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Security Audit

judge-llm

github.com/anton-abyzov/specweave
AI SkillCommit 1823c3f6cf4d
68
CAUTION
Scanned 5 days ago
0
Critical
Immediate action required
1
High
Priority fixes suggested
2
Medium
Best practices review
1
Low
Acknowledged / Tracked

Trust Assessment

judge-llm received a trust score of 68/100, placing it in the Caution category. This skill has some security considerations that users should review before deployment.

SkillShield's automated analysis identified 4 findings: 0 critical, 1 high, 2 medium, and 1 low severity. Key findings include Missing required field: name, Sensitive Code Sent to Third-Party LLM, Potential Command Injection via Bash Permission.

The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. The Static Code Analysis layer scored lowest at 69/100, indicating areas for improvement.

Last analyzed on February 15, 2026 (commit 1823c3f6). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.

Layer Breakdown

Manifest Analysis
100%
Static Code Analysis
69%
Dependency Graph
100%
LLM Behavioral Safety
100%

Behavioral Risk Signals

Network Access
1 finding
Filesystem Write
2 findings
Shell Execution
2 findings
Dynamic Code
2 findings
Excessive Permissions
2 findings

Security Findings4

SeverityFindingLayerLocation

Scan History

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