Security Audit
similarweb_digitalrank_api-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
similarweb_digitalrank_api-automation received a trust score of 88/100, 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 Potential Command Injection 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 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 | |
|---|---|---|---|---|
| HIGH | Potential Command Injection via RUBE_REMOTE_WORKBENCH The skill's documentation recommends using `RUBE_REMOTE_WORKBENCH` with `run_composio_tool()` in a loop with `ThreadPoolExecutor` for parallel execution. The explicit mention of `ThreadPoolExecutor` strongly implies the execution of arbitrary Python code within the `RUBE_REMOTE_WORKBENCH` environment. If this remote workbench is not strictly sandboxed or if the `run_composio_tool()` function can be manipulated to execute arbitrary commands, it presents a significant command injection vulnerability. While the skill itself does not provide malicious code, it instructs the LLM/user to utilize a mechanism that, if insecurely implemented or used, could lead to arbitrary code execution. Ensure the `RUBE_REMOTE_WORKBENCH` environment is strictly sandboxed and that `run_composio_tool()` only executes predefined, safe operations. Avoid recommending patterns that imply arbitrary code execution without explicit security guarantees and clear guidance on safe usage. If arbitrary code execution is an intended feature, clearly document the security implications and required sandboxing measures for users. | LLM | SKILL.md:82 |
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