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Databricks Unity Gateway

Organization
databricks
databricks-unity-gateway

Use when querying or calling pay-per-token foundation models, or when creating, updating, listing, or querying Unity Gateway (also called Unity AI Gateway) services, including model provider services, MCP services, system.ai services, and three-part Unity Catalog service names (catalog.schema.service). Also use for managing spend budgets and usage alerts on Unity Gateway, and for migrating from legacy AI Gateway. Also use when a workload with Model Serving endpoint permissions (for example CAN_QUERY) gets PERMISSION_DENIED querying a three-part model name, since serving-endpoint access does not grant access to a Unity Gateway service. Not for configuring legacy AI Gateway on Model Serving endpoints; use databricks-model-serving for that.

Overview

Publisherdatabricks
Repositorydatabricks-agent-skills
Skill namedatabricks-unity-gateway
Stars
366
Forks
93
Bundled files
11
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 11 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by databricks on GitHub. Read the source before you install it.

Installation

Install the Databricks Unity Gateway AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/databricks/databricks-agent-skills/tree/main/plugins/databricks/claude/skills/databricks-unity-gateway
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/databricks/databricks-agent-skills.git /tmp/databricks-agent-skills
mkdir -p .claude/skills
cp -r /tmp/databricks-agent-skills/plugins/databricks/claude/skills/databricks-unity-gateway .claude/skills/databricks-unity-gateway
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Databricks Unity Gateway in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Databricks Unity Gateway on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Databricks Unity Gateway is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Unity Gateway

Use the parent databricks-core skill for CLI authentication and profile selection.

Unity Gateway provides Unity Catalog securables for governed access to AI services. The CLI command group keeps the earlier product name: databricks ai-gateway --help.

Choose the Correct Gateway

RequestUse
Unity Gateway model service, MCP service, or model provider serviceThis skill and databricks ai-gateway
AI Gateway configuration on a Model Serving endpoint, including put-ai-gatewaydatabricks-model-serving and databricks serving-endpoints
Lakeflow Connect ingestion gatewaydatabricks-lakeflow-connect

Treat Unity AI Gateway as an earlier name for Unity Gateway when the resource is a Unity Catalog securable. Do not translate legacy Model Serving AI Gateway resources into Unity Gateway resources unless the user explicitly requests a migration.

CLI Workflow

Use the Databricks CLI for resource lifecycle and permission management. Do not substitute direct REST calls or a Databricks SDK for those operations. Python clients are supported for querying model and model provider services; read Query services.

An explicit request for Unity Gateway, Unity AI Gateway, or databricks ai-gateway belongs to this skill. Do not switch to databricks-model-serving merely because the request involves a model service or a workspace. Use that skill only for a legacy per-endpoint AI Gateway operation or when a provisioned-throughput destination requires Model Serving endpoint details.

  1. Verify the CLI meets the minimum version:

    bash
    databricks --version

  2. Inspect the command and the relevant operation before constructing a payload:

    bash
    databricks ai-gateway --help
databricks ai-gateway <operation> --help

  3. Pass create and update configuration through --json. Use an explicit profile when profile-based authentication is required:

    bash
    databricks ai-gateway <operation> --json @payload.json --profile <PROFILE>

Do not invent JSON fields from similarly named legacy APIs. Build the payload from the installed CLI help and the Unity Gateway documentation for the selected service type.

Service Types

Service typeCLI operations
Model servicecreate-model-service, get-model-service, list-model-services, update-model-service, delete-model-service; read Model services
MCP servicecreate-mcp-service, get-mcp-service, list-mcp-services, update-mcp-service, delete-mcp-service; read MCP services
Model provider servicecreate-model-provider-service, get-model-provider-service, list-model-provider-services, update-model-provider-service, delete-model-provider-service; read Model provider services

Querying and permissions

Do not fetch public documentation for fields already covered by these references. Consult the authoritative documentation only when a required field is absent from the reference or the user explicitly asks for the latest documentation. Do not assign a Beta or preview status unless the installed CLI help or current documentation explicitly does so.

Migration from Model Serving to Unity Gateway

When migrating from workspace-scoped Model Serving endpoints to Unity Catalog-scoped Unity Gateway services, read references/model-serving-migration.md.

Authoritative Documentation

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Databricks Unity Gateway AI skill do?

Use when querying or calling pay-per-token foundation models, or when creating, updating, listing, or querying Unity Gateway (also called Unity AI Gateway) services, including model provider services, MCP services, system.ai services, and three-part Unity Catalog service names (catalog.schema.service). Also use for managing spend budgets and usage alerts on Unity Gateway, and for migrating from legacy AI Gateway. Also use when a workload with Model Serving endpoint permissions (for example CAN_QUERY) gets PERMISSION_DENIED querying a three-part model name, since serving-endpoint access does...

Why use Databricks Unity Gateway on TypingMind?

Because you install it once and use it with any model. Databricks Unity Gateway is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Databricks Unity Gateway in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/databricks/databricks-agent-skills/tree/main/plugins/databricks/claude/skills/databricks-unity-gateway. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Databricks Unity Gateway?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Databricks Unity Gateway?

As many as you like. As long as a model supports skills, you can use Databricks Unity Gateway with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Databricks Unity Gateway AI skill free?

It is published on GitHub by databricks. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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