Security Audit

drip-jobs-automation

github.com/ComposioHQ/awesome-claude-skills

AI SkillCommit 99e2a2951545

TRUSTED

Scanned 6 days ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

drip-jobs-automation 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 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 17, 2026 (commit 99e2a295). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.

Layer Breakdown

Manifest Analysis

100%

Static Code Analysis

93%

Dependency Graph

100%

LLM Behavioral Safety

100%

Security Findings1

Severity	Finding	Layer	Location
MEDIUM	Unpinned Dependency in Manifest The skill's manifest specifies a dependency on the 'rube' MCP without a version constraint. This means the skill will always use the latest available version of 'rube'. This introduces a supply chain risk as future updates to 'rube' could introduce breaking changes, vulnerabilities, or malicious code without explicit review or pinning, potentially affecting the skill's stability and security. Pin the 'rube' MCP dependency to a specific major or minor version (e.g., `"rube@^1.0.0"` or `"rube@1.2.3"`) to ensure consistent behavior and allow for controlled updates after security review.	Static	Manifest:1

Scan History

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