Security Audit
crustdata-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
crustdata-automation received a trust score of 98/100, 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, 0 medium, and 1 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
Security Findings1
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| LOW | 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. If the 'rube' MCP introduces breaking changes, security vulnerabilities, or malicious code in a future update, this skill could be negatively impacted without explicit action from the skill developer. This introduces a supply chain risk where the skill's behavior and security are entirely dependent on the upstream MCP's evolution. If the platform supports it, specify a version or version range for the 'rube' MCP dependency in the manifest to ensure stability and prevent unexpected behavior from future updates. Alternatively, implement robust error handling and validation for responses from the MCP. | LLM | SKILL.md |
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