OfficialDatabricks Data & Analytics

Databricks DABs Manager

Name: Databricks DABs Manager
Author: databricks

By databricks· databricks/databricks-agent-skills· 0

Create, configure, and deploy Databricks Declarative Automation Bundles for dashboards, jobs, and pipelines.

Installation

1
Make sure Claude is on your device and in your terminal.
Skills load from ~/.claude/skills/ when Claude Code starts up — so you need it on your machine first. If you don't have it yet, install it once with the command below, then run claude in any terminal to verify.
One-time setup
```
npm i -g @anthropic-ai/claude-code
```
Already have it? Skip ahead.

Paste into Claude Code or into your terminal.

Install

git clone ht••••••••••••••••••••••••••••••••••••••••••••••••••••••• •••••••••••••••••••••••••••••••••••••••• •• ••••• •• •••••••••••••••••••••••••••••••• •• •• •• ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• •••••••••••••••••••••••••••••••••

This copies the whole skill folder into ~/.claude/skills/databricks-dabs/ — the SKILL.md plus any scripts, reference docs, or templates the skill ships with. Safe default: works for every skill.

Faster alternative (instruction-only skills)

Skips the clone and grabs only the SKILL.md file. Don't use this if the skill ships Python scripts, reference markdowns, or asset templates — they won't be downloaded and the skill will fail when it tries to load them.

Quick install (SKILL.md only)

mkdir -p ~/.••••••••••••••••••••••••••••• •• •••• ••••• ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• •• •••••••••••••••••••••••••••••••••••••••••

Restart Claude Code.
Quit and reopen Claude Code (or any other agent that loads from ~/.claude/skills/). New skills are picked up on startup.
Just ask Claude.
Skills auto-activate when your request matches the skill's description — no slash command needed. Trigger phrases live in the skill's own frontmatter; you can read them in the “What this skill does” section above.

Prefer to read the source first? Open on GitHub.

When Claude uses it

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.

What this skill does

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

Always validate after configuration changes - Use bundle validate --strict --target <target> after any change
Use reference documentation - Consult the appropriate reference file for detailed patterns and examples
Follow naming conventions - Resource files should use <name>.<resource_type>.yml format
Path resolution is critical - Paths differ based on file location (see Bundle Structure reference)
Preserve existing structure - Keep user comments and structure when editing YAML files
Use variables - Parameterize catalog, schema, and warehouse for multi-environment support