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Security Audit

skill-deps

github.com/openclaw/skills
AI SkillCommit 13146e6a3d46
10
CRITICAL
Scanned about 2 months ago
3
Critical
Immediate action required
0
High
Priority fixes suggested
11
Medium
Best practices review
0
Low
Acknowledged / Tracked

Trust Assessment

skill-deps 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 14 findings: 3 critical, 0 high, 11 medium, and 0 low severity. Key findings include Sensitive environment variable access: $HOME, Sensitive environment variable access: $USER, Command Injection via unsanitized skill name in curl command.

The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. The LLM Behavioral Safety layer scored lowest at 10/100, 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
100%
Static Code Analysis
23%
Dependency Graph
100%
LLM Behavioral Safety
10%

Behavioral Risk Signals

Network Access
3 findings
Shell Execution
14 findings
Dynamic Code
3 findings
Excessive Permissions
11 findings

Security Findings14

SeverityFindingLayerLocation

Scan History

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