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databricks-app-design

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.

76

Quality

96%

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SKILL.md
Quality
Evals
Security

Quality

Content

93%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-structured, token-efficient instruction skill: a clear sequenced workflow, concrete component/token/API bindings, mandatory-state and AI-trust specs, and clean progressive disclosure into four real reference files. The only gap is the absence of an explicit gated validate/retry loop in the workflow, which is less critical for a non-destructive design skill.

Suggestions

Add an explicit 'if a checklist item fails, fix and re-run the relevant checklist before finalizing' feedback loop to step 7 (Review) to push workflow_clarity to a clean 5.

Consider inlining one minimal end-to-end component-plan example (element → AppKit component + queryKey + token + states) in the body so a single common case is copy-paste ready without opening references.

Confirm the 'Compose' tradeoff step names which reference sub-section to consult for each pattern (data abstraction / meta-info / layout / interaction / color) to tighten navigation into dashboard-patterns.md.

DimensionReasoningScore

Conciseness

The body is lean and instruction-dense — it assumes Claude's competence, never explains what a chart/KPI/IBCS is, and every section (workflow, states, AI surfaces, output template, anti-patterns) carries actionable content with no padding.

5 / 5

Actionability

Names exact, real exports and APIs — '@databricks/appkit / @databricks/appkit-ui' exports, 'useAnalyticsQuery'/'queryKey'+'sql.*', 'colorPalette'+semantic tokens, 'Skeleton'/'Empty'/'Card'/'Badge'/'GenieChat', '/api/whoami', 'x-forwarded-email', 'user_api_scopes: [dashboards.genie]', 'attachments[].query', 'useGenieChat().status' — and provides a concrete output template; per the instruction-skill note, deferring copy-paste snippets to references is not penalized when guidance is this actionable.

5 / 5

Workflow Clarity

A clear 7-step sequence (Frame→Genre→Compose→Apply notation→Bind→Cover states→Review) ending with a checklist-driven review step, but it stops short of an explicit gated validate→fix→retry loop, so it sits just below the anchor that requires explicit error-recovery feedback.

4 / 5

Progressive Disclosure

A concise overview body points to four well-signaled one-level-deep reference files (dashboard-patterns, ibcs-notation, appkit-cheatsheet, genie-ai-trust), each tied to a specific role (composition/notation/implementation/AI-trust); all four files were verified to exist, with no nested-reference chaining.

5 / 5

Total

19

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

An information-dense, well-targeted description that states concrete capabilities, gives explicit use/when-NOT triggers including a redirect for the most common ambiguous request, and cleanly distinguishes itself from sibling Databricks skills. Dense rather than padded, with no vague fluff.

DimensionReasoningScore

Specificity

Names the domain (Databricks App data screens) and a comprehensive set of concrete actions — 'choosing genre, layout, charts, KPIs, semantic color, required states (loading/empty/error), IBCS notation, and AI-result trust' — plus concrete screen types (KPI/overview pages, reports, charts, tables, Genie/chat) mapped to AppKit components.

5 / 5

Completeness

Explicitly answers both 'what' ('Design the UX of custom-code Databricks Apps ... data screens ... mapped to concrete AppKit components') and 'when' ('Use when BUILDING or reviewing the UI of an AppKit/React app that displays data or answers data questions') with concrete trigger phrases and explicit when-NOT boundaries.

5 / 5

Trigger Term Quality

Covers the natural phrases users would say — 'dashboard', 'KPI', 'chart', 'table', 'report', 'Genie/chat', 'ask your data' — with synonyms (KPI/overview, Genie/chat/AI surface) and an explicit redirect for the common ambiguous 'create a dashboard' request.

5 / 5

Distinctiveness Conflict Risk

Carves a clear niche (custom-code AppKit/React data screens) and explicitly disambiguates from sibling skills — 'use databricks-aibi-dashboards, NOT this skill' for plain dashboard requests and 'databricks-apps' for scaffolding/build/deploy — minimizing wrong-skill triggers.

5 / 5

Total

20

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
databricks/databricks-agent-skills
Reviewed

Table of Contents

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