Security Audit

paradym-automation

github.com/ComposioHQ/awesome-claude-skills

AI SkillCommit 27904475d127

CAUTION

Scanned about 2 months ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

paradym-automation received a trust score of 73/100, placing it in the Caution category. This skill has some security considerations that users should review before deployment.

SkillShield's automated analysis identified 2 findings: 0 critical, 2 high, 0 medium, and 0 low severity. Key findings include Unpinned external service dependency, Dynamic tool schemas from external source.

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

70%

Behavioral Risk Signals

Network Access

2 findings

Shell Execution

1 finding

Dynamic Code

1 finding

Security Findings2

Severity	Finding	Layer	Location
HIGH	Unpinned external service dependency The skill relies on an external MCP server (`rube.app/mcp`) without specifying a version or hash. This means the skill's behavior is entirely dependent on the current state of the external service. A compromise or malicious change to `rube.app` could directly impact the security and functionality of this skill without any explicit update or review. Implement version pinning or content hashing for external service dependencies where possible. If direct version pinning is not feasible for an MCP endpoint, consider implementing integrity checks or requiring explicit approval for updates to the external service.	LLM	SKILL.md:20
HIGH	Dynamic tool schemas from external source The skill's operational logic, including tool slugs and argument schemas, is dynamically fetched at runtime from an external service (`RUBE_SEARCH_TOOLS`). This means the actual behavior of the `RUBE_MULTI_EXECUTE_TOOL` can be altered by the external service without any review or update to the skill package itself. A compromised `RUBE_SEARCH_TOOLS` endpoint could provide malicious schemas, leading to arbitrary actions or data exfiltration. Implement mechanisms to validate or pin the schemas returned by `RUBE_SEARCH_TOOLS`. Consider requiring explicit approval for schema changes or using a trusted, versioned schema registry. Limit the scope of actions that can be performed by dynamically loaded tools.	LLM	SKILL.md:68

Scan History

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