databricks-apps

name	databricks-apps
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.
compatibility	Requires databricks CLI (>= v0.294.0)
metadata	{"version":"0.1.2"}
parent	databricks-core

Databricks Apps Development

FIRST: Use the parent databricks-core skill for CLI basics, authentication, and profile selection.

Is this even a Databricks App? Don't assume an app is the only way to show data. For a simple "dashboard with a few charts" and no app-specific need, the managed AI/BI (Lakeview) dashboard is the simpler path → use the databricks-aibi-dashboards skill, not this one. Reach for a custom Databricks App only when the user needs something AI/BI can't give them — bespoke interactivity/components, write-back, embedded or auth-gated workflows, a Genie/chat surface inside the app — or explicitly asks for an app. If it's genuinely ambiguous, surface both options (managed AI/BI dashboard vs custom app) and let the user choose instead of defaulting to an app. Once an app is the right call, it's fine for this skill to favor an app-based dashboard, and databricks-app-design covers building it well.

For data UI design (required for any data-displaying app): once you've confirmed the user wants a custom-code app (not a managed AI/BI dashboard — see above), if the app shows ANY data — a KPI/overview page, report, chart, table, query results, OR a conversational / chat / Genie natural-language assistant — you MUST use the databricks-app-design skill (alongside this one) to decide layout, charts, KPIs, semantic color, required states, and AI-result trust, and map them to AppKit components. This includes chat/Genie apps, not just static data views — if in doubt, use it.

Build apps that deploy to Databricks Apps platform.

Required Reading by Phase

Phase	READ BEFORE proceeding
Scaffolding	⚠️ STOP — review the State Storage Guidance and complete the Data Access Decision Gate below before scaffolding. Parent databricks-core skill (auth, warehouse discovery); then run databricks apps manifest + databricks apps init with --features and --set (see AppKit section below)
Writing SQL queries	SQL Queries Guide
Writing UI components	Frontend Guide
Using useAnalyticsQuery	AppKit SDK
Adding API endpoints	Custom Endpoints Guide
Using Lakebase (OLTP database)	Lakebase Guide
Adding Genie chat / Genie-powered apps	Genie Guide — follow the Genie agent workflow below
Using Model Serving (ML inference)	Model Serving Guide
Typed data contracts (proto-first design)	Proto-First Guide and Plugin Contracts
Managing files in UC Volumes	Files Guide
Triggering / monitoring Lakeflow Jobs from the app	Jobs Guide
Platform rules (permissions, deployment, limits)	Platform Guide — READ for ALL apps including AppKit
Non-AppKit app (Streamlit, FastAPI, Flask, Gradio, Next.js, etc.)	Other Frameworks

Generic Guidelines

• App name: ≤26 characters, lowercase letters/numbers/hyphens only (no underscores). dev- prefix adds 4 chars, max 30 total.
• Validation: databricks apps validate --profile <PROFILE> before deploying.
• Smoke tests (AppKit only): ALWAYS update tests/smoke.spec.ts selectors BEFORE running validation. Default template checks for "Minimal Databricks App" heading and "hello world" text — these WILL fail in your custom app. See testing guide.
• Smoke test selectors: use only Playwright locator APIs — getByRole, getByText, getByPlaceholder, getByLabel. getByLabelText does not exist in Playwright (it is a React Testing Library method) and throws TypeError at runtime. See testing guide or npx playwright codegen.
• Smoke test data: keep result sets under the 1 MB analytics-event payload cap. Queries returning thousands of rows cause INVALID_REQUEST: Event exceeds max size of 1048576 bytes and net::ERR_ABORTED, leaving every asserted UI element absent. Use LIMIT or an aggregated query (e.g. COUNT(*) GROUP BY status) — never raw row dumps.
• AppKit version: never override the @databricks/appkit or @databricks/appkit-ui version in package.json — databricks apps init sets the correct version. Do not run npm install @databricks/appkit@<version> unless explicitly asked by the user. If you need a different version, re-scaffold with databricks apps init --version <version>.
• Authentication: covered by parent databricks-core skill.
• AppKit API surface: before writing code that calls AppKit APIs (createApp, plugin shapes, useAnalyticsQuery, etc.), run npx @databricks/appkit docs <section> and use the actual signature. Training data has stale shapes; a single invented signature fails tsc --noEmit during validate. The docs ship with the installed AppKit and are the authoritative source.
• TypeScript casts: never use as unknown as <T> double-assertions — appkit lint enforces no-double-type-assertion and one violation fails the entire validate step. Instead: narrow with Zod (z.infer<typeof schema>), use a runtime type guard, or write a typed mapper function. If a query result needs reshaping, type the row schema via queryKey types rather than casting.

