SkillGit: Federated Skill Evolution via Version Control

Large language model agents can continuously improve their capabilities by evolving reusable natural-language skills from execution experience. In real-world deployments, however, such experience is often distributed across multiple data owners and cannot be directly pooled due to privacy constraints, which naturally motivates federated skill evolution. A straightforward solution is to let each client independently evolve the shared skill and upload the resulting document for server-side aggregation. However, this document-level update abstraction is poorly suited to skill evolution. Specifically, i) an evolved skill may still encode client-specific information distilled from private traces, ii) repeated transmission of the entire document incurs unnecessary communication when only a small portion is modified, and iii) the actual knowledge contributed by a client is buried within a largely unchanged skill document, making it difficult to identify and evaluate the useful revisions. To address this issue, we propose SkillGit, a federated skill evolution framework that reformulates cross-client collaboration from full-document sharing to semantic revision sharing. Instead of uploading complete evolved skills, each client submits compact semantic commits that explicitly describe the intended changes to a shared skill version. SkillGit further introduces a commit-aware aggregation strategy that evaluates and integrates client revisions according to their contribution to the shared skill, allowing complementary knowledge to accumulate while filtering redundant or harmful updates. To stabilize the evolution process, we design a version validation mechanism that evaluates newly merged skill versions with distributed client feedback and rolls back regressions when necessary. Experimental results across three datasets and three LLM families demonstrate that SkillGit outperforms state-of-the-art baselines by up to 22% and approaches centralized evolution without sharing raw client data.