Security Audit

textit-automation

github.com/ComposioHQ/awesome-claude-skills

AI SkillCommit 27904475d127

TRUSTED

Scanned about 2 months ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

textit-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 Potential Command Injection 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 20, 2026 (commit 27904475). 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

Shell Execution

1 finding

Dynamic Code

1 finding

Security Findings1

Severity	Finding	Layer	Location
HIGH	Potential Command Injection via RUBE_REMOTE_WORKBENCH The skill documentation references `RUBE_REMOTE_WORKBENCH` with `run_composio_tool()`. The term 'workbench' and the function name 'run_composio_tool()' strongly suggest the capability to execute arbitrary code or scripts within a remote environment. If an attacker can control the input or arguments to `run_composio_tool()`, this could lead to command injection, allowing them to execute arbitrary commands, exfiltrate data, or manipulate the environment. The skill documentation does not specify any sandboxing, input validation, or restrictions on what `run_composio_tool()` can execute, making this a significant potential vulnerability. Clarify the exact capabilities and security model of `RUBE_REMOTE_WORKBENCH` and `run_composio_tool()`. If it allows arbitrary code execution, implement strict sandboxing, robust input validation, and privilege separation to prevent malicious code execution. If it's not intended for arbitrary code execution, rename or re-document the function to accurately reflect its limited capabilities and avoid misinterpretation. Provide explicit examples of safe usage and clearly state any limitations or security controls in place.	LLM	SKILL.md:80

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