big-data-cloud-automation

帮助用户通过 Rube MCP 自动化大数据云平台任务与运维流程。

来源

GitHub

更新于

2026-06-07

// 安全评估低风险

仅提示词，不执行代码
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正在进行安全审计…

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代码执行
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// 安装

复制安装指令，让 AI 自动完成配置 · 推荐新手

请帮我安装 askskill 上的 "big-data-cloud-automation" 技能：
1. 下载 https://raw.githubusercontent.com/ComposioHQ/awesome-claude-skills/master/composio-skills/big-data-cloud-automation/SKILL.md
2. 保存为 ~/.claude/skills/big-data-cloud-automation/SKILL.md
3. 装好后重载技能，告诉我可以用了

// 下载

下载 SKILL.md机读安装清单 ↗

// 用法示例

自动化集群运维

输入

先搜索可用工具及当前 schema，然后用 Rube MCP 帮我自动化大数据云集群的日常运维，包括查看集群状态、重启失败服务，并输出执行结果摘要。

预期产出

返回已识别的工具、执行步骤、运维结果与异常摘要。

批量管理数据任务

输入

先搜索工具和最新 schema，再通过 Rube MCP 批量检查大数据云中的数据处理任务状态，重试失败任务，并生成处理结果清单。

预期产出

输出任务状态汇总、重试结果和最终任务清单。

创建自动化工作流

输入

先搜索可用工具与 schema，帮我设计一个通过 Rube MCP 执行的大数据云自动化工作流：定时检查资源使用率，超阈值时发送告警并触发扩容。

预期产出

提供可执行的工作流方案、触发条件和自动化动作说明。

// 文档

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`