Security Audit

ambee-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

ambee-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 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 MCP Dependency The skill's manifest specifies a dependency on the 'rube' MCP without a version constraint. This means the skill will always use the latest available version of the Rube MCP. Future updates to the Rube MCP could introduce breaking changes, security vulnerabilities, or malicious code, which would automatically affect this skill without explicit review or version pinning. This creates a supply chain risk. Specify a version constraint for the 'rube' MCP dependency in the manifest (e.g., `"rube": "^1.0.0"` or `"rube": "1.2.3"`) to ensure stability and allow for controlled updates after security review.	LLM	SKILL.md:5

Scan History

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