Security Audit
extracta-ai-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
extracta-ai-automation received a trust score of 82/100, placing it in the Mostly Trusted category. This skill has passed most security checks with only minor considerations noted.
SkillShield's automated analysis identified 2 findings: 0 critical, 1 high, 1 medium, and 0 low severity. Key findings include Skill grants broad execution capabilities via RUBE_REMOTE_WORKBENCH, Unpinned dependency on Rube MCP.
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 Findings2
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| HIGH | Skill grants broad execution capabilities via RUBE_REMOTE_WORKBENCH The skill documentation mentions `RUBE_REMOTE_WORKBENCH` for 'Bulk ops' with `run_composio_tool()`. A 'remote workbench' typically implies a powerful execution environment that could allow the LLM to execute arbitrary code or commands on the remote system, or to orchestrate complex operations with potentially broad access. This grants excessive permissions to the LLM, increasing the risk of unintended actions or command injection if the `run_composio_tool()` function can be manipulated to execute arbitrary code or commands. Clarify and restrict the capabilities of `RUBE_REMOTE_WORKBENCH` and `run_composio_tool()`. Ensure it operates within a sandboxed environment with minimal necessary permissions. Provide explicit examples of allowed operations and disallow arbitrary code execution. If arbitrary code execution is intended, clearly document the security implications and necessary safeguards. | LLM | SKILL.md:68 | |
| MEDIUM | Unpinned dependency on Rube MCP The skill's manifest specifies a dependency on 'rube' MCP (`"mcp": ["rube"]`) without a version constraint. This means that any future changes or vulnerabilities introduced in new versions of Rube MCP could automatically affect this skill without explicit review, posing a supply chain risk. An attacker could potentially introduce malicious code into a new version of the dependency, which would then be pulled by this skill. Pin the Rube MCP dependency to a specific version or version range in the `requires` field of the manifest to ensure stability and allow for controlled updates. Example: `"mcp": ["rube==1.2.3"]` or `"mcp": ["rube>=1.2.0,<2.0.0"]`. | LLM | SKILL.md:1 |
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