Trust Assessment
event-planner received a trust score of 91/100, 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 Suspicious import: requests, Unpinned dependency version.
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
Behavioral Risk Signals
Security Findings2
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| MEDIUM | Suspicious import: requests Import of 'requests' detected. This module provides network or low-level system access. Verify this import is necessary. Network and system modules in skill code may indicate data exfiltration. | Static | skills/udiedrichsen/event-planner/scripts/plan_event.py:27 | |
| LOW | Unpinned dependency version The skill specifies a dependency (`requests`) with a minimum version (`>=2.31.0`) rather than a pinned or caret version. This can lead to unexpected behavior or introduce vulnerabilities if a future version of the dependency contains breaking changes or security flaws. While 'requests' is a widely used and trusted library, it's a best practice to pin dependencies to specific versions to ensure reproducibility and prevent unforeseen issues. Pin the dependency to a specific version (e.g., `requests==2.31.0`) or use a caret range (e.g., `requests~=2.31.0`) to allow for patch updates while maintaining stability. Regularly review and update dependencies. | LLM | scripts/plan_event.py:5 |
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