Security Audit

lever-sandbox-automation

github.com/ComposioHQ/awesome-claude-skills

AI SkillCommit 99e2a2951545

TRUSTED

Scanned about 2 months ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

lever-sandbox-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 declares a dependency on the 'rube' MCP without specifying a version. This 'unpinned dependency' introduces a supply chain risk, as a malicious or vulnerable version of the 'rube' MCP could be introduced and automatically used, compromising the skill's security. It's crucial to pin dependencies to specific, known-good versions to prevent unexpected behavior or security vulnerabilities from upstream changes. Pin the 'rube' MCP dependency to a specific, known-good version (e.g., `"rube@1.2.3"`) to ensure deterministic behavior and mitigate supply chain risks. Regularly review and update pinned dependencies.	Static	Manifest:1

Scan History

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