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apipie-ai-automation skill

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This skill automates Apipie AI tasks via Rube MCP, always discovering current tool schemas before execution to ensure up-to-date workflows.

npx playbooks add skill composiohq/awesome-claude-skills --skill apipie-ai-automation

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---
name: apipie-ai-automation
description: "Automate Apipie AI tasks via Rube MCP (Composio). Always search tools first for current schemas."
requires:
  mcp: [rube]
---

# Apipie AI Automation via Rube MCP

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

**Toolkit docs**: [composio.dev/toolkits/apipie_ai](https://composio.dev/toolkits/apipie_ai)

## Prerequisites

- Rube MCP must be connected (RUBE_SEARCH_TOOLS available)
- Active Apipie AI connection via `RUBE_MANAGE_CONNECTIONS` with toolkit `apipie_ai`
- 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 `apipie_ai`
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: "Apipie AI 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 Apipie AI task"}]
session: {id: "existing_session_id"}
```

### Step 2: Check Connection

```
RUBE_MANAGE_CONNECTIONS
toolkits: ["apipie_ai"]
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 Apipie AI-specific use case |
| Connect | `RUBE_MANAGE_CONNECTIONS` with toolkit `apipie_ai` |
| 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](https://composio.dev)*

Overview

This skill automates Apipie AI tasks through Composio's Apipie AI toolkit using the Rube MCP orchestration layer. It provides a repeatable pattern for discovering tools, validating connections, and executing toolkit operations programmatically. Follow the discovery-first approach to ensure schema compatibility and reliable runs.

How this skill works

The skill queries Rube MCP for current tool schemas via RUBE_SEARCH_TOOLS, validates or establishes an Apipie AI connection with RUBE_MANAGE_CONNECTIONS, then runs one or more toolkit actions with RUBE_MULTI_EXECUTE_TOOL (or bulk with RUBE_REMOTE_WORKBENCH). It always includes a session id and memory object and adapts arguments to the live schemas returned by the search call. The flow enforces connection checks, pagination handling, and schema compliance to avoid runtime errors.

When to use it

Automating Apipie AI workflows from external orchestration systems
Running repeatable or scheduled Apipie AI tasks with schema validation
Batch or bulk execution of Apipie toolkit operations via MCP
Integrating Apipie capabilities into larger multi-tool pipelines
When tool schemas may change frequently and discovery is required

Best practices

Always call RUBE_SEARCH_TOOLS first; never hardcode tool slugs or argument shapes
Verify RUBE_MANAGE_CONNECTIONS shows ACTIVE before executing any tools
Include memory in RUBE_MULTI_EXECUTE_TOOL calls even if empty ({}), and reuse session IDs within a workflow
Respect exact field names and types returned by search results; validate inputs against schemaRef when available
Handle pagination tokens from search and execute calls to fetch complete results

Example use cases

Discover available Apipie AI actions for a specific use case, then execute a single operation with arguments matching the returned schema
Validate and activate an Apipie AI connection, then run a batch of toolkit calls via RUBE_REMOTE_WORKBENCH for bulk processing
Build an automated pipeline that discovers tools, executes multi-step workflows with RUBE_MULTI_EXECUTE_TOOL, and persists state using the memory object
Integrate Apipie AI steps into a broader automation orchestration that queries tool schemas before every run to avoid breaking changes

FAQ

What is the first call my agent should make?

Always call RUBE_SEARCH_TOOLS with an Apipie-specific use case to retrieve current tool slugs and input schemas.

What if the connection is not active?

Call RUBE_MANAGE_CONNECTIONS for the apipie_ai toolkit and follow the returned auth link; confirm ACTIVE status before executing tools.