Security Audit
repairshopr-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
repairshopr-automation received a trust score of 94/100, 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 MCP dependency.
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 MCP dependency The skill's manifest specifies a dependency on the 'rube' Multi-Cloud Platform (MCP) without a version constraint. This means that any future updates to the 'rube' MCP, including potentially malicious or breaking changes, would be automatically incorporated without explicit review, posing a supply chain risk. An attacker could potentially introduce a malicious version of the 'rube' MCP that the skill would then unknowingly use. Pin the 'rube' MCP dependency to a specific version or version range to ensure predictable and reviewed behavior. For example, `{"mcp": ["rube@1.2.3"]}` or `{"mcp": ["rube@^1.0.0"]}`. | LLM | Manifest (frontmatter JSON) |
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