Trust Assessment
job-search-mcp-jobspy 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 Python Dependencies.
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/nikkijasmine/job-search-mcp-jobspy/SKILL.md:1 | |
| MEDIUM | Unpinned Python Dependencies The skill's installation instructions specify Python packages without version pinning. This can lead to supply chain risks, as new versions of dependencies might introduce breaking changes, vulnerabilities, or unexpected behavior. It also makes builds non-deterministic and harder to reproduce securely. Pin all Python dependencies to specific versions (e.g., `package==1.2.3`) to ensure deterministic builds and mitigate risks from unexpected updates. Consider using a `requirements.txt` file with exact versions generated via `pip freeze > requirements.txt`. | LLM | SKILL.md:30 |
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