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Security Audit

everclaw

github.com/openclaw/skills
AI SkillCommit 13146e6a3d46
65
CAUTION
Scanned 2 months ago
0
Critical
Immediate action required
2
High
Priority fixes suggested
2
Medium
Best practices review
1
Low
Acknowledged / Tracked

Trust Assessment

everclaw received a trust score of 65/100, placing it in the Caution category. This skill has some security considerations that users should review before deployment.

SkillShield's automated analysis identified 5 findings: 0 critical, 2 high, 2 medium, and 1 low severity. Key findings include Exfiltration of Agent Memory and Identity Files, API Key Transmission to Third-Party Service, Local Storage of API Key.

The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. The LLM Behavioral Safety layer scored lowest at 54/100, 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
100%
Dependency Graph
100%
LLM Behavioral Safety
54%

Behavioral Risk Signals

Network Access
4 findings
Filesystem Write
3 findings
Shell Execution
2 findings
Dynamic Code
1 finding
Excessive Permissions
1 finding

Security Findings5

SeverityFindingLayerLocation

Scan History

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