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mistral_ai-automation

Automate Mistral AI tasks via Rube MCP (Composio): completions, embeddings, fine-tuning, and model management. Always search tools first for current schemas.

Packaged view

This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.

Stars
45,850
Hot score
99
Updated
March 20, 2026
Overall rating
C4.0
Composite score
4.0
Best-practice grade
C60.4

Install command

npx @skill-hub/cli install composiohq-awesome-claude-skills-mistral-ai-automation

Repository

ComposioHQ/awesome-claude-skills

Skill path: composio-skills/mistral_ai-automation

Automate Mistral AI tasks via Rube MCP (Composio): completions, embeddings, fine-tuning, and model management. Always search tools first for current schemas.

Open repository

Best for

Primary workflow: Analyze Data & AI.

Technical facets: Full Stack, Data / AI, Integration.

Target audience: everyone.

License: Unknown.

Original source

Catalog source: SkillHub Club.

Repository owner: ComposioHQ.

This is still a mirrored public skill entry. Review the repository before installing into production workflows.

What it helps with

  • Install mistral_ai-automation into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
  • Review https://github.com/ComposioHQ/awesome-claude-skills before adding mistral_ai-automation to shared team environments
  • Use mistral_ai-automation for development workflows

Works across

Claude CodeCodex CLIGemini CLIOpenCode

Favorites: 0.

Sub-skills: 0.

Aggregator: No.

Original source / Raw SKILL.md

---
name: mistral_ai-automation
description: "Automate Mistral AI tasks via Rube MCP (Composio): completions, embeddings, fine-tuning, and model management. Always search tools first for current schemas."
requires:
  mcp: [rube]
---

# Mistral AI Automation via Rube MCP

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

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

## Prerequisites

- Rube MCP must be connected (RUBE_SEARCH_TOOLS available)
- Active Mistral AI connection via `RUBE_MANAGE_CONNECTIONS` with toolkit `mistral_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 `mistral_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": "completions, embeddings, fine-tuning, and model management", "known_fields": ""}]
```

This returns:
- Available tool slugs for Mistral AI
- Recommended execution plan steps
- Known pitfalls and edge cases
- Input schemas for each tool

## Core Workflows

### 1. Discover Available Mistral AI Tools

```
RUBE_SEARCH_TOOLS:
  queries:
    - use_case: "list all available Mistral AI tools and capabilities"
```

Review the returned tools, their descriptions, and input schemas before proceeding.

### 2. Execute Mistral AI Operations

After discovering tools, execute them via:

```
RUBE_MULTI_EXECUTE_TOOL:
  tools:
    - tool_slug: "<discovered_tool_slug>"
      arguments: {<schema-compliant arguments>}
  memory: {}
  sync_response_to_workbench: false
```

### 3. Multi-Step Workflows

For complex workflows involving multiple Mistral AI operations:

1. Search for all relevant tools: `RUBE_SEARCH_TOOLS` with specific use case
2. Execute prerequisite steps first (e.g., fetch before update)
3. Pass data between steps using tool responses
4. Use `RUBE_REMOTE_WORKBENCH` for bulk operations or data processing

## Common Patterns

### Search Before Action
Always search for existing resources before creating new ones to avoid duplicates.

### Pagination
Many list operations support pagination. Check responses for `next_cursor` or `page_token` and continue fetching until exhausted.

### Error Handling
- Check tool responses for errors before proceeding
- If a tool fails, verify the connection is still ACTIVE
- Re-authenticate via `RUBE_MANAGE_CONNECTIONS` if connection expired

### Batch Operations
For bulk operations, use `RUBE_REMOTE_WORKBENCH` with `run_composio_tool()` in a loop with `ThreadPoolExecutor` for parallel execution.

## Known Pitfalls

- **Always search tools first**: Tool schemas and available operations may change. Never hardcode tool slugs without first discovering them via `RUBE_SEARCH_TOOLS`.
- **Check connection status**: Ensure the Mistral AI connection is ACTIVE before executing any tools. Expired OAuth tokens require re-authentication.
- **Respect rate limits**: If you receive rate limit errors, reduce request frequency and implement backoff.
- **Validate schemas**: Always pass strictly schema-compliant arguments. Use `RUBE_GET_TOOL_SCHEMAS` to load full input schemas when `schemaRef` is returned instead of `input_schema`.

## Quick Reference

| Operation | Approach |
|-----------|----------|
| Find tools | `RUBE_SEARCH_TOOLS` with Mistral AI-specific use case |
| Connect | `RUBE_MANAGE_CONNECTIONS` with toolkit `mistral_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` |

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