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Dominodatalab

Full Domino Data Lab platform support — workspaces, jobs, model deployment, experiment tracking, GenAI tracing, Spark/Ray/Dask, and app deployment for data science teams

developmentdeploymentai
By Domino Data Lab
53Updated 2 weeks agoPythonMIT

Installation

/plugin install dominodatalab@claude-plugins-official

How to install

  1. Open Claude Code in your terminal
  2. Run the installation command above
  3. The plugin will be enabled automatically
  4. Use the plugin's features in your Claude Code sessions

Domino Data Lab Plugin for Claude Code

A comprehensive Claude Code plugin providing full coverage of the Domino Data Lab platform for AI-assisted development.

Overview

This plugin enables Claude Code to help you with all aspects of Domino Data Lab, including:

  • Workspaces: Jupyter, VS Code, RStudio configuration and management
  • Jobs: Batch execution, scheduled jobs, and monitoring
  • Environments: Custom Docker environments and package management
  • Datasets: Data versioning with snapshots and sharing
  • NetApp Volumes: Enterprise-grade multi-terabyte storage with near-instant snapshots
  • Apps: Deploy React, Streamlit, and Dash applications
  • Models: Deploy, monitor, and manage model endpoints
  • GenAI: Trace and evaluate AI agents with the Domino SDK
  • Distributed Computing: Spark, Ray, and Dask clusters
  • And more...

Installation

Prerequisites

  • Claude Code CLI v1.0.33 or later (claude --version to check)
  • Access to a Domino Data Lab instance
  • Domino API key (for API operations when running outside a Domino workspace)
  • uv package manager (install guide) — required for the bundled Domino MCP server

Option 1: Marketplace Install (Recommended)

This approach registers the plugin through Claude Code's native marketplace system so it persists across sessions.

Step 1: Clone the repository and create a marketplace wrapper

# Clone the plugin
git clone https://github.com/dominodatalab/domino-claude-plugin.git

# Create the marketplace directory structure
mkdir -p ~/.claude/marketplaces/domino/.claude-plugin
mkdir -p ~/.claude/marketplaces/domino/plugins

# Move the plugin into the marketplace
mv domino-claude-plugin ~/.claude/marketplaces/domino/plugins/domino-claude-plugin

Step 2: Create the marketplace manifest

cat > ~/.claude/marketplaces/domino/.claude-plugin/marketplace.json << 'EOF'
{
  "name": "domino-marketplace",
  "owner": {
    "name": "Domino Data Lab",
    "email": "support@dominodatalab.com"
  },
  "plugins": [
    {
      "name": "domino-claude-plugin",
      "description": "Domino Data Lab plugin for Claude Code - workspaces, jobs, environments, datasets, apps, models, and more",
      "version": "1.0.0",
      "source": "./plugins/domino-claude-plugin",
      "category": "development"
    }
  ]
}
EOF

Step 3: Register the marketplace and install the plugin

Launch Claude Code and run:

/plugin marketplace add /home/<your-username>/.claude/marketplaces/domino
/plugin install domino-claude-plugin@domino-marketplace

Note: Replace <your-username> with your actual username, or use the full absolute path (e.g., /home/ubuntu/.claude/marketplaces/domino). The ~ shorthand may not expand correctly.

Step 4: Restart Claude Code

/exit
claude

The plugin should appear in your loaded plugins on startup. Verify with:

/plugin

Navigate to the Installed tab to confirm domino-claude-plugin is listed.


Option 2: Direct Plugin Directory (Development / Quick Start)

Use the --plugin-dir flag to load the plugin directly. This is ideal for development, testing, or quick evaluation.

# Clone the plugin
git clone https://github.com/dominodatalab/domino-claude-plugin.git

# Ensure the plugin manifest exists
mkdir -p domino-claude-plugin/.claude-plugin
cat > domino-claude-plugin/.claude-plugin/plugin.json << 'EOF'
{
  "name": "domino-claude-plugin",
  "description": "Domino Data Lab plugin for Claude Code",
  "version": "1.0.0"
}
EOF

# Run Claude Code with the plugin
claude --plugin-dir ./domino-claude-plugin

To make this persistent without the marketplace approach, add a shell alias:

echo 'alias claude="claude --plugin-dir /path/to/domino-claude-plugin"' >> ~/.bashrc
source ~/.bashrc

Option 3: Team / Project-Level Install

For teams sharing a project, add the marketplace to your project's .claude/settings.json:

{
  "extraKnownMarketplaces": {
    "domino-marketplace": {
      "source": {
        "source": "directory",
        "path": "/path/to/domino-marketplace"
      }
    }
  },
  "enabledPlugins": {
    "domino-claude-plugin@domino-marketplace": true
  }
}

When team members trust the repository folder, Claude Code will prompt them to install the marketplace and plugin automatically.


Verifying the Installation

After installation, test that the plugin is working:

  1. Check slash commands are available:

    /domino-app-init
  2. Test skill auto-invocation by asking a Domino-related question:

    Help me deploy a Streamlit app to Domino

    Claude should automatically invoke the domino-app-deployment skill.

  3. Check the plugin is listed:

    /plugin

    Navigate to the Installed tab.

Note: Plugin skills do not appear in the /skills list. They are auto-invoked by Claude based on task context and will show in Claude's init message at the top of a new session.


Updating the Plugin

If installed via the marketplace approach, navigate to the plugin source and pull updates:

cd ~/.claude/marketplaces/domino/plugins/domino-claude-plugin
git pull

Then restart Claude Code. If installed via --plugin-dir, pull updates in the cloned directory.


