Security Audit

calendarhero-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

calendarhero-automation received a trust score of 82/100, placing it in the Mostly Trusted category. This skill has passed most security checks with only minor considerations noted.

SkillShield's automated analysis identified 2 findings: 0 critical, 1 high, 1 medium, and 0 low severity. Key findings include Unpinned dependency 'rube' in manifest, Potential for arbitrary 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

78%

Behavioral Risk Signals

Shell Execution

1 finding

Dynamic Code

1 finding

Excessive Permissions

1 finding

Security Findings2

Severity	Finding	Layer	Location
HIGH	Potential for arbitrary tool execution via RUBE_REMOTE_WORKBENCH The skill documentation mentions `RUBE_REMOTE_WORKBENCH` with `run_composio_tool()` for 'Bulk ops'. This suggests the ability to execute arbitrary Composio tools within a remote workbench environment. If `run_composio_tool()` allows for the execution of any tool with arbitrary arguments, it could lead to command injection, privilege escalation, or access to resources beyond the intended scope of the Calendarhero automation. The lack of explicit constraints or sandboxing mechanisms described for `run_composio_tool()` raises significant security concerns. Clarify and restrict the capabilities of `RUBE_REMOTE_WORKBENCH` and `run_composio_tool()`. Implement strict input validation, allow-listing of executable tools/commands, and robust sandboxing to prevent arbitrary code execution or access to unauthorized resources. Provide clear documentation on the security implications and limitations of this tool.	LLM	SKILL.md:76
MEDIUM	Unpinned dependency 'rube' in manifest The skill's manifest specifies a dependency on 'rube' without a version constraint. This means the latest version of 'rube' will always be used, which could introduce unexpected behavior, breaking changes, or even malicious code if the 'rube' project is compromised. This makes the skill vulnerable to supply chain attacks. Pin the 'rube' dependency to a specific, known-good version (e.g., `{"mcp": ["rube@1.2.3"]}`) to ensure stability and security. Regularly review and update the pinned version.	LLM	SKILL.md:1

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