Security Audit
big-data-cloud-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
big-data-cloud-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 Capability 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 Capability via RUBE_REMOTE_WORKBENCH The skill describes the use of `RUBE_REMOTE_WORKBENCH` with `run_composio_tool()` for 'Bulk ops'. This suggests a powerful capability that could allow an agent to execute arbitrary Composio tools with potentially arbitrary arguments. If an attacker can manipulate the input to `run_composio_tool()`, they could leverage this to perform unauthorized actions, data manipulation, or exfiltration within the connected Big Data Cloud environment. The scope and sandboxing of `run_composio_tool()` are not defined within this skill description, making this a potential excessive permission risk. Ensure that `run_composio_tool()` within `RUBE_REMOTE_WORKBENCH` is strictly sandboxed, has fine-grained access controls, and validates all inputs to prevent arbitrary tool execution or command injection. Agents using this skill should be carefully constrained in their ability to generate arguments for this function. | LLM | SKILL.md:67 |
Scan History
Embed Code
[](https://skillshield.io/report/fc3aa7f0c25cdbc6)
Powered by SkillShield