Security Audit

moonclerk-automation

github.com/ComposioHQ/awesome-claude-skills

AI SkillCommit 99e2a2951545

TRUSTED

Scanned 6 days ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

moonclerk-automation received a trust score of 94/100, placing it in the Trusted category. This skill has passed all critical security checks and demonstrates strong security practices.

SkillShield's automated analysis identified 1 finding: 0 critical, 0 high, 1 medium, and 0 low severity. Key findings include Unpinned External MCP Dependency.

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 17, 2026 (commit 99e2a295). 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

93%

Behavioral Risk Signals

Network Access

1 finding

Security Findings1

Severity	Finding	Layer	Location
MEDIUM	Unpinned External MCP Dependency The skill declares a dependency on the 'rube' MCP without specifying a version or specific identifier. This introduces a supply chain risk, as changes or vulnerabilities in the 'rube' MCP could silently affect the skill's security and functionality. If the external MCP's behavior changes or becomes malicious, the skill would be impacted without explicit user consent or awareness. If the platform supports it, specify a version or a more precise identifier for the 'rube' MCP dependency. If version pinning is not available for MCPs, acknowledge this inherent risk and ensure the 'rube' MCP provider is trusted and regularly audited.	LLM	SKILL.md:4

Scan History

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