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Security Audit

unipile-linkedin

github.com/openclaw/skills
AI SkillCommit 13146e6a3d46
58
CAUTION
Scanned about 2 months ago
2
Critical
Immediate action required
0
High
Priority fixes suggested
1
Medium
Best practices review
0
Low
Acknowledged / Tracked

Trust Assessment

unipile-linkedin received a trust score of 58/100, placing it in the Caution category. This skill has some security considerations that users should review before deployment.

SkillShield's automated analysis identified 3 findings: 2 critical, 0 high, 1 medium, and 0 low severity. Key findings include Unpinned npm dependency version, Suspicious Dependency Version (Typosquatting/Malicious Package).

The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. The LLM Behavioral Safety layer scored lowest at 40/100, indicating areas for improvement.

Last analyzed on February 14, 2026 (commit 13146e6a). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.

Layer Breakdown

Manifest Analysis
100%
Static Code Analysis
100%
Dependency Graph
93%
LLM Behavioral Safety
40%

Behavioral Risk Signals

Shell Execution
2 findings

Security Findings3

SeverityFindingLayerLocation

Scan History

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