Mistral AI Automation
Scannednpx machina-cli add skill ComposioHQ/awesome-claude-skills/mistral-ai-automation --openclawMistral AI Automation
Automate your Mistral AI workflows -- upload files for fine-tuning, batch processing, and OCR, manage document libraries for RAG-enabled agents, list and retrieve files, track fine-tuning jobs, and integrate Mistral AI into cross-app data pipelines.
Toolkit docs: composio.dev/toolkits/mistral_ai
Setup
- Add the Composio MCP server to your client:
https://rube.app/mcp - Connect your Mistral AI account when prompted (API key authentication)
- Start using the workflows below
Core Workflows
1. Upload Files to Mistral AI
Use MISTRAL_AI_UPLOAD_FILE to upload files for fine-tuning, batch processing, or OCR.
Tool: MISTRAL_AI_UPLOAD_FILE
Inputs:
- file: object (required)
- name: string -- destination filename (e.g., "training_data.jsonl")
- mimetype: string -- MIME type (e.g., "application/pdf", "application/jsonl")
- s3key: string -- S3 key of a previously downloaded/stored file
- purpose: "fine-tune" | "batch" | "ocr" (default "fine-tune")
Limits: Maximum file size is 512 MB. For fine-tuning, only .jsonl files are supported.
2. List and Retrieve Files
Use MISTRAL_AI_LIST_FILES to browse uploaded files with pagination, and MISTRAL_AI_RETRIEVE_FILE to get metadata for a specific file.
Tool: MISTRAL_AI_LIST_FILES
Inputs:
- limit: integer (optional, min 1)
- after: string (file ID cursor for next page)
- order: "asc" | "desc" (default "desc")
Tool: MISTRAL_AI_RETRIEVE_FILE
Inputs:
- file_id: string (required) -- UUID obtained from List Files
3. Create Document Libraries
Use MISTRAL_AI_CREATE_LIBRARY to group documents into libraries for use with RAG-enabled Mistral AI agents.
Tool: MISTRAL_AI_CREATE_LIBRARY
Inputs:
- name: string (required) -- e.g., "Project Documents"
- description: string (optional)
4. Upload Documents to a Library
Use MISTRAL_AI_UPLOAD_LIBRARY_DOCUMENT to add documents to a library for RAG retrieval by Mistral AI agents.
Tool: MISTRAL_AI_UPLOAD_LIBRARY_DOCUMENT
- Requires library_id and file details
- Call RUBE_GET_TOOL_SCHEMAS for full input schema before use
5. List Libraries and Download Files
Use MISTRAL_AI_LIST_LIBRARIES to discover available document libraries, and MISTRAL_AI_DOWNLOAD_FILE to retrieve file content.
Tool: MISTRAL_AI_LIST_LIBRARIES
- Lists all document libraries with metadata (id, name, document counts)
- Call RUBE_GET_TOOL_SCHEMAS for full input schema
Tool: MISTRAL_AI_DOWNLOAD_FILE
- Downloads raw binary content of a previously uploaded file
- Call RUBE_GET_TOOL_SCHEMAS for full input schema
6. Track Fine-Tuning Jobs
Use MISTRAL_AI_GET_FINE_TUNING_JOBS to list and filter fine-tuning jobs by model, status, and creation time.
