Security Audit

convolo-ai-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

convolo-ai-automation received a trust score of 94/100, 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 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 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

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 version constraint. This means that any future changes or vulnerabilities introduced in the `rube` MCP could automatically affect this skill without explicit review or update, posing a supply chain risk. If the `rube` MCP introduces breaking changes or security flaws, this skill could become unstable or vulnerable. Specify a version constraint for the `rube` MCP dependency in the manifest to ensure stability and allow for controlled updates. For example, `"mcp": ["rube@^1.0.0"]` if versioning is supported.	LLM	SKILL.md:4

Scan History

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