Security Audit

geoapify-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

geoapify-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, 0 high, 1 medium, and 0 low severity. Key findings include Unpinned MCP dependency.

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

93%

Security Findings1

Severity	Finding	Layer	Location
MEDIUM	Unpinned MCP dependency The skill manifest specifies a dependency on the 'rube' MCP without a pinned version. This could lead to unexpected behavior or security vulnerabilities if a breaking change or malicious update is introduced in a future version of the 'rube' MCP. Without version pinning, the skill might inadvertently use an incompatible or compromised version of the dependency. Pin the 'rube' MCP dependency to a specific version or version range (e.g., `"rube@^1.0.0"`) in the skill manifest to ensure stability and security against unexpected updates or supply chain attacks.	LLM	SKILL.md

Scan History

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