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Security Audit

proactive-research

github.com/openclaw/skills
AI SkillCommit 13146e6a3d46
10
CRITICAL
Scanned about 2 months ago
5
Critical
Immediate action required
3
High
Priority fixes suggested
1
Medium
Best practices review
0
Low
Acknowledged / Tracked

Trust Assessment

proactive-research 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 9 findings: 5 critical, 3 high, 1 medium, and 0 low severity. Key findings include Persistence / self-modification instructions, Arbitrary command execution, Dangerous call: subprocess.run().

The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. The Manifest Analysis layer scored lowest at 0/100, 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
0%
Static Code Analysis
48%
Dependency Graph
100%
LLM Behavioral Safety
70%

Behavioral Risk Signals

Network Access
1 finding
Shell Execution
7 findings
Dynamic Code
1 finding
Excessive Permissions
1 finding

Security Findings9

SeverityFindingLayerLocation

Scan History

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