Agentic AI ecosystems increasingly rely on reusable agent skills that combine executable code, natural-language instructions, configuration artifacts, and external tool integrations. These hybrid artifacts create software supply-chain risks because malicious intent may be distributed across instructions, code, metadata, and runtime behavior, making isolated alerts difficult to interpret. We evaluate three open-source scanners on a held-out test set of 140 skills comprising 80 malicious and 60 benign samples, including Cisco Skill Scanner with and without its optional LLM analyzer. The default Cisco configuration achieves a recall of 0.91 with 33 false positives among the 60 benign skills, while the LLM-enabled configuration using Qwen3:8B achieves a recall of 0.85 with 40 false positives. We then present a framework that combines static findings, safety-constrained sandbox evidence, and LLM-based reasoning to assess malicious intent. The framework tracks the origins of findings and relates evidence across analysis layers. In a within-system evidence-source ablation using the same reasoning backend, adding dynamic evidence to skill-content reasoning increases F1 from 0.70 to 0.85. Combining static and dynamic evidence further increases F1 to 0.89, with five false positives among 60 benign skills. Dynamic evidence provides the largest observed improvement. The Full Pipeline detects 68 of 80 malicious skills, achieving 0.88 accuracy, 0.93 precision, and 0.85 recall. It matches the observed recall of Cisco Skill Scanner with LLM while reducing false positives from 40 to five. This system-level comparison does not isolate model or architecture effects.

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