Security Audit
api-labz-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
api-labz-automation received a trust score of 95/100, 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 Skill documentation encourages use of overly broad tool.
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 | Skill documentation encourages use of overly broad tool The skill's documentation for 'API Labz Automation' suggests using `RUBE_REMOTE_WORKBENCH` with `run_composio_tool()` for 'Bulk ops'. `RUBE_REMOTE_WORKBENCH` is a general-purpose tool execution mechanism within the Composio ecosystem, allowing the agent to execute arbitrary Composio tools. While intended for 'Bulk ops', this capability grants the agent permissions far beyond specific 'API Labz' operations, potentially enabling interaction with other toolkits or unintended actions if not properly constrained by the environment. If the skill's intent is strictly 'API Labz automation', consider removing or restricting the mention of `RUBE_REMOTE_WORKBENCH` in the documentation, or explicitly stating that its use should be limited to API Labz-specific operations. Ensure that the agent's environment or the Rube MCP configuration enforces granular permissions to prevent the agent from using `RUBE_REMOTE_WORKBENCH` to access unrelated toolkits or perform unauthorized actions. | LLM | SKILL.md:69 |
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