Security Audit
hyperise-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
hyperise-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 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 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 on Rube MCP The skill manifest declares a dependency on the 'rube' MCP without specifying a version. This means the skill could automatically use any future version of the 'rube' MCP, including potentially malicious or incompatible updates, introducing a supply chain risk. If a compromised or malicious version of 'rube' were to be published, this skill would automatically adopt it, potentially leading to data exfiltration, unauthorized actions, or other security breaches. Pin the 'rube' MCP dependency to a specific, known-good version in the manifest (e.g., `{"mcp": ["rube@1.2.3"]}`) to ensure consistent and secure behavior. Regularly review and update pinned dependencies to incorporate security fixes. | Static | Manifest (frontmatter JSON) |
Scan History
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