$ ~/registry/skill/composiohq-composio-skills-bigml-automation

SKILL

bigml-automation

Name: bigml-automation
Author: Composio

通过 Rube MCP 自动执行 BigML 相关任务与机器学习工作流。

星标

★ 68,191

来源

GitHub

更新于

2026-07-21

// 安全评估需留意

仅提示词，不执行代码
开源可审计
社区验证· 64.7k

总评

整体看是一个纯提示词型技能，未声明密钥、无固定远程端点，且来源为高星开源仓库，当前未见明确高风险红旗。主要需要留意的是它依赖 Rube MCP 与 BigML 连接，实际执行时会发生常规的工具调用与数据处理。

凭证密钥低风险

材料未要求任何密钥或 token；仅说明通过 Rube MCP 管理 BigML 连接，且“无 API keys needed”。就凭证面看没有明确敏感凭证暴露或滥用点。

网络外发低风险

未给出固定业务外发端点；仅提到 Rube MCP 的已知端点与 Composio/BigML 相关联接，属于声明范围内的常规联机能力，未见把数据发往不明地址的红旗。

代码执行需留意

这是技能/工具编排型内容，本身不展示本机起进程或任意代码执行能力，但会调用 RUBE_SEARCH_TOOLS、RUBE_MANAGE_CONNECTIONS、RUBE_MULTI_EXECUTE_TOOL 等外部工具，属于常规工具执行风险。

数据访问需留意

数据访问范围主要取决于 BigML 连接与所选工具的具体 schema；README 还要求先搜索工具、再按 schema 执行，说明会读取/操作 BigML 侧资源，但未见超出声明功能的广泛本地文件访问。

来源供应链需留意

来源为 GitHub 开源仓库且社区采用度高（64.7k stars），这是正面信号；但许可证未声明、维护状态未知，且 README 依赖第三方 Rube/Composio 生态，供应链仍需按常规审慎核验。

安全建议

仅在需要时启用 Rube MCP，并确认 BigML 连接对象与权限范围。
执行前先核对 RUBE_SEARCH_TOOLS 返回的工具 schema，避免按过期文档调用。
对涉及数据导入/导出的 BigML 操作做最小权限与最小数据原则控制。
若用于生产环境，补充仓库许可证、维护活跃度与依赖审计。

审计模型: gpt-5.4 · 2026-06-17

// 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 "bigml-automation" 技能：
1. 下载 https://raw.githubusercontent.com/ComposioHQ/awesome-claude-skills/master/composio-skills/bigml-automation/SKILL.md
2. 保存为 ~/.claude/skills/bigml-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 ↗

// 文档

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`

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