Security Audit

neutrino-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

neutrino-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, 0 high, 1 medium, and 0 low severity. Key findings include Broad tool execution 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

93%

Behavioral Risk Signals

Shell Execution

1 finding

Excessive Permissions

1 finding

Security Findings1

Severity	Finding	Layer	Location
MEDIUM	Broad tool execution via RUBE_REMOTE_WORKBENCH The skill instructs the LLM to use `RUBE_REMOTE_WORKBENCH` for 'Bulk ops' with `run_composio_tool()`. This suggests a highly flexible execution environment that could allow the LLM to invoke a wide range of Composio tools. If `run_composio_tool()` can execute arbitrary Composio tools, and its arguments can be influenced by untrusted input, it could lead to excessive permissions, unintended actions, or access to sensitive operations beyond the user's explicit intent or the skill's primary purpose. Clarify the exact scope and limitations of `run_composio_tool()` within `RUBE_REMOTE_WORKBENCH`. Implement strict input validation and authorization checks within `RUBE_REMOTE_WORKBENCH` to prevent arbitrary tool execution or access to unauthorized operations. Provide explicit guidance to the LLM on when and how to safely use this powerful tool, emphasizing user confirmation for sensitive operations. Consider if a more granular set of tools could achieve the 'Bulk ops' functionality without exposing such broad execution capabilities.	LLM	SKILL.md:60

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