Security Audit
helloleads-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
helloleads-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 Broad 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
Behavioral Risk Signals
Security Findings1
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| HIGH | Broad tool execution via RUBE_REMOTE_WORKBENCH The skill documentation indicates that `RUBE_REMOTE_WORKBENCH` can be used for 'Bulk ops' with `run_composio_tool()`. The naming `run_composio_tool()` suggests a generic capability to execute any Composio tool, not just those specific to Helloleads. If this interpretation is correct, an AI agent using this skill could gain excessively broad permissions, potentially allowing it to access or manipulate data in other connected systems via Composio, beyond the intended scope of Helloleads automation. Clarify the scope of `run_composio_tool()` when used with `RUBE_REMOTE_WORKBENCH`. If it is intended to be restricted to Helloleads tools, ensure this is enforced by the Rube MCP and explicitly stated in the documentation. If it is truly generic, consider if this skill should expose such a broad capability, or if a more granular tool should be used for Helloleads-specific bulk operations. | LLM | SKILL.md:73 |
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