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Security Audit

lingzhu

github.com/openclaw/skills
AI SkillCommit 13146e6a3d46
10
CRITICAL
Scanned 2 months ago
6
Critical
Immediate action required
1
High
Priority fixes suggested
2
Medium
Best practices review
3
Low
Acknowledged / Tracked

Trust Assessment

lingzhu received a trust score of 10/100, placing it in the Untrusted category. This skill has significant security findings that require attention before use in production.

SkillShield's automated analysis identified 12 findings: 6 critical, 1 high, 2 medium, and 3 low severity. Key findings include Network egress to untrusted endpoints, Unpinned npm dependency version, Node lockfile missing.

The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. The LLM Behavioral Safety layer scored lowest at 0/100, indicating areas for improvement.

Last analyzed on February 13, 2026 (commit 13146e6a). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.

Layer Breakdown

Manifest Analysis
10%
Static Code Analysis
100%
Dependency Graph
89%
LLM Behavioral Safety
0%

Behavioral Risk Signals

Network Access
5 findings
Filesystem Write
3 findings
Shell Execution
6 findings
Dynamic Code
2 findings
Excessive Permissions
2 findings

Security Findings12

SeverityFindingLayerLocation

Scan History

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