Security Audit

m365-agents-py

github.com/sickn33/antigravity-awesome-skills

AI SkillCommit e36d6fd3b3f6

TRUSTED

Scanned 2 days ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

m365-agents-py 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, 1 high, 0 medium, and 0 low severity. Key findings include Unpinned Dependencies in Installation Instructions.

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

85%

Security Findings1

Severity	Finding	Layer	Location
HIGH	Unpinned Dependencies in Installation Instructions The installation instructions recommend installing Python packages without specifying exact versions. This practice can lead to supply chain vulnerabilities, as future versions of these packages might introduce breaking changes, security flaws, or even malicious code. It also makes builds non-deterministic. Pin all dependencies to specific, known-good versions (e.g., `pip install package-name==1.2.3`). Consider using a dependency management tool like Poetry or Pipenv, or a `requirements.txt` file with pinned versions, to ensure deterministic and secure builds.	LLM	SKILL.md:26

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