Databricks CLI operations and the parent/entry-point skill for Databricks CLI use: authentication, profile selection, and bundles. Load this first for CLI, auth, profile, and bundle tasks, then load the matching product skill. For finding or exploring data, answering questions about the data, or generating SQL, load the databricks-data-discovery skill (it routes to Genie One). Contains up-to-date guidelines for Databricks-related CLI tasks.
The English summary is being prepared. Showing the original description.
See how to installReview the instructions the agent will receive before you install.
Core skill for Databricks CLI, authentication, and data exploration.
For specific products, use dedicated skills:
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.
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.
UPGRADE or INSTALL: STOP and follow that reference file to upgrade or install — do
not proceed or tell the user their CLI is fine.databricks-setup-local needs v1.16.0), 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.DATABRICKS_HOST and DATABRICKS_TOKEN environment variables if present in the shell. See the Databricks REST API docs.Authenticated: databricks auth profiles
NEVER auto-select a profile.
databricks auth profilesEach 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!
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.
⚠️ 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>
| Error | Solution |
|---|---|
cannot configure default credentials | Use --profile flag or authenticate first |
configuration does not support OAuth tokens | The command requires OAuth (e.g., databricks apps logs). Re-authenticate with databricks auth login --host <URL> --profile <PROFILE>. See CLI Authentication. |
PERMISSION_DENIED | Check workspace/UC permissions |
RESOURCE_DOES_NOT_EXIST | Verify resource name/id and profile |
| Task | READ BEFORE proceeding |
|---|---|
| First time setup | CLI Installation |
| Auth issues / new workspace | CLI Authentication |
| Exploring tables/schemas | Manual Data Exploration (or databricks-data-discovery if installed) |
| Deploying jobs/pipelines | Use /databricks-dabs |
No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Create Agent Bricks: Knowledge Assistants (KA) for document Q&A and Supervisor Agents for multi-agent orchestration (MAS).
The English summary is being prepared. Showing the original description.
Use Databricks built-in AI Functions (ai_classify, ai_extract, ai_summarize, ai_mask, ai_translate, ai_fix_grammar, ai_gen, ai_analyze_sentiment, ai_similarity, ai_parse_document, ai_prep_search, ai_query, ai_forecast) to add AI capabilities directly to SQL and PySpark pipelines without managing model endpoints. Also covers document parsing and building custom RAG pipelines (parse → prep_search → index → query).
The English summary is being prepared. Showing the original description.
Databricks AI Runtime, the `databricks air` CLI commands for submitting and managing GPU training workloads on Databricks serverless compute. Use for: writing and submitting `databricks air` workload YAML, passing hyperparameters and secrets, checking run status, listing/cancelling runs, streaming a run's logs and watching its progress, custom Docker image setup, and environment configuration.
The English summary is being prepared. Showing the original description.
Create Databricks AI/BI dashboards. Must use when creating, updating, or deploying Lakeview dashboards as Databricks Dashboard have a unique json structure. CRITICAL: You MUST test ALL SQL queries via CLI BEFORE deploying. Follow guidelines strictly.
The English summary is being prepared. Showing the original description.
Design the UX of custom-code Databricks Apps (AppKit/React) data screens — KPI/overview pages, reports, charts, tables, and Genie/chat data assistants — mapped to concrete AppKit components. Use when BUILDING or reviewing the UI of an AppKit/React app that displays data or answers data questions: choosing genre, layout, charts, KPIs, semantic color, required states (loading/empty/error), IBCS notation, and AI-result trust (showing generated SQL/sources for Genie/chat). A plain "create a dashboard" request means a managed AI/BI (Lakeview) dashboard → use databricks-aibi-dashboards, NOT this skill. Also NOT for non-data frontend (forms, settings, auth, marketing) or scaffolding/build/deploy (→ databricks-apps). Complements databricks-apps; use it alongside whenever a custom app has a chart, table, KPI, report, or Genie/chat/AI surface.
The English summary is being prepared. Showing the original description.
Build apps on Databricks Apps platform. Use when asked to create data apps, analytics tools, or custom interactive visualizations. A plain "create a dashboard" request means a managed AI/BI (Lakeview) dashboard → use databricks-aibi-dashboards, not this skill. Evaluates data access patterns (analytics vs Lakebase synced tables) before scaffolding. Invoke BEFORE starting implementation.
The English summary is being prepared. Showing the original description.