Security Audit

reasoning-trace-optimizer

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

AI SkillCommit 7942df36cfca

TRUSTED

Scanned about 2 months ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

reasoning-trace-optimizer 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 Unpinned Python dependency version.

The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. 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

100%

Static Code Analysis

100%

Dependency Graph

93%

LLM Behavioral Safety

100%

Security Findings1

Severity	Finding	Layer	Location
MEDIUM	Unpinned Python dependency version Dependency 'anthropic>=0.40.0' is not pinned to an exact version. Pin Python dependencies with exact versions where feasible.	Dependencies	examples/interleaved_thinking/pyproject.toml

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