Security Audit
gan-ai-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
gan-ai-automation received a trust score of 94/100, 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 Excessive Permissions for Gan AI Operations.
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
Behavioral Risk Signals
Security Findings1
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| MEDIUM | Excessive Permissions for Gan AI Operations The skill grants broad permissions to interact with the Gan AI toolkit via Rube MCP. It explicitly instructs the LLM to use `RUBE_SEARCH_TOOLS` to discover available tools and then `RUBE_MULTI_EXECUTE_TOOL` or `RUBE_REMOTE_WORKBENCH` (with `run_composio_tool()`) to execute any discovered Gan AI operation. This design allows the LLM to perform any action exposed by the Gan AI API, limited only by the permissions of the connected Gan AI account. While intended for automation, this broad access means a compromised or misaligned LLM could perform unintended or destructive operations within Gan AI without specific constraints defined within the skill itself. Consider implementing more granular control over which Gan AI operations the LLM is permitted to execute. Instead of dynamic discovery and execution of *any* tool, define a specific whitelist of allowed Gan AI tool slugs and their expected argument schemas. If broad automation is necessary, ensure robust guardrails and human oversight are in place for the LLM's outputs and actions. | LLM | SKILL.md:49 |
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