Security Audit
Toggl Automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
Toggl 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 external MCP server 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 20, 2026 (commit 27904475). 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 external MCP server introduces supply chain risk The skill explicitly requires the 'Rube' MCP (Multi-Cloud Platform) server hosted at `https://rube.app/mcp` for its operation, as indicated in the manifest and setup instructions. This introduces a significant supply chain risk. The security and integrity of the skill's interactions with Toggl Track become dependent on the trustworthiness and security posture of this third-party service. A compromise or malicious change in `rube.app` could lead to data interception, manipulation, or unauthorized access to users' Toggl Track accounts. Evaluate the trustworthiness and security posture of `rube.app`. If possible, consider alternative integration methods that do not rely on an external, third-party intermediary, such as direct API integration or using an open-source/self-hostable MCP solution. Implement robust vetting and continuous monitoring for all external dependencies. | LLM | SKILL.md:14 |
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