Security Audit
zyte-api-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
zyte-api-automation received a trust score of 96/100, placing it in the Trusted category. This skill has passed all critical security checks and demonstrates strong security practices.
SkillShield's automated analysis identified 1 finding: 0 critical, 0 high, 1 medium, and 0 low severity. Key findings include Ambiguous `RUBE_REMOTE_WORKBENCH` tool suggests excessive permissions.
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 17, 2026 (commit 99e2a295). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.
Layer Breakdown
Behavioral Risk Signals
Security Findings1
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| MEDIUM | Ambiguous `RUBE_REMOTE_WORKBENCH` tool suggests excessive permissions The skill documentation lists `RUBE_REMOTE_WORKBENCH` with `run_composio_tool()` for 'Bulk ops'. The terms 'workbench' and 'bulk ops' typically imply a powerful execution environment that could potentially allow broad system access or arbitrary code execution if `run_composio_tool()` is not strictly sandboxed. While the exact implementation of `run_composio_tool()` is not visible in the provided content, the exposure of such a powerful and vaguely defined tool raises concerns about excessive permissions and potential command injection if an LLM agent were to provide malicious input. Provide clear documentation on the exact capabilities, limitations, and security boundaries of `RUBE_REMOTE_WORKBENCH` and `run_composio_tool()`. Ensure `run_composio_tool()` is strictly sandboxed to prevent arbitrary code execution, file system access, or other unintended operations, and that its scope is limited to the intended 'bulk operations' for Zyte API. | LLM | SKILL.md:70 |
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