Databricks App Design

Data App Design

Make Databricks data + AI apps that communicate clearly and compile to real AppKit code. This skill merges two bodies of knowledge and binds them to implementation:

• Composition — what to show, how much to abstract, how to lay it out → references/dashboard-patterns.md
• Notation — make comparable things look comparable; honest scales; scenario marks → references/ibcs-notation.md
• Implementation — the exact AppKit components, hooks, and tokens to use → references/appkit-cheatsheet.md

Design advice that doesn't name a real component is incomplete. Always end at a component plan.

When to use / when NOT

• USE for: the data screens of a custom-code Databricks App (AppKit/React) — overview/KPI pages, reports, metric/ontology pages, variance analysis, charts, tables, and Genie/NL data surfaces — design or critique.
• Do NOT use for: authoring managed AI/BI (Lakeview) dashboards (→ databricks-aibi-dashboards), generic frontend (forms, auth, settings, marketing), or scaffolding/build/deploy (→ databricks-apps). A plain "create a dashboard" / "build a dashboard" request (no app / AppKit / React / custom-code signal) means a managed AI/BI (Lakeview) dashboard → use databricks-aibi-dashboards, not this skill. If a request is "add a form", "deploy this", or "build a Lakeview / AI-BI dashboard", this skill should not fire.
• Relationship: databricks-apps builds/runs the app; this skill decides what the data screens should look like and which primitives realize them.

Workflow

1. Frame — audience, the decision/question, refresh cadence, device, primary task. One sentence.
2. Genre — pick the closest from dashboard-patterns.md (static / analytic / magazine / infographic / repository / embedded mini). State it.
3. Compose — choose content + composition patterns (data abstraction, meta-info, layout, interaction, color). Make the tradeoff explicit: what's summarized, hidden, paginated, or made interactive — and why.
4. Apply notation — run the relevant ibcs-notation.md rules: message-in-title, scenario marks (actual/PY/plan/forecast), honest scales, semantic color. On any chart-vocabulary conflict, IBCS wins (see the conflict note in that file).
5. Bind to components — map every element to a primitive that's actually exported from @databricks/appkit / @databricks/appkit-ui (see appkit-cheatsheet.md); never cite a component AppKit doesn't ship. There's no prebuilt KPI/trend/distribution card — compose those from primitives, following the notation rules. Use colorPalette + semantic tokens, never hardcoded hex. Bind data with useAnalyticsQuery/queryKey + sql.* params.
6. Cover the states — every data view must handle loading / empty / error / partial (see checklist).
7. Review — run the checklists in both reference files; lead critiques with the highest-impact comprehension or integrity issue, citing the affected component/file.

Required states & data realism (non-negotiable for data apps)

• Loading → Skeleton; Empty → Empty with a useful next action; Error → inline message, never a blank panel; Partial/stale → show what you have + a freshness note.
• Every KPI shows unit + period + comparison + freshness/source (mirror the metric definition; don't show a number with no provenance).
• Large tables → server-side pagination/sort/filter, not client-side over a huge result set.
• Long-running queries → optimistic loading + timeout/error UX.

AI / Genie surfaces (the "AI" half)

Gate: this section applies only if the app has a Genie / chat / natural-language / "ask your data" surface. For a pure dashboard / KPI / report app with no conversational input, skip this section and references/genie-ai-trust.md entirely. When it does apply, implement ALL five (code in references/genie-ai-trust.md): A Genie/chat/NL answer is only trustworthy if the user can see how it was produced and who it ran as. "Use GenieChat + a spinner" is NOT enough — for ANY Genie/chat surface, ship all five (copy the exact snippets from the reference):

1. Identity — a /api/whoami route (real x-forwarded-email/x-forwarded-user headers) + the signed-in user in a Badge. Claim OBO only if user_api_scopes: [dashboards.genie] is wired; otherwise disclose the query runs as the app's service principal.
2. Generated SQL — render attachments[].query in an inspectable "Generated SQL" Card; never hide how the answer was computed.
3. Streaming/status — reflect useGenieChat().status (streaming/error), never a frozen spinner.
4. Disclaimer — a persistent "AI-generated — verify" note per answer.
5. Governance + states — genie() space config + a truthful execution-identity note (OBO when user-scoped, else service principal) + empty/error/ambiguous handling (Empty, Alert).

Output formats

Design proposal:

## Direction[Genre, audience, primary task, design intent.]## Pattern & notation choices- Composition: [data info, meta info, layout, interaction, color]- Notation: [message, scenario marks, scales, semantic color]## Component plan        ← the part that makes it buildable- [element] → [AppKit component] (queryKey/props), [token/palette], states handled## Tradeoffs & risks[What's summarized/hidden/paginated/interactive; overload, scale, a11y, maintenance risks.]

Critique: lead with the top comprehension/integrity issue, cite the component/file, then list findings by impact, each with the concrete fix (which component/token/state to change).

Anti-patterns

• Producing a design memo with no component plan.
• "Use semantic color" without naming the token/palette.
• Naming a component AppKit doesn't export (e.g. a prebuilt KpiCard) — compose composites from published primitives instead.
• Adding interaction, pages, or density the task doesn't need (over-engineering a mock-first app).
• Forgetting loading/empty/error states, or KPIs with no freshness/source.