Gan AI Automation

What This Skill Does

• This skill automates Gan AI operations using the Composio Gan AI toolkit through Rube MCP.
• It's designed for users looking to automate tasks within the Gan AI platform.
• The skill streamlines interaction with Gan AI by providing tools for connection management, tool discovery, and execution.

When to Use

• Automate Gan AI workflows.
• Discover available Gan AI tools.
• Check Gan AI connection status.
• Execute Gan AI tools with schema-compliant arguments.
• Integrate Gan AI operations into existing workflows.
• Dynamically adapt to Gan AI tool schema changes.

Key Features

• Connects to Gan AI via Rube MCP.
• Discovers available Gan AI tools and their schemas.
• Manages Gan AI connection status.
• Executes Gan AI tools with provided arguments.
• Provides a structured workflow pattern for Gan AI automation.
• Offers guidance on avoiding common pitfalls.

Manual Installation

• Manual installation.
• View Full Skill Content.
• The complete markdown content that gets installed.
• Gan AI Automation via Rube MCP.

Automate Gan AI operations through Composio's Gan AI toolkit via Rube MCP.

Toolkit docs: composio.dev/toolkits/gan_ai

Prerequisites

Rube MCP must be connected (RUBE_SEARCH_TOOLS available) Active Gan AI connection via RUBE_MANAGE_CONNECTIONS with toolkit gan_ai Always call RUBE_SEARCH_TOOLS first to get current tool schemas

Setup

• Get Rube MCP: Add https://rube.app/mcp as an MCP server in your client configuration.
• No API keys needed — just add the endpoint and it works.

Verify Rube MCP is available by confirming RUBE_SEARCH_TOOLS responds Call RUBE_MANAGE_CONNECTIONS with toolkit gan_ai If connection is not ACTIVE, follow the returned auth link to complete setup Confirm connection status shows ACTIVE before running any workflows

Tool Discovery

Always discover available tools before executing workflows:

RUBE_SEARCH_TOOLS queries: [{use_case: "Gan AI operations", known_fields: ""}] session: {generate_id: true}

This returns available tool slugs, input schemas, recommended execution plans, and known pitfalls.

Core Workflow Pattern

Step 1: Discover Available Tools

RUBE_SEARCH_TOOLS queries: [{use_case: "your specific Gan AI task"}] session: {id: "existing_session_id"}

Step 2: Check Connection

RUBE_MANAGE_CONNECTIONS toolkits: ["gan_ai"] session_id: "your_session_id"

Step 3: Execute Tools

RUBE_MULTI_EXECUTE_TOOL tools: [{ tool_slug: "TOOL_SLUG_FROM_SEARCH", arguments: {/ schema-compliant args from search results /} }] memory: {} session_id: "your_session_id"

Known Pitfalls

• Always search first: Tool schemas change.
• Never hardcode tool slugs or arguments without calling RUBE_SEARCH_TOOLS. Check connection: Verify RUBE_MANAGE_CONNECTIONS shows ACTIVE status before executing tools Schema compliance: Use exact field names and types from the search results Memory parameter: Always include memory in RUBE_MULTI_EXECUTE_TOOL calls, even if empty ({}) Session reuse: Reuse session IDs within a workflow. Generate new ones for new workflows Pagination: Check responses for pagination tokens and continue fetching until complete

Quick Reference

Powered by Composio. Gan AI Automation via Rube MCP

Automate Gan AI operations through Composio's Gan AI toolkit via Rube MCP.

Toolkit docs: composio.dev/toolkits/gan_ai

Prerequisites

Rube MCP must be connected (RUBE_SEARCH_TOOLS available) Active Gan AI connection via RUBE_MANAGE_CONNECTIONS with toolkit gan_ai Always call RUBE_SEARCH_TOOLS first to get current tool schemas

Setup

• Get Rube MCP: Add https://rube.app/mcp as an MCP server in your client configuration.
• No API keys needed — just add the endpoint and it works.

Verify Rube MCP is available by confirming RUBE_SEARCH_TOOLS responds Call RUBE_MANAGE_CONNECTIONS with toolkit gan_ai If connection is not ACTIVE, follow the returned auth link to complete setup Confirm connection status shows ACTIVE before running any workflows

Tool Discovery

Always discover available tools before executing workflows:

RUBE_SEARCH_TOOLS queries: [{use_case: "Gan AI operations", known_fields: ""}] session: {generate_id: true}

This returns available tool slugs, input schemas, recommended execution plans, and known pitfalls.

Core Workflow Pattern

Step 1: Discover Available Tools

RUBE_SEARCH_TOOLS queries: [{use_case: "your specific Gan AI task"}] session: {id: "existing_session_id"}

Step 2: Check Connection

RUBE_MANAGE_CONNECTIONS toolkits: ["gan_ai"] session_id: "your_session_id"

Step 3: Execute Tools

RUBE_MULTI_EXECUTE_TOOL tools: [{ tool_slug: "TOOL_SLUG_FROM_SEARCH", arguments: {/ schema-compliant args from search results /} }] memory: {} session_id: "your_session_id"

Known Pitfalls

• Always search first: Tool schemas change.
• Never hardcode tool slugs or arguments without calling RUBE_SEARCH_TOOLS. Check connection: Verify RUBE_MANAGE_CONNECTIONS shows ACTIVE status before executing tools Schema compliance: Use exact field names and types from the search results Memory parameter: Always include memory in RUBE_MULTI_EXECUTE_TOOL calls, even if empty ({}) Session reuse: Reuse session IDs within a workflow. Generate new ones for new workflows Pagination: Check responses for pagination tokens and continue fetching until complete

Quick Reference

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