$ ~/registry/skill/composiohq-composio-skills-big-data-cloud-automation

SKILL

big-data-cloud-automation

Name: big-data-cloud-automation
Author: Composio

Automate big data cloud tasks and operations through Rube MCP workflows.

Stars

★ 67,807

Source

GitHub

Updated

2026-07-15

// securityCaution

Prompt only, no code
Open source
Community-trusted· 64.7k

Summary

Overall, this is a prompt-only skill built around Rube MCP, with no declared local code execution, no required secrets, and no explicit business-specific remote endpoints. The main risk is its use of a third-party MCP connector to reach external services, so it is cautionary but does not show clear high-risk red flags from the provided material.

CredentialsLow risk

No API key or token is required by the skill itself; it only mentions connection setup via RUBE_MANAGE_CONNECTIONS. Based on the description, there is no direct credential exposure or abuse signal.

Network egressCaution

There is network egress via Rube MCP to external services, but no unknown endpoint or extra data recipient is shown. This is standard outbound connectivity for this class of tool.

Code executionCaution

The skill is prompt-only and does not describe spawning local processes or executing arbitrary code. It does invoke MCP tools to run workflows, which is controlled tool execution rather than local code execution.

Data accessCaution

It can access the connected Big Data Cloud resources and operation results, with scope determined by the remote tool permissions. The material does not show overbroad local file access or private-data reads/writes.

Supply chainCaution

The source is a GitHub open-source repository with strong community adoption (64.7k stars), which lowers supply-chain risk. However, the license is not stated and maintenance status is unknown, so it still warrants normal third-party caution.

Recommendations

Use only after confirming the trustworthiness of the Rube MCP and Big Data Cloud connection sources.
Check runtime behavior for extra endpoints, unexpected data egress, or tool calls beyond the description.
Apply least-privilege permissions to the connected account and review grants periodically.
If used for sensitive data workflows, validate behavior in a non-production environment first.

Audited by: gpt-5.4 · 2026-06-17

// install

Copy the install command and let the AI configure it · recommended for beginners

Please install the "big-data-cloud-automation" skill from askskill:
1. Download https://raw.githubusercontent.com/ComposioHQ/awesome-claude-skills/master/composio-skills/big-data-cloud-automation/SKILL.md
2. Save it as ~/.claude/skills/big-data-cloud-automation/SKILL.md
3. Reload skills and tell me it's ready

// download

Download SKILL.mdInstall manifest ↗

// docs

Big Data Cloud Automation via Rube MCP

Automate Big Data Cloud operations through Composio's Big Data Cloud toolkit via Rube MCP.

Toolkit docs: composio.dev/toolkits/big_data_cloud

Prerequisites

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

Step 2: Check Connection

RUBE_MANAGE_CONNECTIONS
toolkits: ["big_data_cloud"]
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 Big Data Cloud-specific use case
Connect	`RUBE_MANAGE_CONNECTIONS` with toolkit `big_data_cloud`
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`

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