Security Audit

productlane-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

productlane-automation received a trust score of 76/100, 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: 1 critical, 0 high, 0 medium, and 0 low severity. Key findings include Excessive permissions and potential for arbitrary code 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

70%

Behavioral Risk Signals

Filesystem Write

1 finding

Shell Execution

1 finding

Dynamic Code

1 finding

Excessive Permissions

1 finding

Security Findings1

Severity	Finding	Layer	Location
CRITICAL	Excessive permissions and potential for arbitrary code execution via RUBE_REMOTE_WORKBENCH The skill recommends using `RUBE_REMOTE_WORKBENCH` with `run_composio_tool()` for 'Bulk ops'. A 'workbench' tool, especially one that can 'run_composio_tool()', typically implies an environment capable of executing arbitrary code or commands. If `run_composio_tool()` is not strictly sandboxed and its arguments are not thoroughly validated, it could be exploited for command injection, arbitrary file access, or data exfiltration. The skill encourages the use of this powerful tool without explicit warnings about its capabilities or potential security implications, thereby exposing the system to significant risk if the underlying tool is vulnerable. 1. Clarify the exact capabilities and security boundaries of `RUBE_REMOTE_WORKBENCH` and `run_composio_tool()`. 2. Implement strict input validation and robust sandboxing for `run_composio_tool()` to prevent arbitrary code execution or unauthorized system access. 3. Provide explicit warnings in the skill documentation about the power and potential risks of using `RUBE_REMOTE_WORKBENCH`. 4. Consider if such a powerful tool is necessary for the skill's stated purpose or if a more constrained alternative exists for 'Bulk ops'.	LLM	SKILL.md:91

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