Security Audit

statuscake-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

statuscake-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 1 finding: 0 critical, 0 high, 1 medium, and 0 low severity. Key findings include 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

93%

Security Findings1

Severity	Finding	Layer	Location
MEDIUM	Unpinned Rube MCP dependency The skill manifest specifies a dependency on the 'rube' MCP without a version constraint. This means that any future changes, including breaking changes or the introduction of vulnerabilities, in the 'rube' MCP could directly impact the security and stability of this skill without explicit user action or review. This introduces a supply chain risk. Pin the 'rube' MCP dependency to a specific version or version range in the skill's manifest to ensure stability and security. For example, `{"mcp": ["rube@1.0.0"]}` or `{"mcp": ["rube@^1.0.0"]}` if versioning is supported for MCPs.	LLM	SKILL.md:1

Scan History

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