Security Audit

moonclerk-automation

github.com/ComposioHQ/awesome-claude-skills

AI SkillCommit 27904475d127

TRUSTED

Scanned about 2 months 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 85/100, placing it in the Mostly Trusted category. This skill has passed most security checks with only minor considerations noted.

SkillShield's automated analysis identified 2 findings: 0 critical, 0 high, 1 medium, and 1 low severity. Key findings include Skill enables broad execution of external tools, Unpinned Rube 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 20, 2026 (commit 27904475). 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

91%

Behavioral Risk Signals

Network Access

1 finding

Shell Execution

1 finding

Excessive Permissions

1 finding

Security Findings2

Severity	Finding	Layer	Location
MEDIUM	Skill enables broad execution of external tools The skill instructs the LLM to use `RUBE_MULTI_EXECUTE_TOOL` and `RUBE_REMOTE_WORKBENCH`. These are generic tools capable of executing a wide range of operations on Moonclerk via the Rube MCP. While the skill advises caution (e.g., 'Always search first'), these tools grant the LLM broad, unconstrained access to Moonclerk functionalities. This broad capability could lead to unintended or unauthorized actions if the LLM is compromised, misaligned, or misused. Implement stricter access controls or granular permissions within the Rube MCP for the Moonclerk toolkit, limiting the scope of operations an LLM can perform. Alternatively, ensure the LLM's execution environment is heavily sandboxed and monitored when using such broad tools.	LLM	SKILL.md:50
LOW	Unpinned Rube MCP dependency The skill's manifest specifies `rube` as a required MCP but does not pin a specific version. This could lead to unexpected behavior, compatibility issues, or security vulnerabilities if a future, unverified version of the `rube` MCP is used, potentially introducing breaking changes or malicious code. Specify a precise version or version range for the `rube` MCP in the `requires` section of the manifest (e.g., `{"mcp": ["rube==1.2.3"]}` or `{"mcp": ["rube>=1.0.0,<2.0.0"]}`) to ensure deterministic behavior and mitigate supply chain risks.	LLM	SKILL.md:1

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