Security Audit

satismeter-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

satismeter-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 Excessive Permissions / 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

Excessive Permissions

1 finding

Security Findings1

Severity	Finding	Layer	Location
HIGH	Potential Excessive Permissions / Command Injection via RUBE_REMOTE_WORKBENCH The skill instructs the LLM to use `RUBE_REMOTE_WORKBENCH` with `run_composio_tool()` for 'Bulk ops'. The term 'workbench' often implies an environment capable of executing arbitrary code or complex operations. If `RUBE_REMOTE_WORKBENCH` or `run_composio_tool()` allows the execution of unvalidated commands or arbitrary code, it could lead to command injection, data exfiltration, or other severe security breaches. The skill exposes this powerful tool without sufficient context on its sandboxing or argument validation, making it a potential vector for an attacker to leverage the LLM to execute malicious commands. Clarify the exact capabilities and security boundaries of `RUBE_REMOTE_WORKBENCH` and `run_composio_tool()`. Ensure that this tool is properly sandboxed and that any arguments passed to `run_composio_tool()` are strictly validated against a whitelist of safe operations and schemas. If it allows arbitrary code execution, consider removing its exposure to the LLM or implementing strong guardrails and explicit user consent mechanisms for its use.	LLM	SKILL.md:63

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