Security Audit

thanks-io-automation

github.com/ComposioHQ/awesome-claude-skills

AI SkillCommit 99e2a2951545

TRUSTED

Scanned 6 days ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

thanks-io-automation received a trust score of 95/100, placing it in the Trusted category. This skill has passed all critical security checks and demonstrates strong security practices.

SkillShield's automated analysis identified 1 finding: 0 critical, 0 high, 1 medium, and 0 low severity. Key findings include Skill enables broad, dynamic tool execution via Rube MCP.

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

100%

Dependency Graph

100%

LLM Behavioral Safety

93%

Behavioral Risk Signals

Network Access

1 finding

Shell Execution

1 finding

Dynamic Code

1 finding

Excessive Permissions

1 finding

Security Findings1

Severity	Finding	Layer	Location
MEDIUM	Skill enables broad, dynamic tool execution via Rube MCP The skill instructs the LLM to use `RUBE_MULTI_EXECUTE_TOOL` and `RUBE_REMOTE_WORKBENCH`. These tools allow for dynamic discovery and execution of any available tool within the `thanks_io` toolkit via Rube MCP. This grants the LLM broad access to all functionalities exposed by the `thanks_io` toolkit, potentially including sensitive operations, without explicit enumeration or restriction within the skill definition. The `RUBE_REMOTE_WORKBENCH` for 'Bulk ops' further suggests powerful, potentially unconstrained operations, which could lead to unintended actions if the LLM misinterprets a request or if the underlying toolkit has sensitive functionalities. If the intended scope of automation is narrower, consider if the `thanks_io` toolkit can be configured with more granular permissions, or if the skill can be designed to only call specific, pre-approved tool slugs rather than relying on dynamic discovery and execution of any tool. Ensure that the `thanks_io` toolkit itself adheres to the principle of least privilege and that the LLM's access is appropriately constrained.	LLM	SKILL.md:50

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