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Security Audit

agentic-calling

github.com/openclaw/skills
AI SkillCommit 13146e6a3d46
37
CRITICAL
Scanned 2 months ago
1
Critical
Immediate action required
1
High
Priority fixes suggested
4
Medium
Best practices review
0
Low
Acknowledged / Tracked

Trust Assessment

agentic-calling received a trust score of 37/100, placing it in the Untrusted category. This skill has significant security findings that require attention before use in production.

SkillShield's automated analysis identified 7 findings: 1 critical, 1 high, 4 medium, and 0 low severity. Key findings include Missing required field: name, Sensitive environment variable access: $HOME, Command Injection via Python String Literal.

The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. The LLM Behavioral Safety layer scored lowest at 55/100, indicating areas for improvement.

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
72%
Dependency Graph
100%
LLM Behavioral Safety
55%

Behavioral Risk Signals

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

Security Findings7

SeverityFindingLayerLocation

Scan History

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