Security Audit

fillout-forms-automation

github.com/ComposioHQ/awesome-claude-skills

AI SkillCommit 99e2a2951545

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, 1 high, 0 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

85%

Dependency Graph

100%

LLM Behavioral Safety

100%

Behavioral Risk Signals

Shell Execution

1 finding

Dynamic Code

1 finding

Security Findings1

Severity	Finding	Layer	Location
HIGH	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 into the 'rube' MCP could automatically affect this skill without explicit review or update, posing a significant supply chain risk. A malicious update to the 'rube' MCP could lead to data exfiltration, command injection, or other severe compromises. Pin the 'rube' MCP dependency to a specific, known-good version (e.g., `"rube": "^1.2.3"` or `"rube": "1.2.3"`) to ensure deterministic behavior and prevent unexpected changes from upstream. Regularly review and update the pinned version to incorporate necessary security patches and features.	Static	Manifest:1

Scan History

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