Databricks Agent Skills

Skills for AI coding assistants (Claude Code, Cursor, etc.) that provide Databricks-specific guidance.

Installation

Two install paths cover the stable skills. They install to different places but end up loaded by the same agents — pick whichever fits your workflow.

• Databricks CLI writes SKILL.md files directly into each agent's skill directory (~/.claude/skills/, ~/.cursor/extensions/<...>, etc.).
• Plugin marketplaces (Claude Code, Cursor) cache the plugin under the agent's plugin directory (e.g. ~/.claude/plugins/cache/databricks-skills/); the agent discovers skills from there.

Via the Databricks CLI (canonical; supports experimental skills):

databricks aitools install

The CLI auto-detects your coding agent(s) and installs the stable skills to the right location:

• Claude Code → ~/.claude/skills/
• Cursor, Codex CLI, OpenCode, GitHub Copilot, Antigravity → their respective skill directories

For finer control, use the aitools skills install subcommand directly — it accepts a positional skill name and an --experimental flag (see the Experimental Skills section).

Via the Claude Code plugin marketplace (stable skills only — installs every skill under ./skills/):

/plugin marketplace add databricks/databricks-agent-skills
/plugin install databricks-skills

Via the Cursor plugin marketplace:

/add-plugin databricks-skills

CLI vs plugin marketplace

	CLI	Plugin marketplace
Stable skills	✅ (default)	✅
Experimental skills	✅ (with --experimental or by name)	❌
Per-skill selection	✅ (databricks aitools install <name>)	❌ (all-or-nothing)
Updates	databricks aitools update	Plugin marketplace update flow
Required outside the agent	Databricks CLI v1.0.0+	None

If in doubt, use the CLI — it's the canonical install path and the only one that exposes experimental skills.

Available Skills

Stable skills shipped from skills/:

• databricks-core — CLI, authentication, profile selection, data exploration. Parent skill for all product skills.
• databricks-apps — Build full-stack TypeScript apps on Databricks using AppKit.
• databricks-dabs — Declarative Automation Bundles (formerly Asset Bundles) for deploying and managing Databricks resources.
• databricks-jobs — Lakeflow Jobs orchestration: task types, triggers, schedules, notifications.
• databricks-lakebase — Lakebase Postgres: projects, branching, autoscaling, synced tables, Data API.
• databricks-model-serving — Model Serving endpoint management, AI Gateway, traffic config.
• databricks-pipelines — Lakeflow Spark Declarative Pipelines (formerly DLT) for batch and streaming.
• databricks-serverless-migration — Migrate classic-compute workloads to serverless compute.

Experimental Skills

The experimental/ directory contains additional skills imported from databricks-solutions/ai-dev-kit on a best-effort basis.

• Experimental skills are not officially supported — they may be used, but do not follow the same review / quality bar as the stable skills under skills/.
• They are not installed by default by databricks aitools install. Pass --experimental to install all of them, or install a specific one by name (with the --experimental flag — e.g. databricks aitools install databricks-iceberg --experimental).
• See experimental/README.md for the full list and caveats.

Structure

Each skill follows the Agent Skills Specification:

skill-name/
├── SKILL.md           # Main skill file with frontmatter + instructions
└── references/        # Additional documentation loaded on demand

Development

Adding New Skills

For a narrower variation of an existing skill, create a subskill that declares its parent via frontmatter. This is how the stable skills are organized today — each product skill sets parent: databricks-core.

---
name: "databricks-apps-chatbots"
description: "Databricks apps with chatbot features"
parent: databricks-apps
---

# Chatbot Apps

**FIRST**: Use the parent `databricks-apps` skill for app development basics.

Then apply these patterns:
- Pattern 1
- Pattern 2

This approach:

• Keeps the main skill stable and focused
• Allows experimentation without modifying core skills
• Makes it easy to follow the changes in the main skill

Manifest Management

manifest.json is generated by scripts/skills.py from the skill directories and frontmatter. Do not edit it by hand. CI rejects manual changes via two checks: content drift (parsed dict doesn't match what generate would produce) and canonical form (on-disk bytes don't match json.dumps(..., indent=2, sort_keys=True)).

Sync assets and regenerate the manifest after adding or updating skills:

python3 scripts/skills.py

Validate that assets and manifest are up to date (used by CI):

python3 scripts/skills.py validate

The manifest is consumed by the CLI to discover available skills.

Security

Please see SECURITY for vulnerability reporting guidelines.

Integrity

All future release tags will be GPG-signed and verifiable via git tag -v <tag>.

Contributing

• All changes require approval from a code owner (see CODEOWNERS).
• Documentation examples must follow least-privilege defaults — avoid suggesting elevated permissions or broad scopes unless explicitly necessary.