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Security Audit

postiz

github.com/openclaw/skills
AI SkillCommit 13146e6a3d46
51
CAUTION
Scanned about 2 months ago
3
Critical
Immediate action required
4
High
Priority fixes suggested
2
Medium
Best practices review
2
Low
Acknowledged / Tracked

Trust Assessment

postiz received a trust score of 51/100, placing it in the Caution category. This skill has some security considerations that users should review before deployment.

SkillShield's automated analysis identified 11 findings: 3 critical, 4 high, 2 medium, and 2 low severity. Key findings include Suspicious import: requests, Hardcoded Credentials in Skill Code and Documentation, Hardcoded Credentials in Python Script.

The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. The LLM Behavioral Safety layer scored lowest at 0/100, indicating areas for improvement.

Last analyzed on February 12, 2026 (commit 13146e6a). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.

Layer Breakdown

Manifest Analysis
100%
Static Code Analysis
86%
Dependency Graph
100%
LLM Behavioral Safety
0%

Behavioral Risk Signals

Network Access
5 findings
Filesystem Write
7 findings
Shell Execution
5 findings
Excessive Permissions
8 findings

Security Findings11

SeverityFindingLayerLocation

Scan History

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