Tool: MISTRAL_AI_GET_FINE_TUNING_JOBS
- Supports filtering by model, status, creation time, and W&B integration
- Call RUBE_GET_TOOL_SCHEMAS for full input schema
Known Pitfalls
| Pitfall | Detail |
|---|---|
| Fine-tune file format | Only .jsonl files are supported for fine-tuning uploads. Other formats will be rejected. |
| File size limit | Maximum upload size is 512 MB per file. |
| File object structure | MISTRAL_AI_UPLOAD_FILE requires an s3key referencing a previously stored file, not raw binary content. Use a download action first to stage files in S3. |
| Pagination cursors | MISTRAL_AI_LIST_FILES uses cursor-based pagination via the after parameter (file ID). Continue fetching until no more results are returned. |
| Library document processing | Uploaded library documents are processed asynchronously. They may not be immediately available for RAG queries after upload. |
| Schema references | Several tools (MISTRAL_AI_UPLOAD_LIBRARY_DOCUMENT, MISTRAL_AI_LIST_LIBRARIES, MISTRAL_AI_GET_FINE_TUNING_JOBS, MISTRAL_AI_DOWNLOAD_FILE) require calling RUBE_GET_TOOL_SCHEMAS to load full input schemas before execution. |
Quick Reference
| Tool Slug | Description |
|---|---|
MISTRAL_AI_UPLOAD_FILE | Upload files for fine-tuning, batch processing, or OCR |
MISTRAL_AI_LIST_FILES | List uploaded files with pagination |
MISTRAL_AI_RETRIEVE_FILE | Get metadata for a specific file by ID |
MISTRAL_AI_DOWNLOAD_FILE | Download content of an uploaded file |
MISTRAL_AI_CREATE_LIBRARY | Create a document library for RAG |
MISTRAL_AI_LIST_LIBRARIES | List all document libraries with metadata |
MISTRAL_AI_UPLOAD_LIBRARY_DOCUMENT | Add a document to a library for RAG |
MISTRAL_AI_GET_FINE_TUNING_JOBS | List and filter fine-tuning jobs |
Powered by Composio
Source
git clone https://github.com/ComposioHQ/awesome-claude-skills/blob/master/composio-skills/mistral-ai-automation/SKILL.mdView on GitHub Overview
Automate Mistral AI operations—from uploading files for fine-tuning, batch processing, and OCR to organizing libraries for RAG-enabled agents. Track fine-tuning jobs and build end-to-end data pipelines via the Composio MCP integration.
How This Skill Works
The skill orchestrates Mistral AI tasks through MCP-powered tools such as MISTRAL_AI_UPLOAD_FILE, MISTRAL_AI_LIST_FILES, MISTRAL_AI_CREATE_LIBRARY, MISTRAL_AI_UPLOAD_LIBRARY_DOCUMENT, and MISTRAL_AI_GET_FINE_TUNING_JOBS. You stage inputs by providing a file object (name, mimetype, s3key) and a purpose (fine-tune, batch, or ocr), then organize content into libraries for RAG-enabled agents and monitor tuning progress.
When to Use It
- Upload a JSONL dataset to fine-tune a Mistral AI model (respecting the 512 MB limit).
- Batch process or OCR large document sets and feed results into Mistral pipelines.
- Create and populate document libraries for RAG-enabled agents.
- List and retrieve file or library metadata to plan data workflows.
- Track and filter ongoing fine-tuning jobs (model, status, creation time, W&B integration).
Quick Start
- Step 1: Set up MCP by adding the Composio MCP server (https://rube.app/mcp) and connect your Mistral AI account.
- Step 2: Upload a file with MISTRAL_AI_UPLOAD_FILE, providing a file object (name, mimetype, s3key) and a purpose (default fine-tune).
- Step 3: Create or list a library (MISTRAL_AI_CREATE_LIBRARY or MISTRAL_AI_LIST_LIBRARIES) and upload library documents as needed to build your RAG pipeline.
Best Practices
- For fine-tuning, use only .jsonl files and keep each file within 512 MB.
- Stage files in S3 and reference them with s3key in MISTRAL_AI_UPLOAD_FILE; do not upload raw binary content directly.
- Use MISTRAL_AI_LIST_FILES with limit and after cursor to paginate through results.
- Organize related documents into libraries with MISTRAL_AI_CREATE_LIBRARY and MISTRAL_AI_UPLOAD_LIBRARY_DOCUMENT for efficient RAG retrieval.
- Consult RUBE_GET_TOOL_SCHEMAS for full input schemas when uploading library documents and monitor progress with MISTRAL_AI_GET_FINE_TUNING_JOBS (including W&B options).
Example Use Cases
- A data science team uploads a 350 MB JSONL dataset to fine-tune a Mistral model and monitors the tuning status.
- An enterprise creates a 'Product Docs' library, uploads manuals as library documents, and uses RAG-enabled agents to answer customer queries.
- An operations team runs OCR on invoices, staging outputs via MISTRAL_AI_UPLOAD_FILE with purpose='ocr' and storing results in S3.
- A knowledge base team lists libraries to audit content and downloads a file for backup or review.
- A data ops workflow tracks fine-tuning jobs with filters for model and status, integrating with W&B for experiment tracking.