Security Audit
apilio-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
apilio-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 Excessive Permissions via Generic Rube MCP Tools.
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 | Excessive Permissions via Generic Rube MCP Tools The skill, while described for 'Apilio Automation', instructs the LLM to use generic Rube MCP tools such as `RUBE_SEARCH_TOOLS`, `RUBE_MANAGE_CONNECTIONS`, `RUBE_MULTI_EXECUTE_TOOL`, and `RUBE_REMOTE_WORKBENCH`. These tools grant the LLM the ability to discover, manage connections for, and execute *any* tool available through the Rube MCP, not just those related to Apilio. This broad access means a malicious prompt could instruct the LLM to interact with other connected services (e.g., CRM, email, cloud providers) if they are also integrated via Rube MCP, bypassing the intended scope of the skill. If possible, restrict the Rube MCP tools available to this skill to only those directly related to Apilio. For example, if Rube MCP allows for tool-specific permissions, configure the skill to only access Apilio-specific tools. Alternatively, ensure that the LLM's internal instructions strictly limit the use of these generic Rube tools to Apilio-related operations only, even if the underlying tools are broader. | Static | SKILL.md:39 |
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