Security Audit

convertapi-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

convertapi-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 Skill enables use of highly privileged 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

Excessive Permissions

1 finding

Security Findings1

Severity	Finding	Layer	Location
HIGH	Skill enables use of highly privileged RUBE_REMOTE_WORKBENCH The skill documentation explicitly guides the agent to use `RUBE_REMOTE_WORKBENCH` with `run_composio_tool()` for 'Bulk ops'. This tool typically allows for the execution of arbitrary code or complex, multi-step operations within a remote environment, granting very broad capabilities. While this is part of the Rube MCP design, its inclusion in the skill's recommended usage pattern means the skill is designed to leverage a highly privileged function. Misuse or compromise of the agent could lead to significant impact, as the workbench could be used to perform actions beyond the intended scope of the Convertapi skill. Review the necessity of exposing `RUBE_REMOTE_WORKBENCH` for this specific skill. If not strictly required, consider removing its mention or providing stricter guidelines/guardrails for its use. Ensure the underlying `RUBE_REMOTE_WORKBENCH` implementation has robust security controls, sandboxing, and auditing to mitigate risks associated with arbitrary code execution.	LLM	SKILL.md:79

Scan History

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