Security Audit

anonyflow-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

anonyflow-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 capability via Rube MCP.

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 capability via Rube MCP The skill's primary function is 'Anonyflow Automation', yet it relies on generic Rube MCP tools like `RUBE_MULTI_EXECUTE_TOOL`. This tool allows the execution of any tool slug discovered via `RUBE_SEARCH_TOOLS`. While the examples provided focus on 'Anonyflow operations', the skill does not explicitly restrict the `tool_slug` parameter to only Anonyflow-related tools. This means an LLM using this skill could potentially be prompted to search for and execute tools from other toolkits available through Rube MCP, granting broader access than intended for a skill focused solely on Anonyflow. To mitigate this, consider implementing stricter controls within the skill's definition or the Rube MCP configuration to limit `RUBE_MULTI_EXECUTE_TOOL` to only accept `tool_slug` values explicitly associated with the 'anonyflow' toolkit. Additionally, ensure the LLM's prompt engineering strongly reinforces the intended scope of Anonyflow operations and includes guardrails against executing out-of-scope tools.	LLM	SKILL.md:49

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