SkillOps: Agent Skill Libraries Begin to Manage Technical Debt Like Software Ecosystems
SkillOps, submitted on May 13, 2026, achieves a 79.5% success rate on ALFWorld, surpassing the strongest baseline by 8.8 percentage points without increasing LLM calls during tasks; as a plugin, it also improves retrieval-based baselines by 0.68–2.90 points.
Accumulating more skills does not necessarily make an agent stronger; compatibility issues, dependency changes, and erroneous reuse can create library-level technical debt. SkillOps elevates maintenance from task-time patching to ongoing software asset governance.
The framework uses typed Skill Contracts (including preconditions, outputs, actions, validations, and failures) to describe capabilities, organizes dependencies via a hierarchical ecosystem graph, and diagnoses from four dimensions: utility, compatibility, risk, and verification. Rule-based maintenance consumes almost no extra LLM tokens and can be plugged into existing retrieval or planning agents.
The long-term competitiveness of agent platforms will depend on skill registration, contracts, versioning, dependency, and health management; tool count only reflects scale. Skill marketplaces also require package management and supply chain governance.
Enterprises should establish processes for skill ownership, contracts, testing, and retirement, measuring asset value by task contribution and failure propagation to avoid unbounded accumulation.
Further validation is needed on larger real-world skill libraries, malicious dependencies, version migration, cross-agent reuse, and a comparison of rule-based vs. model-driven maintenance reliability.