Security Audit
all-images-ai-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
all-images-ai-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 Dependency on external Rube MCP introduces supply chain risk.
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
Behavioral Risk Signals
Security Findings1
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| MEDIUM | Dependency on external Rube MCP introduces supply chain risk The skill explicitly declares a dependency on the 'rube' Managed Control Plane (MCP) within its manifest. This MCP is an external service, as indicated by the documentation pointing to `https://rube.app/mcp`. Reliance on external services introduces a supply chain risk, as a compromise of the external MCP provider or its infrastructure could directly impact the security and integrity of the skill's operations. The skill's functionality is entirely dependent on the security posture and availability of the Rube MCP. Review the security practices and audit reports of the Rube MCP provider. Implement strict input validation and output sanitization for all data exchanged with the MCP. Consider implementing robust error handling and fallback mechanisms. If possible, evaluate the feasibility of using a trusted, self-hosted, or open-source alternative, or explore options to pin to specific versions/hashes of the MCP if supported by the platform. | Static | Manifest |
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