LLM-powered agents increasingly rely on third-party skills that combine natural-language instructions with executable code, creating supply-chain risks that may span both artifacts. Existing skill scanners often rely on rule matching or direct LLM judgments and provide limited support for tracing such cross-domain behavior. We present the Supply-Chain Scanner for Agent Skills (SCS), a static analysis framework that unifies instructions and code for security analysis. SCS compiles natural-language instructions into a constrained Python-style intermediate representation using a limited set of security-oriented APIs. It then constructs program dependence graphs for instructions and bundled code and connects them into a cross-domain graph (xPDG). A Trust-Sensitivity-Capability (TSC) model propagates security properties over the graph to support structured, largely deterministic risk detection. On a manually verified benchmark of 117 malicious and 117 benign skills, SCS achieves the highest average accuracy (85.6%), recall (95.2%), and F1-score (86.9%) among evaluated methods across five LLMs, with 80.2% precision and average token consumption of 13.7 million. Its NL-to-IR compiler achieves 92.5%-94.5% step-level correctness on 400 human-annotated instruction steps. Finally, analyzing 66,157 real-world ClawHub skills yields 2,473 candidates, of which 627 are confirmed malicious through auxiliary dynamic validation and manual review.

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