Security Audit

new_relic-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

new_relic-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 Unversioned 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	Unversioned External MCP Dependency The skill declares a dependency on the 'rube' MCP (Managed Control Plane) without specifying a version or integrity check. The documentation also refers to a hardcoded endpoint `https://rube.app/mcp`. This introduces a supply chain risk, as changes or compromises to the `rube.app` service could silently affect the skill's behavior and security without the skill author or user being able to control the version or detect unauthorized modifications. This is analogous to an unpinned dependency for a software library, where the lack of version control for an external service can lead to unexpected or malicious behavior. Implement a mechanism to specify and verify the version or integrity of the `rube` MCP. If the MCP provider offers versioned endpoints, use a specific version. If not, consider alternative methods for integrity verification or risk mitigation, such as a hash or signature check if supported by the MCP client, or a more robust trust mechanism for the external service.	LLM	SKILL.md:20

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