Kaggle Automation

What This Skill Does

This skill automates Kaggle operations using Composio's Kaggle toolkit through Rube MCP. It is designed for users who need to streamline their Kaggle workflows, such as data scientists and machine learning engineers. The skill ensures proper setup, tool discovery, and execution while avoiding common pitfalls.

When to Use

Automate dataset downloads. Submit predictions to Kaggle competitions. Manage Kaggle API connections. Discover available Kaggle tools. Check Kaggle connection status. Execute Kaggle tools with schema compliance.

Key Features

Automates Kaggle operations via Rube MCP.Uses Composio's Kaggle toolkit. Requires active Kaggle connection. Discovers tools using RUBE_SEARCH_TOOLS.Checks connection status with RUBE_MANAGE_CONNECTIONS.Executes tools with RUBE_MULTI_EXECUTE_TOOL.

Manual Installation

Manual installationView Full Skill ContentThe complete markdown content that gets installedKaggle Automation via Rube MCP Automate Kaggle operations through Composio's Kaggle toolkit via Rube MCP. Toolkit docs: composio.dev/toolkits/kaggle

Prerequisites

Rube MCP must be connected (RUBE_SEARCH_TOOLS available) Active Kaggle connection via RUBE_MANAGE_CONNECTIONS with toolkit kaggle 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 kaggle 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: "Kaggle 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 Kaggle task"}] session: {id: "existing_session_id"}

Step 2: Check Connection

RUBE_MANAGE_CONNECTIONS toolkits: ["kaggle"] 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

Prerequisites

Rube MCP must be connected (RUBE_SEARCH_TOOLS available) Active Kaggle connection via RUBE_MANAGE_CONNECTIONS with toolkit kaggle 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 kaggle 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: "Kaggle 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 Kaggle task"}] session: {id: "existing_session_id"}

Step 2: Check Connection

RUBE_MANAGE_CONNECTIONS toolkits: ["kaggle"] 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