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Security Audit

hotel-finder

github.com/openclaw/skills
AI SkillCommit 13146e6a3d46
42
CRITICAL
Scanned 2 months ago
1
Critical
Immediate action required
1
High
Priority fixes suggested
2
Medium
Best practices review
0
Low
Acknowledged / Tracked

Trust Assessment

hotel-finder received a trust score of 42/100, placing it in the Untrusted category. This skill has significant security findings that require attention before use in production.

SkillShield's automated analysis identified 4 findings: 1 critical, 1 high, 2 medium, and 0 low severity. Key findings include File read + network send exfiltration, Sensitive path access: AI agent config, Unpinned repository in installation instructions.

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
70%
Static Code Analysis
85%
Dependency Graph
100%
LLM Behavioral Safety
86%

Behavioral Risk Signals

Network Access
2 findings
Filesystem Write
2 findings
Shell Execution
3 findings
Excessive Permissions
2 findings

Security Findings4

SeverityFindingLayerLocation

Scan History

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