Skip to main content

Security Audit

umea-data

github.com/openclaw/skills
AI SkillCommit 13146e6a3d46
66
CAUTION
Scanned about 2 months ago
0
Critical
Immediate action required
2
High
Priority fixes suggested
1
Medium
Best practices review
0
Low
Acknowledged / Tracked

Trust Assessment

umea-data received a trust score of 66/100, placing it in the Caution category. This skill has some security considerations that users should review before deployment.

SkillShield's automated analysis identified 3 findings: 0 critical, 2 high, 1 medium, and 0 low severity. Key findings include Missing required field: name, Command Injection via unsanitized dataset_id in nearby.sh, Command Injection via unsanitized dataset_id and limit in query.sh.

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

Manifest Analysis
100%
Static Code Analysis
93%
Dependency Graph
100%
LLM Behavioral Safety
70%

Behavioral Risk Signals

Network Access
2 findings
Shell Execution
2 findings
Dynamic Code
2 findings

Security Findings3

SeverityFindingLayerLocation

Scan History

Embed Code

[![SkillShield](https://skillshield.io/api/v1/badge/2567d14a9e6d1b5b.svg)](https://skillshield.io/report/2567d14a9e6d1b5b)
SkillShield Badge

Powered by SkillShield