/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](references/appkit/sql-queries.md) | | Writing UI components | [Frontend Guide](references/appkit/frontend.md) | | Using `useAnalyticsQuery` | [AppKit SDK](references/appkit/appkit-sdk.md) | | Adding API endpoints | [Custom Endpoints Guide](references/appkit/custom-endpoints.md) | | Using Lakebase (OLTP database) | [Lakebase Guide](references/appkit/lakebase.md) | | Adding Genie chat / Genie-powered apps | [Genie Guide](references/appkit/genie.md) — follow the Genie agent workflow below | | Using Model Serving (ML inference) | [Model Serving Guide](references/appkit/model-serving.md) | | Typed data contracts (proto-first design) | [Proto-First Guide](references/appkit/proto-first.md) and [Plugin Contracts](references/appkit/proto-contracts.md) | | Managing files in UC Volumes | [Files Guide](references/appkit/files.md) | | Triggering / monitoring Lakeflow Jobs from the app | [Jobs Guide](references/appkit/jobs.md) | | Platform rules (permissions, deployment, limits) | [Platform Guide](references/platform-guide.md) — READ for ALL apps including AppKit | | Non-AppKit app (Streamlit, FastAPI, Flask, Gradio, Next.js, etc.) | [Other Frameworks](references/other-frameworks.md) |

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](references/testing.md).
• **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](references/testing.md) 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 type