Do I need API keys to use this skill?

No. Get Rube MCP connected to the endpoint and use the Bigml toolkit through RUBE_MANAGE_CONNECTIONS; no API keys are required.

How do I ensure I’m using current tool schemas?

Always run RUBE_SEARCH_TOOLS first to fetch tool slugs and input schemas before any execution.

What if tool schemas change between runs?

Re-run RUBE_SEARCH_TOOLS to get the latest slugs and field definitions; do not hardcode tool slugs or arguments.

bigml-automation

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Bigml Automation via Rube MCP

Automate Bigml operations through Composio's Bigml toolkit via Rube MCP.

Toolkit docs: composio.dev/toolkits/bigml

Prerequisites

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

Step 2: Check Connection

RUBE_MANAGE_CONNECTIONS
toolkits: ["bigml"]
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

Operation	Approach
Find tools	`RUBE_SEARCH_TOOLS` with Bigml-specific use case
Connect	`RUBE_MANAGE_CONNECTIONS` with toolkit `bigml`
Execute	`RUBE_MULTI_EXECUTE_TOOL` with discovered tool slugs
Bulk ops	`RUBE_REMOTE_WORKBENCH` with `run_composio_tool()`
Full schema	`RUBE_GET_TOOL_SCHEMAS` for tools with `schemaRef`

Powered by Composio

Source

git clone https://github.com/ComposioHQ/awesome-claude-skills/blob/master/composio-skills/bigml-automation/SKILL.mdView on GitHub

Overview

Automate Bigml operations through Composio's Bigml toolkit via Rube MCP. This skill orchestrates discovery, connection management, and tool execution in a single workflow. It emphasizes always pulling current tool schemas to avoid stale slugs.

How This Skill Works

It starts by discovering available Bigml tools with RUBE_SEARCH_TOOLS, then verifies an active connection via RUBE_MANAGE_CONNECTIONS for the bigml toolkit, and finally executes the chosen tool with RUBE_MULTI_EXECUTE_TOOL using the discovered slug and schema-compliant arguments.

When to Use It

When you need to automate repetitive Bigml operations (deployments, predictions, dataset prep) using a defined workflow.
When tool schemas change frequently and you must fetch current slugs before execution.
When you must verify the Bigml connection is ACTIVE before running workflows.
When performing batch or bulk operations across multiple Bigml tools in a single run.
When you want to reuse a session ID within a workflow to optimize continuity and performance.

Quick Start

Step 1: Ensure MCP is connected and validate RUBE_SEARCH_TOOLS responds.
Step 2: Establish the Bigml connection with RUBE_MANAGE_CONNECTIONS and toolkit bigml.
Step 3: Discover tools with RUBE_SEARCH_TOOLS and execute a chosen tool with RUBE_MULTI_EXECUTE_TOOL using the discovered slug and memory.

Best Practices

Always call RUBE_SEARCH_TOOLS before executing any tool to fetch current schemas.
Verify RUBE_MANAGE_CONNECTIONS shows ACTIVE status before running workflows.
Use exact field names and types from the tool schemas returned by RUBE_SEARCH_TOOLS.
Always include a memory parameter in RUBE_MULTI_EXECUTE_TOOL calls, even if empty (memory: {}).
Reuse session IDs within a workflow; generate new ones only for separate workflows.

Example Use Cases

Automate a model deployment and scoring workflow by discovering tools, connecting to Bigml, and executing the scoring tool in sequence.
Run batch predictions across multiple datasets by discovering available prediction tools and executing them in a single session.
Update multiple datasets and re-evaluate models in one streamlined workflow with proper memory handling.
Perform bulk transformations across several Bigml tools in a single orchestration, reducing manual steps.
Complete consecutive Bigml tasks by reusing a session ID, then starting a new workflow only when needed.

Frequently Asked Questions

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