Security Audit
chatfai-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
chatfai-automation received a trust score of 86/100, 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 Explicit dependency on external 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 | |
|---|---|---|---|---|
| HIGH | Explicit dependency on external MCP introduces supply chain risk The skill explicitly requires and instructs the LLM to connect to an external Managed Control Plane (MCP) hosted at `https://rube.app/mcp`. The security and trustworthiness of this third-party service are critical. If `rube.app` were compromised or designed maliciously, it could lead to data exfiltration, command injection, or other attacks when the LLM executes tools provided by this MCP (e.g., `RUBE_MULTI_EXECUTE_TOOL`, `RUBE_REMOTE_WORKBENCH`). This introduces a significant supply chain risk as the skill's security is entirely dependent on an external entity. Review the trustworthiness and security posture of `https://rube.app/mcp`. Consider if the functionality can be achieved with local or more tightly controlled dependencies. Implement robust input validation and output sanitization for all interactions with the Rube MCP to mitigate potential downstream vulnerabilities. | LLM | SKILL.md:15 |
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