Security Audit
owl-protocol-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
owl-protocol-automation received a trust score of 97/100, 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 Reliance on Broad Rube MCP Tool Permissions.
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 | |
|---|---|---|---|---|
| LOW | Reliance on Broad Rube MCP Tool Permissions The skill instructs the agent to use Rube MCP tools such as `RUBE_MANAGE_CONNECTIONS`, `RUBE_MULTI_EXECUTE_TOOL`, and `RUBE_REMOTE_WORKBENCH`. These tools inherently grant broad permissions, allowing the agent to manage connections, execute arbitrary tools, and perform bulk operations within the Composio ecosystem. While this is the intended functionality for a skill integrating with Rube MCP, it means the agent, when using this skill, will have access to these powerful capabilities. The security posture depends heavily on the agent's overall configuration and the trust placed in the Rube MCP system, rather than a specific vulnerability introduced by this skill. Ensure that the AI agent's access to Rube MCP and its underlying toolkits is strictly controlled and follows the principle of least privilege. Regularly audit the connections and tools available through Rube MCP. Users should be aware of the broad capabilities granted to agents utilizing Rube MCP. | Static | SKILL.md:1 |
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