Security Audit
factorial-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
factorial-automation received a trust score of 93/100, 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 Unpinned Dependency in Manifest.
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
Security Findings1
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| MEDIUM | Unpinned Dependency in Manifest The skill's manifest declares a dependency on 'rube' within the 'mcp' category without specifying a version. This 'unpinned' dependency means that any future updates to the 'rube' MCP, including potentially malicious or breaking changes, could be automatically incorporated without explicit review. This introduces a supply chain risk, as the skill's behavior could change unexpectedly or become compromised if the 'rube' dependency is altered. Specify a precise version or version range for the 'rube' dependency in the 'requires' field of the manifest. For example, `"mcp": ["rube@1.2.3"]` or `"mcp": ["rube@^1.2.0"]` to ensure predictable and secure dependency resolution. | Static | SKILL.md:4 |
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