Trust Assessment
resource-leveler received a trust score of 90/100, placing it in the Trusted category. This skill has passed all critical security checks and demonstrates strong security practices.
SkillShield's automated analysis identified 2 findings: 0 critical, 0 high, 1 medium, and 1 low severity. Key findings include Missing required field: name, Unpinned 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 14, 2026 (commit 13146e6a). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.
Layer Breakdown
Behavioral Risk Signals
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/datadrivenconstruction/resource-leveler/SKILL.md:1 | |
| LOW | Unpinned dependencies The `pip install` command specifies `pandas` and `numpy` without version pinning. This can lead to non-deterministic builds, unexpected behavior, or potential security vulnerabilities if a future version of a dependency introduces issues. While pandas and numpy are widely used and generally trusted, pinning versions is a best practice for supply chain security. Pin dependency versions (e.g., `pandas==2.2.0 numpy==1.26.0`) to ensure consistent and secure environments. Regularly review and update pinned versions. | LLM | SKILL.md:306 |
Scan History
Embed Code
[](https://skillshield.io/report/f7dc7edf9ca044d9)
Powered by SkillShield