Security Audit

expofp-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

expofp-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 external 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%

Behavioral Risk Signals

Network Access

1 finding

Security Findings1

Severity	Finding	Layer	Location
MEDIUM	Unpinned external dependency The skill manifest declares a dependency on the 'rube' MCP without specifying a version. This means that the skill could automatically use any version of the 'rube' MCP, including future versions that might introduce breaking changes, vulnerabilities, or malicious code. Relying on unpinned dependencies increases the supply chain risk. Specify a precise version or version range for the 'rube' MCP dependency in the skill's manifest to ensure stability and security. For example, `{"mcp": ["rube@1.2.3"]}` or `{"mcp": ["rube@^1.0.0"]}`.	LLM	SKILL.md:1

Scan History

Embed Code

[![SkillShield](https://skillshield.io/api/v1/badge/5527f467932ce524.svg)](https://skillshield.io/report/5527f467932ce524)