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Security Audit

context-engineering-collection

github.com/muratcankoylan/Agent-Skills-for-Context-Engineering
AI SkillCommit c578e85e40fe
10
CRITICAL
Scanned 30 days ago
23
Critical
Immediate action required
31
High
Priority fixes suggested
9
Medium
Best practices review
0
Low
Acknowledged / Tracked

Trust Assessment

context-engineering-collection 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 63 findings: 23 critical, 31 high, 9 medium, and 0 low severity. Key findings include Persistence / self-modification instructions, Network egress to untrusted endpoints, Unsafe environment variable passthrough.

The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. The Manifest Analysis layer scored lowest at 0/100, indicating areas for improvement.

Last analyzed on August 1, 2026 (commit c578e85e). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.

Layer Breakdown

Manifest Analysis
0%
Static Code Analysis
0%
Dependency Graph
72%
LLM Behavioral Safety
85%

Behavioral Risk Signals

Network Access
4 findings
Filesystem Write
2 findings
Shell Execution
37 findings
Dynamic Code
13 findings
Excessive Permissions
15 findings

Security Findings63

SeverityFindingLayerLocation

Scan History

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