AI coding agents rely on repository instructions, skills, hooks, tool-server declarations, and subagent definitions. These artifacts distribute both behavior and access to executable dependencies, making configuration review part of the agent software supply chain. We study 3,171 public GitHub repositories: 2,660 assembled setups and 511 skill collections. Deterministic analysis, mechanical re-derivation, model-assisted adjudication, human review, and platform documentation checks identify six categories of configuration exposure and conformance issues. Unpinned MCP package declarations occur in 9.8% of setups, broad execution grants in 2.5%, and broad skill tool preapproval in 3.8%. Their union covers 409 setups (15.4%); among setups with MCP configuration, 24.5% contain an unpinned declaration. Including required-field and skill-format issues brings the setup rate to 17.9% and the collection rate to 6.8%. Holding the six categories fixed, contextual review changes the setup rate from 18.3% to 17.9%; rule selection explains most of the reduction from the broader candidate set. The findings identify concrete opportunities to pin dependencies, review execution pre approval, and check component conformance before distribution or use. A documented permission exception also exposes a shared error in the scanner and its mechanical audit, motivating version-specific semantic checks. The study provides reproducible evidence about repository declarations; agreement with human judgments informs label validation, while runtime consequences and recall remain unmeasured.

翻译：暂无翻译