Trust Assessment
moltauth received a trust score of 87/100, placing it in the Mostly Trusted category. This skill has passed most security checks with only minor considerations noted.
SkillShield's automated analysis identified 2 findings: 0 critical, 0 high, 2 medium, and 0 low severity. Key findings include Missing required field: name, Unpinned dependency in installation instructions.
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 13, 2026 (commit 13146e6a). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.
Layer Breakdown
Security Findings2
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| MEDIUM | Missing required field: name The 'name' field is required for claude_code skills but is missing from frontmatter. Add a 'name' field to the SKILL.md frontmatter. | Static | skills/bhoshaga/moltauth/skill.md:1 | |
| MEDIUM | Unpinned dependency in installation instructions The installation instructions for the `moltauth` package do not specify a version (e.g., `pip install moltauth` and `npm install moltauth`). This practice can lead to supply chain vulnerabilities where a future malicious or buggy update to the `moltauth` package could be automatically installed, potentially introducing security risks or breaking changes into the agent's environment. It is best practice to pin dependencies to specific versions to ensure deterministic and secure builds. Advise users to pin the dependency to a specific version (e.g., `pip install moltauth==1.0.0` or `npm install moltauth@1.0.0`) and utilize a lock file (`requirements.txt`, `package-lock.json`) to ensure deterministic and secure builds. | LLM | skill.md:19 |
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