Security Audit

better-proposals-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

better-proposals-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 Potential Command Injection / Excessive Permissions 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

Shell Execution

1 finding

Dynamic Code

1 finding

Excessive Permissions

1 finding

Security Findings1

Severity	Finding	Layer	Location
HIGH	Potential Command Injection / Excessive Permissions via RUBE_REMOTE_WORKBENCH The skill instructs the LLM to use `RUBE_REMOTE_WORKBENCH` which explicitly mentions `run_composio_tool()`. A 'workbench' tool combined with a generic 'run_tool' function suggests the capability to execute arbitrary code or complex operations within the Composio environment. Without explicit sandboxing or strict input validation defined within the skill, this presents a high risk of command injection or excessive permissions. An attacker could potentially manipulate the arguments passed to `RUBE_REMOTE_WORKBENCH` to execute unintended commands or access unauthorized resources. The skill documentation should clearly state the security boundaries and input validation mechanisms of `RUBE_REMOTE_WORKBENCH` and `run_composio_tool()`. Ideally, direct arbitrary code execution capabilities should be avoided or heavily restricted. If `run_composio_tool()` is intended for specific, pre-defined operations, this should be made explicit, and inputs should be strictly validated against those definitions. The underlying Rube MCP system should enforce robust sandboxing and least-privilege principles for such powerful tools.	LLM	SKILL.md:49

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