Trust Assessment
felt-automation received a trust score of 95/100, 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 Reliance 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 | Reliance on external Rube MCP introduces supply chain risk The skill explicitly requires and instructs the LLM to connect to `https://rube.app/mcp` as its MCP server. This introduces a supply chain risk as the functionality and security of the skill are entirely dependent on the integrity and trustworthiness of the `rube.app` service. If `rube.app` were compromised or malicious, the LLM could be directed to perform harmful actions through the Rube MCP, effectively inheriting the security posture of this external dependency. Verify the trustworthiness and security practices of `rube.app`. Consider implementing mechanisms to validate the integrity of the MCP server or using self-hosted alternatives if available. Clearly document the security implications of relying on this external service for users of the skill. | LLM | SKILL.md:20 |
Scan History
Embed Code
[](https://skillshield.io/report/24de6df455ea90ce)
Powered by SkillShield