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Security Audit

molt-registry

github.com/openclaw/skills
AI SkillCommit 13146e6a3d46
69
CAUTION
Scanned 3 months ago
0
Critical
Immediate action required
1
High
Priority fixes suggested
2
Medium
Best practices review
2
Low
Acknowledged / Tracked

Trust Assessment

molt-registry received a trust score of 69/100, 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, 1 high, 2 medium, and 2 low severity. Key findings include Unpinned npm dependency version, Node lockfile missing, Skill accesses private key from environment variables.

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 14, 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
91%
LLM Behavioral Safety
76%

Behavioral Risk Signals

Filesystem Write
3 findings
Shell Execution
1 finding
Excessive Permissions
2 findings

Security Findings5

SeverityFindingLayerLocation

Scan History

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