Security Audit

statuscake-automation

github.com/ComposioHQ/awesome-claude-skills

AI SkillCommit 99e2a2951545

TRUSTED

Scanned about 2 months ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

statuscake-automation received a trust score of 90/100, 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, 1 high, 0 medium, and 0 low severity. Key findings include Access to highly flexible execution environment via RUBE_REMOTE_WORKBENCH.

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

Manifest Analysis

100%

Static Code Analysis

100%

Dependency Graph

100%

LLM Behavioral Safety

85%

Behavioral Risk Signals

Network Access

1 finding

Shell Execution

1 finding

Dynamic Code

1 finding

Excessive Permissions

1 finding

Security Findings1

Severity	Finding	Layer	Location
HIGH	Access to highly flexible execution environment via RUBE_REMOTE_WORKBENCH The skill documentation describes the use of `RUBE_REMOTE_WORKBENCH` for 'Bulk ops' with `run_composio_tool()`. This tool suggests a highly flexible and programmatic execution environment within the Rube MCP. If `run_composio_tool()` allows for arbitrary code execution or complex scripting, a malicious prompt could leverage this capability to perform command injection, access unauthorized resources, or exfiltrate data from the Rube environment, effectively bypassing typical API call constraints. While the skill itself does not contain malicious instructions, it exposes a powerful primitive that requires robust sandboxing and input validation by the Rube MCP system. Ensure that the `RUBE_REMOTE_WORKBENCH` and `run_composio_tool()` within Rube MCP are implemented with strict sandboxing, least privilege principles, and robust input validation to prevent arbitrary code execution or unauthorized access. The LLM's access to this tool should be carefully controlled and monitored. Consider if such a powerful tool is strictly necessary for the skill's intended purpose, or if more constrained alternatives exist.	LLM	SKILL.md:70

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