Trust Assessment
moss-docs received a trust score of 94/100, placing it in the Trusted category. This skill has passed all critical security checks and demonstrates strong security practices.
SkillShield's automated analysis identified 1 finding: 0 critical, 0 high, 1 medium, and 0 low severity. Key findings include Unpinned dependencies in installation instructions.
The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. All layers scored 70 or above, reflecting consistent security practices.
Last analyzed on February 14, 2026 (commit 13146e6a). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.
Layer Breakdown
Security Findings1
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| MEDIUM | Unpinned dependencies in installation instructions The skill's installation instructions for JavaScript/TypeScript and Python packages do not specify exact versions. This can lead to non-deterministic builds, introduce breaking changes, or expose users to supply chain attacks if a future version of the dependency contains vulnerabilities or malicious code. Recommend pinning dependency versions (e.g., `npm install @inferedge/moss@1.2.3` or `pip install inferedge-moss==1.2.3`) to ensure deterministic and secure installations. | LLM | SKILL.md:169 |
Scan History
Embed Code
[](https://skillshield.io/report/eef8793b63aea8dd)
Powered by SkillShield