SkillOps: Managing LLM Agent Skill Libraries as Self-Maintaining Software Ecosystems

LLM agents increasingly use skill libraries to solve complex multi-step tasks, but these libraries can accumulate persistent defects as skills are added, reused, patched, and connected to changing dependencies. We identify this failure mode as skill technical debt: redundancy, missing validation, interface drift, stale implementations, and other library-level defects that may not break an individual skill locally but can harm future retrieval, composition, and execution. Existing skill-based agents mainly focus on task-time retrieval, planning, and repair, leaving library-time management largely unaddressed. We propose SkillOps, a method-agnostic plug-in framework for skill-library maintenance. SkillOps represents each skill as a typed Skill Contract \((P,O,A,V,F)\), organizes skills into a Hierarchical Skill Ecosystem Graph (HSEG) with dependency, compatibility, redundancy, and alternative edges, and diagnoses library health along utility, redundancy, compatibility, failure-risk, and validation-gap dimensions. Through the central interface cleaned_lib = run_maintenance(raw_lib), SkillOps transforms a raw skill library into a maintained library that can be used by existing retrieval or planning agents without changing their internal code. On ALFWorld, SkillOps achieves \(79.5%\) task success as a standalone agent, outperforming the strongest baseline by \(+8.8\)% with zero additional LLM calls. As a plug-in layer, it improves retrieval-heavy baselines by \(+0.68\)–\(+2.90\)%. The current rule-based maintenance implementation incurs nearly zero library-time LLM calls or tokens, showing that skill-library maintenance can be added as a low-overhead architectural layer.