Security Audit
talenthr-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
talenthr-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 Third-Party 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 Third-Party Dependency The skill's manifest declares a dependency on the 'rube' MCP (`"mcp": ["rube"]`) without specifying a version. This means the skill will always use the latest available version of the 'rube' MCP. If a future version of the 'rube' MCP introduces breaking changes, security vulnerabilities, or malicious behavior, the skill would automatically inherit these issues without explicit review or control. This lack of version pinning is a common supply chain risk. Specify a precise version or version range for the 'rube' MCP dependency in the `requires` section of the manifest (e.g., `"mcp": ["rube@1.2.3"]` or `"mcp": ["rube@^1.0.0"]`) to ensure stability and allow for controlled updates after security review. | Static | SKILL.md:4 |
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