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ai-ml-api-automation

@openteams-lab · 收录于 昨天 · 上游提交 2 天前

Automate AI ML API tasks via Rube MCP (Composio). Always search tools first for current schemas.

场景ai模型调用api自动化

适合你，如果你频繁调用AI API并希望自动执行任务

/ 下载安装
https://github.com/openteams-lab/openteams/tree/main/assets/skills/ai-ml-api-automation ↗
/ 通过 npx 安装 校验哈希
npx oh-my-skill add openteams-lab/openteams/ai-ml-api-automation
/ 通过 bash 安装
curl -fsSL https://oh-my-skill.com/install.sh | bash -s -- openteams-lab/openteams/ai-ml-api-automation
/ 已经装过?验证本机副本,不用重装
npx oh-my-skill verify openteams-lab/openteams/ai-ml-api-automation
安装目标可用 --agent / --scope 或 --to 明确指定;省略时只会在唯一已存在的 agent 目录上自动选择,零命中或多命中会停止并提示。content_hash 缺失或不一致均拒装。
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怎么用

商店整理自技能原文 · 版本 808a325 · 表述以原文为准
它做什么

装上后，Claude 能通过 Rube MCP 自动执行 AI/ML API 任务。它会先搜索最新工具模式，再按照正确参数调用 API。

什么时候触发

当你要求执行 AI 或机器学习 API 任务（如文本分类、图像分析等），并且已配置 Rube MCP 连接时触发。

装好后可以这样说
Claude 会搜索工具并执行分类任务。
返回当前可用的工具列表和参数。
确认连接是否生效，否则指导你完成授权。
技能原文 SKILL.md作者撰写 · Apache-2.0 · 808a325

AI ML API Automation via Rube MCP

Automate AI ML API operations through Composio's AI ML API toolkit via Rube MCP.

Toolkit docs: composio.dev/toolkits/ai_ml_api

Prerequisites
  • Rube MCP must be connected (RUBE_SEARCH_TOOLS available)
  • Active AI ML API connection via RUBE_MANAGE_CONNECTIONS with toolkit ai_ml_api
  • 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.

  1. Verify Rube MCP is available by confirming RUBE_SEARCH_TOOLS responds
  2. Call RUBE_MANAGE_CONNECTIONS with toolkit ai_ml_api
  3. If connection is not ACTIVE, follow the returned auth link to complete setup
  4. Confirm connection status shows ACTIVE before running any workflows
Tool Discovery

Always discover available tools before executing workflows:

RUBE_SEARCH_TOOLS
queries: [{use_case: "AI ML API 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 AI ML API task"}]
session: {id: "existing_session_id"}
Step 2: Check Connection
RUBE_MANAGE_CONNECTIONS
toolkits: ["ai_ml_api"]
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 AI ML API-specific use case | | Connect | RUBE_MANAGE_CONNECTIONS with toolkit ai_ml_api | | 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

按 Apache-2.0 许可原样转载,未经改动 · 在 GitHub 查看 →

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