Project Structure (after databricks apps init --features analytics)

• client/src/App.tsx — main React component (start here)
• config/queries/*.sql — SQL query files (queryKey = filename without .sql)
• server/server.ts — backend entry (onPluginsReady + Express routes)
• tests/smoke.spec.ts — smoke test (⚠️ MUST UPDATE selectors for your app)
• client/src/appKitTypes.d.ts — auto-generated types (npm run typegen)

Project Structure (after databricks apps init --features lakebase)

• server/server.ts — backend with Lakebase pool + Express routes
• client/src/App.tsx — React frontend
• app.yaml — manifest with database resource declaration
• package.json — includes @databricks/lakebase dependency
• Note: No config/queries/ — Lakebase apps use appkit.lakebase.query() in Express routes, not SQL files

Data Discovery

Before writing any SQL, use the parent databricks-core skill for data exploration — search information_schema by keyword, then batch discover-schema for the tables you need. Do NOT skip this step.

State Storage Guidance (evaluate BEFORE the Decision Gate):

If the user's app description involves storing or persisting data — forms, CRUD operations, user submissions, orders, todos, or other user-generated content — the app likely needs a Lakebase database.

1. Ask the user whether the app needs persistent storage (Lakebase) before scaffolding. Do not silently add Lakebase.
2. If confirmed, ask whether to reuse an existing Lakebase project or create a new one (same as the Genie flow below) — never create one silently. Use the databricks-lakebase skill to obtain the branch and database resource names:
   • Reusing: list with databricks postgres list-projects, then list-branches / list-databases, and let the user pick the project, branch, and database; confirm which schema the app will own (a fresh/dedicated schema avoids the service-principal ownership conflict).
   • Creating: create a new project (it auto-provisions a production branch + databricks_postgres database).
3. Scaffold with --features lakebase and pass --set lakebase.postgres.branch=<BRANCH_NAME> --set lakebase.postgres.database=<DATABASE_NAME>.
4. If the app also reads from Unity Catalog tables, proceed to the Data Access Decision Gate below to determine whether to add --features analytics or use Lakebase synced tables.

Do NOT add Lakebase to analytics, dashboard, or visualization apps unless the user explicitly requests persistent write-back storage. Read-only data display, filters, and preferences do not require a database.

Development Workflow (FOLLOW THIS ORDER)

Data Access Decision Gate (REQUIRED before scaffolding):

If the app reads from Unity Catalog / lakehouse tables, you MUST show the comparison below to the user and ask them to choose. Do not skip this. Do not choose for them.

	(A) Lakebase synced tables	(B) Analytics
Speed	Sub-second responses	Takes a few seconds
Best for	Full-text search, typeahead, autocomplete, real-time lookups, operational apps	Dashboards, charts, aggregations, KPIs, filtered queries, browsing
How it works	Data synced from Delta into Lakebase Postgres	Queries run on SQL warehouse at read time

After showing the table, add a brief recommendation. Default to recommending Analytics (B) for most read-only apps — dashboards, charts, filtered queries, browsing, and aggregations. Recommend Lakebase synced tables (A) only when the app needs sub-second latency for full-text search, typeahead/autocomplete, real-time lookups by ID, or operational data serving. Note: "search" or "filter" in a prompt usually means SQL WHERE clauses (Analytics), not full-text search (Lakebase). Always let the user make the final call.

After the user chooses:

• (A) Lakebase synced tables → scaffold with --features lakebase. See Lakebase Guide for full workflow.
• (B) Analytics → scaffold with --features analytics.
• Both → scaffold with --features analytics,lakebase if the app needs both patterns.
• If the app does NOT read Unity Catalog data (pure CRUD, Genie, Model Serving), skip this gate and scaffold with the appropriate --features flag.

