, no human approval step, and no fork; the skill in the agent's library is the same skill that will be read in the next session.
Self-improvement distinguishes this style from the standard retrieval pattern, where the agent reads from a documentation store but never writes to it. It also distinguishes from fine-tuning, where improvement happens at training time on aggregated data rather than during the operational moment that surfaces the gap. The cost of getting self-improvement wrong — a skill that gets edited to be worse, or that drifts away from its original purpose — is a real failure mode the Autonomous Curator is designed to catch through periodic review and consolidation.
Self-improving skill is the "improve" step inside the 📝closed learning loop. Without it, the loop would still capture procedural knowledge from initial runs but would not refine it as conditions change. With it, the agent's skill library tracks reality with a lag of one execution rather than one curation cycle.