Security Audit

telegram-automation

github.com/sickn33/antigravity-awesome-skills

AI SkillCommit e36d6fd3b3f6

TRUSTED

Scanned about 21 hours ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

telegram-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, 0 medium, and 1 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 e36d6fd3). 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

98%

Security Findings1

Severity	Finding	Layer	Location
LOW	Unpinned MCP Dependency The skill's manifest specifies a dependency on the 'rube' MCP without a specific version or hash. This means the skill will always use the latest version of the Rube MCP, which could introduce breaking changes, vulnerabilities, or altered behavior if the MCP provider updates their service. Relying on unpinned dependencies can lead to unexpected behavior or security regressions. If possible, specify a version or a hash for the 'rube' MCP dependency in the manifest to ensure deterministic behavior and prevent unexpected changes from upstream updates. Consult the Rube MCP documentation for best practices on pinning dependencies.	LLM	SKILL.md:1

Scan History

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