
domino-vibe-modeling
by jvdomino
A comprehensive Claude Code plugin providing coverage of the Domino Data Lab platform for AI-assisted development.
SKILL.md
name: domino-vibe-modeling description: Enable AI-assisted development (vibe modeling) within Domino using MCP (Model Context Protocol) servers. AI coding assistants like Cursor and GitHub Copilot can execute commands as Domino jobs, maintaining security, governance, and reproducibility. Use when setting up AI code assistants to work with Domino, configuring MCP servers, or enabling vibe modeling workflows.
Domino Vibe Modeling Skill
This skill provides comprehensive knowledge for enabling vibe modeling within Domino Data Lab, allowing AI coding assistants to interact with the platform while maintaining security and governance.
What is Vibe Modeling?
Vibe modeling refers to using AI code assistants to go beyond pure code generation and assist with:
- Experiment setup and configuration
- Data analysis and exploration
- Model training and evaluation
- Results interpretation
All while maintaining the security, governance, and reproducibility offered by the Domino platform.
Key Components
MCP (Model Context Protocol)
MCP servers bridge AI coding assistants with the Domino platform:
- Commands run as containerized Domino jobs (not locally)
- All work maintains platform audit trails and version control
- Data access through standardized paths (
/mnt/data/,/mnt/imported/data/)
Supported AI Assistants
| Assistant | Support Level |
|---|---|
| Cursor | Primary support with agent mode |
| GitHub Copilot | Supported with MCP integration |
| VSCode | Supported within Domino workspaces |
| JupyterLab | Supported within Domino workspaces |
Related Documentation
- MCP-SERVER.md - Domino MCP Server setup and usage
- SETUP.md - Complete setup guide for vibe modeling
Quick Start
1. Clone the MCP Server
git clone https://github.com/dominodatalab/domino_mcp_server.git
cd domino_mcp_server
2. Install Dependencies
uv sync # or pip install -r requirements.txt
3. Configure Environment
# .env file
DOMINO_API_KEY=your_api_key
DOMINO_HOST=https://your-domino.company.com
4. Configure Cursor
Create .cursor/mcp.json:
{
"mcpServers": {
"domino": {
"command": "uv",
"args": ["run", "domino-mcp-server"],
"cwd": "/path/to/domino_mcp_server"
}
}
}
5. Create Project Settings
Create domino_project_settings.md in your project:
# Domino Project Settings
- Project Owner: your-username
- Project Name: your-project
MCP Server Tools
The Domino MCP Server provides three primary tools:
| Tool | Description |
|---|---|
run_domino_job | Execute commands (Python scripts, etc.) as Domino jobs |
check_job_status | Retrieve execution status of specific job runs |
check_job_results | Obtain detailed results and outputs from completed jobs |
Important Considerations
-
Git Commits Required: Changes must be committed to git before running Domino jobs, as remote execution cannot access uncommitted local changes
-
Firewall Configuration: Copilot's URLs must be whitelisted for proper functionality
-
Custom Environment: Use the
quay.io/domino/field:vibe-modelingDocker image for vibe modeling workflows
Blueprint Reference
Official Vibe Modeling Blueprint: https://domino.ai/resources/blueprints/vibe-modeling
GitHub Repository: https://github.com/dominodatalab/domino_mcp_server
スコア
総合スコア
リポジトリの品質指標に基づく評価
SKILL.mdファイルが含まれている
ライセンスが設定されている
100文字以上の説明がある
GitHub Stars 100以上
3ヶ月以内に更新がある
10回以上フォークされている
オープンIssueが50未満
プログラミング言語が設定されている
1つ以上のタグが設定されている
レビュー
レビュー機能は近日公開予定です