Databricks

Core skill for Databricks CLI, authentication, and data exploration.

Product Skills

For specific products, use dedicated skills:

• databricks-jobs - Lakeflow Jobs development and deployment
• databricks-pipelines - Lakeflow Spark Declarative Pipelines (batch and streaming data pipelines)
• databricks-apps - Full-stack TypeScript app development and deployment
• databricks-lakebase - Lakebase Postgres Autoscaling project management
• databricks-model-serving - Model Serving endpoint management and inference

For data discovery, exploration, and query generation — finding tables, answering natural-language questions about the data, or generating SQL — use databricks-data-discovery (it asks Genie One first, then falls back to manual exploration). If it isn't installed, use the AI-tool commands below and Manual Data Exploration.

Prerequisites

1. CLI installed and current: the CLI must be >= v1.0.0. A CLI that is present but too old is not "good enough" — it must be upgraded, not worked around.

   • Run the floor check in CLI Installation. If it reports UPGRADE or INSTALL: STOP and follow that reference file to upgrade or install — do not proceed or tell the user their CLI is fine.
   • If another skill routed you here to upgrade (it needs a newer CLI than v1.0.0 — e.g. databricks-setup-local needs v1.12.0), that skill already detected the gap. A passing v1.0.0 floor check is not enough: follow the Update / repair procedures to install the latest stable, which satisfies any skill's floor.
   • Note: In sandboxed environments (Cursor IDE, containers), install commands write outside the workspace and may be blocked. Present the install command to the user and ask them to run it in their own terminal.
   • Exception: If CLI installation is blocked (sandboxed containers, restricted environments), ask the user whether to fall back to direct REST API calls using DATABRICKS_HOST and DATABRICKS_TOKEN environment variables if present in the shell. See the Databricks REST API docs.

2. Authenticated: databricks auth profiles

   • If not: see CLI Authentication

Profile Selection - CRITICAL

NEVER auto-select a profile.

1. List profiles: databricks auth profiles
2. Present ALL profiles to user with workspace URLs
3. Let user choose (even if only one exists)
4. Offer to create new profile if needed

Claude Code - IMPORTANT

Each Bash command runs in a separate shell session.

# WORKS: --profile flag
databricks apps list --profile my-workspace

# WORKS: chained with &&
export DATABRICKS_CONFIG_PROFILE=my-workspace && databricks apps list

# DOES NOT WORK: separate commands
export DATABRICKS_CONFIG_PROFILE=my-workspace
databricks apps list  # profile not set!

Data Exploration — Use AI Tools

Use these instead of manually navigating catalogs/schemas/tables:

# discover table structure (columns, types, sample data, stats)
databricks experimental aitools tools discover-schema catalog.schema.table --profile <PROFILE>

# run ad-hoc SQL queries
databricks experimental aitools tools query "SELECT * FROM table LIMIT 10" --profile <PROFILE>

# find the default warehouse
databricks experimental aitools tools get-default-warehouse --profile <PROFILE>

Names are literal. Use catalog/schema/table names exactly as given — never change a hyphen to an underscore or otherwise normalize them. In SQL, backtick-quote any name part with special characters (e.g. `my-catalog`.schema.table); unquoted hyphens cause a parse error.

These commands are first-class for running known SQL and profiling — Genie isn't required for that. For natural-language data questions, locating data you can't pin down, or generating a query from a question, prefer the databricks-data-discovery skill (above) if it's installed. See Manual Data Exploration for the full command surface, quoting rules, and troubleshooting.

Quick Reference

⚠️ CRITICAL: Some commands use positional arguments, not flags

# current user
databricks current-user me --profile <PROFILE>

# list resources
databricks apps list --profile <PROFILE>
databricks jobs list --profile <PROFILE>
databricks clusters list --profile <PROFILE>
databricks warehouses list --profile <PROFILE>
databricks pipelines list --profile <PROFILE>
databricks serving-endpoints list --profile <PROFILE>

# ⚠️ Unity Catalog — POSITIONAL arguments (NOT flags!)
databricks catalogs list --profile <PROFILE>

# ✅ CORRECT: positional args
databricks schemas list <CATALOG> --profile <PROFILE>
databricks tables list <CATALOG> <SCHEMA> --profile <PROFILE>
databricks tables get <CATALOG>.<SCHEMA>.<TABLE> --profile <PROFILE>

# ❌ WRONG: these flags/commands DON'T EXIST
# databricks schemas list --catalog-name <CATALOG>    ← WILL FAIL
# databricks tables list --catalog <CATALOG>           ← WILL FAIL
# databricks sql-warehouses list                       ← doesn't exist, use `warehouses list`
# databricks execute-statement                         ← doesn't exist, use `experimental aitools tools query`
# databricks sql execute                               ← doesn't exist, use `experimental aitools tools query`

# When in doubt, check help:
# databricks schemas list --help

# get details
databricks apps get <NAME> --profile <PROFILE>
databricks jobs get --job-id <ID> --profile <PROFILE>
databricks clusters get --cluster-id <ID> --profile <PROFILE>

# bundles
databricks bundle init --profile <PROFILE>
databricks bundle validate --profile <PROFILE>
databricks bundle deploy -t <TARGET> --profile <PROFILE>
databricks bundle run <RESOURCE> -t <TARGET> --profile <PROFILE>

Troubleshooting

Required Reading by Task

Reference Guides

• CLI Installation
• CLI Authentication
• Manual Data Exploration