Recent work has evaluated automated skill curation against automated baselines, largely neglecting the role of human maintenance. This research examines the full commit histories of five public AI-skill repositories, analyzing 873 commits, 143 skill files, and 254 substantive post-creation edits from October 2025 to June 2026. Each edit was coded using pre-registered governance, operation, and trigger-evidence codebooks. The findings indicate that every substantive edit is authored or merged through a named human account, with 62% carrying an AI co-author trailer. The majority of changes involved additions and corrections to skill content.
The study also found that a pre-registered rule-likeness axis failed its reliability gate, suggesting that reliably coding rule-likeness from commit artifacts remains a measurement challenge. This highlights the complexity of assessing skill evolution in AI agents. The research emphasizes that current skill maintenance looks less like an autonomous pipeline and more like a human-governed, AI-assisted loop.
Researchers released the corpus, codebooks, mining scripts, and a replay protocol for automated skill curators. This release offers tools for understanding and potentially improving the process of maintaining AI agent skills. This work is relevant for engineers running models and agents in production who need to understand the ongoing maintenance requirements of their systems.
Source: https://arxiv.org/abs/2609.05677