Analytics apps (--features analytics):

1. Create SQL files in config/queries/
2. Run npm run typegen — verify all queries show ✓
3. Read client/src/appKitTypes.d.ts to see generated types
4. THEN write App.tsx using the generated types
5. Update tests/smoke.spec.ts selectors
6. Run databricks apps validate --profile <PROFILE>

DO NOT write UI code before running typegen — types won't exist and you'll waste time on compilation errors.

Lakebase apps (--features lakebase): No SQL files or typegen. See Lakebase Guide for the onPluginsReady pattern: initialize schema at startup, register Express routes in server/server.ts, then build the React frontend.

When to Use What

After completing the decision gate above, use this routing table:

• Read analytics data → display in chart/table: Use visualization components with queryKey prop
• Read analytics data → custom display (KPIs, cards): Use useAnalyticsQuery hook
• Read analytics data → need computation before display: Still use useAnalyticsQuery, transform client-side
• Read lakehouse data at low latency (lookups, search, catalogs): Use Lakebase synced tables — see Lakebase Guide
• Read/write persistent data (users, orders, CRUD state): Use Lakebase via Express routes in onPluginsReady — see Lakebase Guide
• Natural language query interface over tables (Genie): Use genie() plugin — see Genie Guide
• Call ML model endpoint: Use serving() plugin — see Model Serving Guide
• Trigger or monitor a Lakeflow Job from the app: Use the jobs() plugin — see Jobs Guide
• ⚠️ NEVER add custom endpoints to run SELECT queries against the warehouse — always use SQL files in config/queries/
• ⚠️ NEVER use useAnalyticsQuery for Lakebase data — it queries the SQL warehouse only

Frameworks

AppKit (Recommended)

TypeScript/React framework with type-safe SQL queries and built-in components.

Official Documentation — the source of truth for all API details:

npx @databricks/appkit docs                              # ← ALWAYS start here to see available pages
npx @databricks/appkit docs <query>                      # view a section by name or doc path
npx @databricks/appkit docs --full                       # full index with all API entries
npx @databricks/appkit docs "appkit-ui API reference"    # example: section by name
npx @databricks/appkit docs ./docs/plugins/analytics.md  # example: specific doc file

DO NOT guess doc paths. Run without args first, pick from the index. The <query> argument accepts both section names (from the index) and file paths. Docs are the authority on component props, hook signatures, and server APIs — skill files only cover anti-patterns and gotchas.

App Manifest and Scaffolding

Agent workflow for scaffolding: get the manifest first, then build the init command.

1. Get the manifest (JSON schema describing plugins and their resources):

   databricks apps manifest --profile <PROFILE>
   # See plugins available in a specific AppKit version:
   databricks apps manifest --version <VERSION> --profile <PROFILE>
   # Custom template:
   databricks apps manifest --template <GIT_URL> --profile <PROFILE>
   

   The output defines:

   • Plugins: each has a key (plugin ID for --features), plus requiredByTemplate, and resources.
   • requiredByTemplate: If true, that plugin is mandatory for this template — do not add it to --features (it is included automatically); you must still supply all of its required resources via --set. If false or absent, the plugin is optional — add it to --features only when the user's prompt indicates they want that capability (e.g. analytics/SQL), and then supply its required resources via --set.
   • Resources: Each plugin has resources.required and resources.optional (arrays). Each item has resourceKey and fields (object: field name → description/env). Use --set <plugin>.<resourceKey>.<field>=<value> for each required resource field of every plugin you include.

2. Scaffold (DO NOT use npx; use the CLI only):

   databricks apps init --name <NAME> --features <plugin1>,<plugin2> \
     --set <plugin1>.<resourceKey>.<field>=<value> \
     --set <plugin2>.<resourceKey>.<field>=<value> \
     --description "<DESC>" --run none --profile <PROFILE>
   # --run none: skip auto-run after scaffolding (review code first)
   # With custom template:
   databricks apps init --template <GIT_URL> --name <NAME> --features ... --set ... --profile <PROFILE>
   

   Optionally use --version <VERSION> to target a specific AppKit version.

