Domino Skills extend coding assistants with platform-specific actions. Inside a Domino workspace, your agent automatically gains access to specific skills, including running jobs, registering models, tracking experiments, deploying apps, and more.
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Note
| Domino Skills are currently optimized for Claude Code. They might not work out of the box with other coding agents. |
Skills load automatically when a coding assistant starts inside a Domino workspace. The agent detects the Domino project and loads available skills from the domino-claude-plugin.
You don’t need to install or configure skills manually. To use a skill, reference the skill by name when prompting your agent. For example:
Use the modeling assistant skill. Analyze the dataset in this project
and train a few models to predict diabetes.The agent will activate the skill and begin working with full access to Domino platform resources.
The following skills are included in the Domino Standard Environment:
Development & modeling
| Skill | Description |
|---|---|
modeling-assistant | AI-assisted model development with MCP servers |
experiment-tracking | Track ML experiments using MLflow-based Experiment Manager |
domino-experiment-setup | Set up MLflow experiment tracking for traditional ML |
environments | Create and customize Domino Compute Environments |
workspaces | Manage interactive development environments |
projects | Work with Domino Projects, Git integration, and collaboration |
Applications & SDK
| Skill | Description |
|---|---|
domino-app-init | Initialize new Domino-ready web apps such as Vite+React, Streamlit, Dash, and Flask |
domino-ui-design | Build Domino-styled web apps matching the Domino Design System |
python-sdk | Programmatically interact with Domino using python-domino SDK |
domino-debug-proxy | Debug Domino proxy and routing issues for web applications |
When you ask your coding agent to perform a Domino-specific task, the agent selects and activates the appropriate skill. For example:
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"Train a model on the diabetes dataset and track the experiment" activates the modeling-assistant and experiment-tracking skills.
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"Deploy this model as an API endpoint" activates the model-endpoints skill.
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"Create a Streamlit app for this project" activates the domino-app-init skill.
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"Set up a Spark cluster and run this distributed job" activates the distributed-computing skill.
Modeling assistant walkthrough has a complete example of the modeling-assistant skill in action.
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Get started: Launch a workspace and authenticate your coding agent.
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Modeling assistant walkthrough: An end-to-end demo of data analysis, model training, and comparison using skills.