Security Audit

context-fundamentals

github.com/muratcankoylan/Agent-Skills-for-Context-Engineering

AI SkillCommit 7942df36cfca

TRUSTED

Scanned 8 days ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

context-fundamentals received a trust score of 93/100, placing it in the Trusted category. This skill has passed all critical security checks and demonstrates strong security practices.

SkillShield's automated analysis identified 1 finding: 0 critical, 0 high, 1 medium, and 0 low severity. Key findings include Unsafe deserialization / dynamic eval.

The analysis covered 4 layers: manifest_analysis, llm_behavioral_safety, dependency_graph, static_code_analysis. All layers scored 70 or above, reflecting consistent security practices.

Last analyzed on February 11, 2026 (commit 7942df36). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.

Layer Breakdown

Manifest Analysis

93%

Static Code Analysis

100%

Dependency Graph

100%

LLM Behavioral Safety

100%

Behavioral Risk Signals

Shell Execution

1 finding

Dynamic Code

1 finding

Security Findings1

Severity	Finding	Layer	Location
MEDIUM	Unsafe deserialization / dynamic eval Decryption followed by code execution Remove obfuscated code execution patterns. Legitimate code does not need base64-encoded payloads executed via eval, encrypted-then-executed blobs, or dynamic attribute resolution to call system functions.	Unknown	/var/folders/1k/67b8r20n777f_xcmmm8b7m5h0000gn/T/skillscan-clone-nsppidbi/repo/skills/context-fundamentals/scripts/context_manager.py:4

Scan History

Embed Code

[![SkillShield](https://skillshield.io/api/v1/badge/01813fd0e3dec6c8.svg)](https://skillshield.io/report/01813fd0e3dec6c8)