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Security Audit

personal-genomics

github.com/openclaw/skills
AI SkillCommit 13146e6a3d46
16
CRITICAL
Scanned 2 months ago
0
Critical
Immediate action required
0
High
Priority fixes suggested
12
Medium
Best practices review
0
Low
Acknowledged / Tracked

Trust Assessment

personal-genomics received a trust score of 16/100, 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: 0 critical, 0 high, 12 medium, and 0 low severity. Key findings include Unsafe deserialization / dynamic eval, Missing required field: name, Unpinned Python dependency version.

The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. The Manifest Analysis layer scored lowest at 44/100, 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
44%
Static Code Analysis
93%
Dependency Graph
79%
LLM Behavioral Safety
100%

Behavioral Risk Signals

Shell Execution
8 findings
Dynamic Code
8 findings

Security Findings12

SeverityFindingLayerLocation

Scan History

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