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Security Audit

molt-chess

github.com/openclaw/skills
AI SkillCommit 13146e6a3d46
10
CRITICAL
Scanned 2 months ago
2
Critical
Immediate action required
2
High
Priority fixes suggested
5
Medium
Best practices review
0
Low
Acknowledged / Tracked

Trust Assessment

molt-chess received a trust score of 10/100, placing it in the Untrusted category. This skill has significant security findings that require attention before use in production.

SkillShield's automated analysis identified 9 findings: 2 critical, 2 high, 5 medium, and 0 low severity. Key findings include Arbitrary command execution, Suspicious import: requests, Sensitive environment variable access: $HOME.

The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. The LLM Behavioral Safety layer scored lowest at 33/100, 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
70%
Static Code Analysis
86%
Dependency Graph
86%
LLM Behavioral Safety
33%

Behavioral Risk Signals

Network Access
6 findings
Filesystem Write
1 finding
Shell Execution
5 findings
Dynamic Code
3 findings
Excessive Permissions
3 findings

Security Findings9

SeverityFindingLayerLocation

Scan History

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