Security Audit
twelve-data-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
twelve-data-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, 0 high, 1 medium, and 0 low severity. Key findings include 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 Findings1
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| MEDIUM | Unpinned dependency on Rube MCP The skill's manifest specifies a dependency on the 'rube' MCP without a version constraint. This means that any update to the 'rube' MCP, including potentially malicious or incompatible changes, would be automatically adopted by this skill without explicit review. This introduces a supply chain risk where a compromised 'rube' MCP could lead to arbitrary code execution or data exfiltration through the skill's use of `RUBE_MULTI_EXECUTE_TOOL` or `RUBE_REMOTE_WORKBENCH`. Pin the 'rube' MCP dependency to a specific, trusted version (e.g., `"rube": ["rube@1.2.3"]`) in the skill's manifest to ensure stability and security. Regularly review and update the pinned version. | LLM | SKILL.md |
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