What's Included

Bundled MCP Server

The plugin includes a vendored copy of the Domino MCP Server that starts automatically when the plugin is enabled. It provides tools for running Domino jobs, checking job status/results, and syncing files with DFS-based projects.

  • Inside a Domino workspace: Fully automatic — authentication uses ephemeral tokens, project info is auto-detected.
  • Outside Domino (laptop): Set DOMINO_API_KEY and DOMINO_HOST as environment variables in your shell.

Requires uv to be installed (see Prerequisites).

Skills (20 Total)

SkillDescription
domino-workspacesJupyter, VS Code, RStudio workspace management
domino-jobsJobs and scheduled jobs execution
domino-environmentsCompute environments and Dockerfile customization
domino-datasetsData management, snapshots, and versioning
domino-projectsGit integration and project collaboration
domino-app-deploymentDeploy web apps (React, Streamlit, Dash)
domino-experiment-trackingMLflow experiment tracking and model registry
domino-genai-tracing@add_tracing decorator and DominoRun
domino-model-endpointsDeploy and call model APIs
domino-model-monitoringDrift detection and model quality tracking
domino-flowsFlyte-based workflow orchestration
domino-distributed-computingSpark, Ray, Dask cluster management
domino-ai-gatewayLLM proxy for OpenAI, Bedrock, etc.
domino-launchersParameterized web forms for self-service
domino-modeling-assistantMCP server for AI-assisted model development
domino-data-connectivityS3 Mountpoint, AWS IRSA, Azure credentials
domino-python-sdkPython SDK (python-domino) and REST API
domino-data-sdkData SDK (domino-data) for data sources, datasets, training sets
domino-ui-designKnowledge on Domino UI styling for integrated App design
netapp-volumesEnterprise-grade multi-terabyte NetApp ONTAP storage with near-instant snapshots and versioning

Slash Commands

CommandDescription
/domino-app-initInitialize a new Domino app with framework templates
/domino-debug-proxyDebug reverse proxy issues for apps
/domino-experiment-setupSet up MLflow experiment tracking
/domino-trace-setupSet up GenAI tracing with the Domino SDK

Subagents

AgentDescription
domino-deploySpecialized agent for deploying apps, models, and endpoints
domino-debugAgent for debugging Domino issues and troubleshooting
domino-setupAgent for setting up new projects and configurations

Output Styles

Switch output styles with /output-style:

StyleDescription
domino-learningEducational mode with Domino Insights after each task
domino-mlopsProduction-focused with MLOps checklists and best practices

Project Structure

domino-claude-plugin/
├── .claude-plugin/
│   └── plugin.json          # Plugin manifest
├── .mcp.json                # Bundled MCP server config (auto-starts)
├── mcp-servers/             # Vendored MCP servers
│   └── domino_mcp_server/   # Domino MCP Server (jobs, DFS sync)
├── agents/                  # Subagents
│   ├── domino-deploy.md
│   ├── domino-debug.md
│   └── domino-setup.md
├── output-styles/           # Custom output styles
│   ├── domino-learning.md
│   └── domino-mlops.md
├── skills/                  # 18 skill directories
│   ├── workspaces/
│   ├── jobs/
│   ├── environments/
│   ├── datasets/
│   ├── projects/
│   ├── app-deployment/
│   ├── experiment-tracking/
│   ├── genai-tracing/
│   ├── model-endpoints/
│   ├── model-monitoring/
│   ├── flows/
│   ├── distributed-computing/
│   ├── ai-gateway/
│   ├── launchers/
│   ├── modeling-assistant/
│   ├── data-connectivity/
│   ├── python-sdk/
│   ├── domino-data-sdk/
│   └── netapp-volumes/
├── commands/                # Slash commands
├── hooks/                   # Example automation hooks
├── templates/               # Code templates
│   ├── vite-react/
│   ├── streamlit/
│   ├── dash/
│   ├── experiment/
│   └── tracing/
├── CONTRIBUTING.md
├── LICENSE
└── README.md

Troubleshooting

IssueSolution
/skills shows "No skills found"Plugin skills don't appear in /skills — they are auto-invoked by Claude based on context. Check /plugin → Installed tab instead.
Plugin not loading from settings.jsonClaude Code does not support a "plugins" array in settings.json. Use the marketplace approach or --plugin-dir flag.
~ path not expandingAlways use absolute paths (e.g., /home/ubuntu/...) in marketplace commands and settings.
Slash commands not appearingRestart Claude Code after installing. Commands are loaded at session start.
"Failed to parse marketplace file"Ensure marketplace.json has the owner object and source is a string path (e.g., "./plugins/domino-claude-plugin"), not a nested object.

Usage Examples

Deploy a Streamlit App

User: Help me deploy a Streamlit dashboard to Domino
Claude: I'll help you set up a Streamlit app for Domino...

Set Up Experiment Tracking

User: /domino-experiment-setup
Claude: I'll configure MLflow experiment tracking for your project...

Create a Scheduled Job

User: How do I run a training script every day at midnight?
Claude: I'll show you how to create a scheduled job in Domino...

Deploy a Model API

User: I need to deploy my scikit-learn model as an API
Claude: I'll help you create a model endpoint in Domino...

API Reference

The domino-python-sdk skill includes comprehensive REST API documentation:

  • API-PROJECTS.md — Projects, collaborators, Git repos
  • API-JOBS.md — Jobs, logs, scheduled execution
  • API-DATASETS.md — Datasets, snapshots, permissions
  • API-MODELS.md — Model APIs, deployments, registry
  • API-ENVIRONMENTS.md — Environments, revisions
  • API-APPS.md — Apps, versions, instances
  • API-ADMIN.md — Users, orgs, hardware tiers

Documentation

View source on GitHub