Trust Assessment
tos-vectors 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 Python dependency 'tos'.
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 Python dependency 'tos' The Python scripts import the 'tos' library without specifying a version. This makes the skill vulnerable to supply chain attacks if a malicious version of 'tos' is published to PyPI or if the package maintainer's account is compromised. An attacker could introduce malicious code into a new version of 'tos', which would then be automatically installed if the skill is deployed in an environment that doesn't pin dependencies. Pin the version of 'tos' in a 'requirements.txt' file (e.g., 'tos==X.Y.Z') and ensure the deployment environment installs dependencies from this file. Regularly audit and update pinned dependencies. | LLM | scripts/init_vectors.py:5 |
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