Security Audit

classmarker-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

classmarker-automation received a trust score of 93/100, 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 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

93%

Dependency Graph

100%

LLM Behavioral Safety

100%

Security Findings1

Severity	Finding	Layer	Location
MEDIUM	Unpinned MCP Dependency The skill manifest specifies a dependency on the 'rube' Managed Component Provider (MCP) without a version constraint. This means the skill will use whatever version of 'rube' is currently available, which could lead to unexpected behavior, breaking changes, or security vulnerabilities if a compromised or incompatible version of 'rube' is introduced. It's best practice to pin dependencies to specific versions or version ranges. Specify a version or version range for the 'rube' MCP dependency in the manifest to ensure stability and security. For example, `"mcp": ["rube@^1.0.0"]` or `"mcp": ["rube@1.2.3"]`.	Static	Manifest:4

Scan History

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