   • Required: --name, --profile. Name: ≤26 chars, lowercase letters/numbers/hyphens only. Use --features only for optional plugins the user wants (plugins with requiredByTemplate: false or absent); mandatory plugins must not be listed in --features.
   • Resources: Pass --set for every required resource (each field in resources.required) for (1) all plugins with requiredByTemplate: true, and (2) any optional plugins you added to --features. Add --set for resources.optional only when the user requests them.
   • Discovery: Use the parent databricks-core skill to resolve IDs (e.g. warehouse: databricks warehouses list --profile <PROFILE> or databricks experimental aitools tools get-default-warehouse --profile <PROFILE>).

DO NOT guess plugin names, resource keys, or property names — always derive them from databricks apps manifest output. Example: if the manifest shows plugin analytics with a required resource resourceKey: "sql-warehouse" and fields: { "id": ... }, include --set analytics.sql-warehouse.id=<ID>.

Scaffolding Rules Protocol — databricks apps manifest may emit scaffolding.rules at the template level (top-level scaffolding.rules) and on individual plugins (plugins[].scaffolding.rules). Each block has must / should / never arrays of short directive strings. Consume them as follows:

1. Gather — for every plugin in your final --features list AND every plugin with requiredByTemplate: true, read plugins[].scaffolding.rules. Union those with the top-level template scaffolding.rules into one working set, tagged by source (template vs <plugin>).
2. Precedence — manifest rules override the directives baked into this skill. Where the manifest is silent on a topic, this skill's content is the floor.
3. Phase ordering — rules whose text begins with Before init MUST be executed before databricks apps init. Rules beginning with After init MUST be executed after init completes (e.g. migrations, typegen, connectivity checks). Rules without a phase prefix apply throughout the scaffold/develop loop.
4. Conflict detection — if a plugin must rule contradicts a template never rule on the same target (or vice versa), STOP and ask the user which to follow before proceeding. Do not silently pick one. Treat must vs never on the same action as a conflict; should is advisory and does not block.
5. Reporting — before running databricks apps init, surface the merged working set to the user grouped by phase (Before init / After init / Always) and by severity (must / should / never), so the active guardrails are explicit.

READ AppKit Overview for project structure, workflow, and pre-implementation checklist.

Genie Agent Workflow — when the user wants a Genie-powered app, do not start by asking for a Genie Space ID. Instead:

1. Ask which Unity Catalog tables the app should query (fully qualified: catalog.schema.table).
2. Ask whether to reuse an existing Genie space or create a new one.
3. If creating: discover the warehouse, then create the space with databricks genie create-space (see Genie Guide for syntax and serialized space format).
4. If reusing: discover existing spaces with databricks genie list-spaces --profile <PROFILE> and let the user pick.
5. Scaffold or wire the space ID into the app — derive --set keys from databricks apps manifest.

Read the Genie Guide for configuration, SSE endpoints, and frontend integration.

Common Scaffolding Mistakes

# ❌ WRONG: name is NOT a positional argument
databricks apps init --features analytics my-app-name
# → "unknown command" error

# ✅ CORRECT: use --name flag
databricks apps init --name my-app-name --features analytics --set "..." --profile <PROFILE>

Directory Naming

databricks apps init creates directories in kebab-case matching the app name. App names must be lowercase with hyphens only (≤26 chars).

Other Frameworks (Streamlit, FastAPI, Flask, Gradio, Dash, Next.js, etc.)

Databricks Apps supports any framework that runs as an HTTP server. LLMs already know these frameworks — the challenge is Databricks platform integration.

READ Other Frameworks Guide BEFORE building any non-AppKit app. It covers port/host configuration, app.yaml and databricks.yml setup, dependency management, networking, and framework-specific gotchas.

Post-Deploy Verification

After deploying, verify the app is running:

databricks apps get <app-name> --profile <PROFILE> -o json   # Check app_status.state: RUNNING; the `url` field is the app's URL
databricks apps logs <app-name> --follow --profile <PROFILE>  # Stream live logs (Ctrl+C to stop)

Note: databricks apps logs requires OAuth authentication and does not work with PAT. Use databricks apps get for status checks if using PAT auth.