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Security Audit

memdata

github.com/openclaw/skills
AI SkillCommit 13146e6a3d46
65
CAUTION
Scanned about 2 months ago
1
Critical
Immediate action required
2
High
Priority fixes suggested
0
Medium
Best practices review
0
Low
Acknowledged / Tracked

Trust Assessment

memdata received a trust score of 65/100, placing it in the Caution category. This skill has some security considerations that users should review before deployment.

SkillShield's automated analysis identified 3 findings: 1 critical, 2 high, 0 medium, and 0 low severity. Key findings include Skill requires direct wallet access and transaction signing, Skill enables ingestion of arbitrary data to external service, Skill requires handling of sensitive UCAN delegation token.

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
100%
Dependency Graph
100%
LLM Behavioral Safety
40%

Behavioral Risk Signals

Network Access
2 findings
Filesystem Write
1 finding
Excessive Permissions
3 findings

Security Findings3

SeverityFindingLayerLocation

Scan History

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