Security Audit

fixer-automation

github.com/ComposioHQ/awesome-claude-skills

AI SkillCommit 27904475d127

TRUSTED

Scanned about 2 months ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

fixer-automation received a trust score of 85/100, placing it in the Mostly Trusted category. This skill has passed most security checks with only minor considerations noted.

SkillShield's automated analysis identified 1 finding: 0 critical, 1 high, 0 medium, and 0 low severity. Key findings include Unpinned Dependency in Manifest.

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 20, 2026 (commit 27904475). 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

85%

Security Findings1

Severity	Finding	Layer	Location
HIGH	Unpinned Dependency in Manifest The skill manifest declares a dependency on 'rube' without specifying a precise version or version range. This 'unpinned dependency' can lead to supply chain risks, as updates to 'rube' could introduce breaking changes, vulnerabilities, or malicious code without explicit review or control. This makes the skill vulnerable to changes in the 'rube' dependency. Specify a precise version or version range for the 'rube' dependency in the manifest (e.g., `"mcp": ["rube==1.2.3"]}` or `"mcp": ["rube>=1.0.0,<2.0.0"]`) to ensure deterministic and secure dependency resolution.	LLM	SKILL.md:5

Scan History

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