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Security Audit

jami

github.com/openclaw/skills
AI SkillCommit 13146e6a3d46
58
CAUTION
Scanned about 2 months ago
0
Critical
Immediate action required
4
High
Priority fixes suggested
1
Medium
Best practices review
0
Low
Acknowledged / Tracked

Trust Assessment

jami received a trust score of 58/100, placing it in the Caution category. This skill has some security considerations that users should review before deployment.

SkillShield's automated analysis identified 5 findings: 0 critical, 4 high, 1 medium, and 0 low severity. Key findings include Sensitive environment variable access: $HOME, Command Injection in jami_caller.sh via user-supplied arguments, Command Injection in jami_listener.sh via LOG_FILE argument.

The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. The LLM Behavioral Safety layer scored lowest at 40/100, 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
93%
Dependency Graph
100%
LLM Behavioral Safety
40%

Behavioral Risk Signals

Filesystem Write
2 findings
Shell Execution
5 findings
Dynamic Code
4 findings
Excessive Permissions
1 finding

Security Findings5

SeverityFindingLayerLocation

Scan History

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