Trust Assessment
scenario-planner 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 dependencies 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 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/scenario-planner/SKILL.md:1 | |
| MEDIUM | Unpinned dependencies in installation instructions The `pip install` command specifies `pandas` and `numpy` without version pinning. This can lead to non-reproducible environments, unexpected behavior due to API changes in newer versions, or the installation of a malicious package if a future version is compromised through a supply chain attack. Pin dependencies to specific versions (e.g., `pandas==2.1.4 numpy==1.26.3`) or use a `requirements.txt` file with pinned versions to ensure reproducible and secure installations. | LLM | SKILL.md:196 |
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