Security Audit

semanticscholar-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

semanticscholar-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 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 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

Shell Execution

1 finding

Security Findings1

Severity	Finding	Layer	Location
MEDIUM	Unpinned Rube MCP dependency The skill's manifest specifies a dependency on the 'rube' MCP without a specific version. This could lead to a supply chain risk where a malicious or vulnerable version of the Rube MCP could be loaded if the dependency resolution mechanism allows it. An attacker could potentially publish a malicious version of 'rube' that an agent might then fetch and execute, leading to various security compromises. Pin the Rube MCP dependency to a specific, known-good version in the manifest (e.g., `"rube@1.2.3"`) to ensure consistent and secure behavior.	LLM	manifest.json:1

Scan History

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