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dominodatalab Plugin

Author
Domino Data Lab
Category
Development
Topics
Data Engineering & Analytics · AI Agents & AI App Development · Enterprise Business Platforms
First seen
2026-07-09
Last confirmed
2026-07-13
Explanation last updated
2026-07-12

The explanation below is AI-generated. Please verify it against the sources.

This is a Claude Code plugin that adds coverage of the Domino Data Lab platform, according to its GitHub README. It bundles 20 skills spanning workspaces, jobs, environments, datasets, app deployment, model endpoints, experiment tracking, GenAI tracing, and Spark/Ray/Dask distributed computing, along with slash commands, subagents, and a bundled MCP server. It can be installed via Claude Code's marketplace system, a direct --plugin-dir flag, or team/project-level settings. Domino Data Lab itself, per its homepage, is described as an enterprise AI platform for building, scaling, and governing AI-powered applications and agentic AI.

Overview

Domino Data Lab is described on its homepage as an enterprise AI platform that integrates model development, MLOps, collaboration, and governance, aimed at helping enterprises build, scale, and govern AI-powered applications. Founded in 2013, it is backed by investors including Sequoia Capital, Coatue Management, NVIDIA, and Snowflake, according to the homepage. This plugin, per the README, is a Claude Code integration that provides AI-assisted development support for that Domino platform, not a separate SaaS product itself.

What you can do with dominodatalab

  • Manage Jupyter, VS Code, and RStudio workspaces via the domino-workspaces skill
  • Run, schedule, and monitor jobs via the domino-jobs skill
  • Configure custom Docker environments via the domino-environments skill
  • Version and share datasets with snapshots via the domino-datasets skill
  • Deploy React, Streamlit, or Dash apps via the domino-app-deployment skill and /domino-app-init command
  • Set up MLflow experiment tracking and model registry via the domino-experiment-tracking skill and /domino-experiment-setup command
  • Trace and evaluate GenAI agents via the domino-genai-tracing skill and /domino-trace-setup command
  • Deploy, call, and monitor model endpoints via the domino-model-endpoints and domino-model-monitoring skills
  • Manage Spark, Ray, and Dask distributed computing clusters via the domino-distributed-computing skill
  • Use subagents (domino-deploy, domino-debug, domino-setup) for deployment, debugging, and project setup
  • Run the bundled Domino MCP server for job execution, status checks, and DFS-based file syncing

Sources

Original description (English)

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

History of dominodatalab

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