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Databricks Dabs

Organization
databricks
databricks-dabs

Create, configure, validate, deploy, run, and manage Declarative Automation Bundles (DABs, formerly Databricks Asset Bundles). Use when working with Databricks resources via DABs including dashboards, jobs, pipelines, alerts, volumes, and apps.

Overview

Publisherdatabricks
Repositorydatabricks-agent-skills
Skill namedatabricks-dabs
Stars
366
Forks
93
Bundled files
8
Links
  • 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.

  • 8 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 Dabs 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-dabs
  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-dabs .claude/skills/databricks-dabs
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Databricks Dabs 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 Dabs 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 Dabs 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.

Declarative Automation Bundles (DABs)

Use this skill for any bundle-related request including creating, configuring, validating, deploying, running, and managing Databricks resources through DABs.

Reference Documentation

The following reference files provide detailed guidance for specific bundle tasks:

  • Bundle Structure - Bundle structure, databricks.yml configuration, resource definitions, path resolution, variables, and multi-environment targets
  • SDP Pipelines - Spark Declarative Pipeline configurations for DABs
  • SQL Alerts - SQL Alert schemas and configuration (critical - API differs from other resources)
  • Deploy and Run - Validation, deployment, running resources, monitoring logs, and troubleshooting common issues
  • Resource Permissions - Permission levels and access control for bundle resources, per-resource-type levels, grants vs permissions

When to Use This Skill

Load this skill for any request involving:

  • Creating new bundle projects or resources
  • Configuring databricks.yml or resource YAML files
  • Setting up multi-environment deployments (dev/prod targets)
  • Deploying or running bundle resources
  • Managing permissions for bundle resources
  • Troubleshooting bundle validation or deployment errors
  • Working with specific resource types (dashboards, jobs, pipelines, alerts, volumes, apps)

General Guidelines

  1. Always validate after configuration changes - Use bundle validate --strict --target <target> after any change
  2. Use reference documentation - Consult the appropriate reference file for detailed patterns and examples
  3. Follow naming conventions - Resource files should use <name>.<resource_type>.yml format
  4. Path resolution is critical - Paths differ based on file location (see Bundle Structure reference)
  5. Preserve existing structure - Keep user comments and structure when editing YAML files
  6. Use variables - Parameterize catalog, schema, and warehouse for multi-environment support
  7. Namespace App names - App names are workspace-global and limited to 30 characters. With Databricks CLI 0.270.0 or later, shared-development defaults should include the app and ${workspace.current_user.domain_friendly_name}; override production with a stable name, and persist an explicit local value when the default is invalid, collides, or must distinguish multiple non-production targets in one workspace. On older CLI versions, require an explicit app_name value instead

Required App Completion Contract

Before validating any bundle that creates or changes an App, re-read the final bundle YAML and confirm all of the following structural requirements:

  • Each App resource uses a dedicated name variable, such as name: ${var.app_name}, while preserving an existing equivalent variable when present.
  • For Databricks CLI 0.270.0 or later, each App name variable defaults to a recognizable App prefix plus ${workspace.current_user.domain_friendly_name} for development, and the production target overrides it with a stable name. On older CLI versions, App name variables have no default and each developer must provide values.
  • No development App name uses ${workspace.current_user.short_name}, a bare ${bundle.target}, or a hardcoded value.

Only after this check, run databricks bundle validate --strict --target <target> --output json and inspect each resolved resources.apps.<key>.name. Each name must contain only lowercase letters, digits, and hyphens and be at most 30 characters. If a resolved name is invalid, collides after normalization, or multiple non-production targets share a workspace, persist a shorter, distinct value in the uncommitted .databricks/bundle/<target>/variable-overrides.json file and validate again. Successful validation alone does not satisfy this contract because validation does not catch every invalid or non-namespaced App name.

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 Dabs AI skill do?

Create, configure, validate, deploy, run, and manage Declarative Automation Bundles (DABs, formerly Databricks Asset Bundles). Use when working with Databricks resources via DABs including dashboards, jobs, pipelines, alerts, volumes, and apps.

Why use Databricks Dabs on TypingMind?

Because you install it once and use it with any model. Databricks Dabs 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 Dabs 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-dabs. 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 Dabs?

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 Dabs?

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

Is the Databricks Dabs 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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