Security Audit

fillout-forms-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

fillout-forms-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 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 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 in manifest The skill's manifest specifies a dependency on the 'rube' MCP (`"mcp": ["rube"]`) without pinning a specific version. This means the skill could implicitly use any future version of the 'rube' MCP, which might introduce breaking changes, vulnerabilities, or unexpected behavior without explicit review or testing. Pin the 'rube' MCP dependency to a specific, known-good version in the `requires` section of the manifest. For example, `"mcp": ["rube@1.2.3"]` or similar versioning scheme if supported by the ecosystem, to ensure consistent and secure behavior.	LLM	SKILL.md:4

Scan History

Embed Code

[![SkillShield](https://skillshield.io/api/v1/badge/10c19bd4c4a3ee8e.svg)](https://skillshield.io/report/10c19bd4c4a3ee8e)