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Security Audit

hour-meter

github.com/openclaw/skills
AI SkillCommit 13146e6a3d46
10
CRITICAL
Scanned 3 months ago
4
Critical
Immediate action required
0
High
Priority fixes suggested
4
Medium
Best practices review
2
Low
Acknowledged / Tracked

Trust Assessment

hour-meter 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 10 findings: 4 critical, 0 high, 4 medium, and 2 low severity. Key findings include File read + network send exfiltration, Credential harvesting, Suspicious import: urllib.request.

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
86%
Dependency Graph
100%
LLM Behavioral Safety
82%

Behavioral Risk Signals

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

Security Findings10

SeverityFindingLayerLocation

Scan History

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