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Security Audit

user-cognitive-profiles

github.com/openclaw/skills
AI SkillCommit 13146e6a3d46
49
CRITICAL
Scanned 2 months ago
0
Critical
Immediate action required
2
High
Priority fixes suggested
3
Medium
Best practices review
0
Low
Acknowledged / Tracked

Trust Assessment

user-cognitive-profiles received a trust score of 49/100, placing it in the Untrusted category. This skill has significant security findings that require attention before use in production.

SkillShield's automated analysis identified 5 findings: 0 critical, 2 high, 3 medium, and 0 low severity. Key findings include Unsafe deserialization / dynamic eval, Dangerous call: __import__(), Unpinned Python dependency version.

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

Layer Breakdown

Manifest Analysis
85%
Static Code Analysis
85%
Dependency Graph
79%
LLM Behavioral Safety
100%

Behavioral Risk Signals

Shell Execution
1 finding
Dynamic Code
1 finding

Security Findings5

SeverityFindingLayerLocation

Scan History

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