Security Audit
qualaroo-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
qualaroo-automation received a trust score of 85/100, placing it in the Mostly Trusted category. This skill has passed most security checks with only minor considerations noted.
SkillShield's automated analysis identified 1 finding: 0 critical, 1 high, 0 medium, and 0 low severity. Key findings include Reliance on unverified third-party MCP service.
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 | |
|---|---|---|---|---|
| HIGH | Reliance on unverified third-party MCP service The skill explicitly relies on the 'Rube MCP' service hosted at `https://rube.app/mcp` for all its functionality, as indicated in the setup instructions and the manifest's `requires` section. This introduces a significant supply chain risk, as the security and integrity of the skill's operations are entirely dependent on the trustworthiness and security posture of this external service. A compromise of `rube.app` or the Qualaroo toolkit provided by Composio could lead to unauthorized access to Qualaroo data or the execution of arbitrary actions within Qualaroo via the `RUBE_MULTI_EXECUTE_TOOL`. Evaluate the security posture and trustworthiness of `rube.app` and Composio. Consider implementing additional validation or sandboxing mechanisms for responses from the Rube MCP, if possible. Ensure that the LLM's permissions are scoped to only what is necessary for interacting with the Rube MCP and Qualaroo, and that any sensitive data handled by Qualaroo is appropriately protected. | Static | SKILL.md:20 |
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