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domino-vibe-modeling

Run AI coding assistant commands as secure Domino Data Lab jobs.

6|3|Updated Feb 19, 2026
Search Tags:#mlops#data science#mcp#domino#ai assisted development#vibe modeling
One-click install
npx skills add https://github.com/dominodatalab/domino-claude-plugin --skill domino-vibe-modeling-dominodatalab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: domino-vibe-modeling
Source: https://github.com/dominodatalab/domino-claude-plugin/tree/main/skills/vibe-modeling
Command: npx skills add https://github.com/dominodatalab/domino-claude-plugin --skill domino-vibe-modeling-dominodatalab

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables AI coding assistants to securely and reproducibly interact with the Domino Data Lab platform, transforming how developers work with data science projects.

Core Features & Use Cases

  • Secure Job Execution: Run code as containerized Domino jobs, not locally, ensuring governance and audit trails.
  • AI-Driven Workflows: Use AI assistants like Cursor to analyze data, train models, and manage experiments within Domino.
  • Reproducibility: All AI-assisted actions are tracked and versioned within Domino projects.
  • Use Case: A data scientist can prompt their AI assistant to "train a model on the latest dataset and log experiments," and the AI will orchestrate this as a Domino job, ensuring all parameters and results are captured.

Quick Start

Use the domino-vibe-modeling skill to run the python script 'train_model.py' as a Domino job.

Frequently Asked Questions about domino-vibe-modeling

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run AI-assisted data science workflows as secure Domino jobs?▼

AI-assisted data science workflows run as secure Domino jobs by integrating AI coding assistants with the Domino platform via MCP servers. This facilitates secure command execution, maintaining governance, reproducibility, and audit trails for AI-driven data science projects.

Can I use Cursor or GitHub Copilot to train models within the Domino Data Lab platform?▼

Yes, Cursor and GitHub Copilot are supported AI assistants for model training within the Domino Data Lab platform. They connect through MCP servers to orchestrate tasks like data analysis and experiment tracking as containerized, reproducible Domino jobs.

What is vibe modeling in MLOps and how does it maintain reproducibility?▼

Vibe modeling in MLOps is an AI-assisted development approach where AI coding assistants interact with the Domino Data Lab platform. It maintains reproducibility by executing AI-driven actions as containerized Domino jobs, ensuring all parameters and results are tracked and versioned.

Does AI-assisted development with Domino support experiment tracking and governance?▼

AI-assisted development with Domino supports experiment tracking and governance by running code as containerized jobs rather than locally. This ensures all AI-driven actions, parameters, and results are captured within versioned Domino projects.

How do I execute a Python script as a reproducible Domino job using an AI assistant?▼

To execute a Python script as a reproducible Domino job, use the AI-assisted development skill to prompt your AI assistant. The AI orchestrates the script execution through MCP servers, running it securely as a containerized Domino job with full audit trails.

What are the limitations of running AI-driven workflows locally versus as Domino jobs?▼

Running AI-driven workflows locally lacks inherent governance, reproducibility, and audit trails. Executing them as containerized Domino jobs via MCP servers ensures all AI-assisted data science actions are securely tracked, versioned, and compliant with platform standards.

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