Security Audit
re-amaze-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
re-amaze-automation received a trust score of 94/100, 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, 1 medium, and 0 low severity. Key findings include Broad Tool Access via Rube MCP.
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 | |
|---|---|---|---|---|
| MEDIUM | Broad Tool Access via Rube MCP The skill's manifest explicitly requires access to the `rube` MCP tool. As described in the `SKILL.md`, the `rube` tool, particularly `RUBE_MULTI_EXECUTE_TOOL`, allows for the execution of arbitrary, schema-compliant operations on connected toolkits like Re Amaze. This grants the skill a very broad scope of action within the Re Amaze system, effectively allowing it to perform any operation that the Re Amaze toolkit supports. While this may be the intended functionality for a general automation skill, it represents a significant permission scope that could be misused if the LLM or the skill's execution context were compromised. Review the necessity of granting such broad access. If possible, consider if the `rube` tool can be configured with more granular permissions for specific Re Amaze operations, or if the skill's scope can be narrowed to only require specific sub-tools or actions within Re Amaze. Implement robust input validation and output sanitization for any data passed to or received from the `rube` tool to mitigate risks associated with broad access. | LLM | SKILL.md:1 |
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