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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.

74

Quality

93%

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SKILL.md
Quality
Evals
Security

Quality

Content

86%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-engineered skill body: a sequenced workflow with named primitives, non-negotiable state coverage, gated AI-trust requirements, and clean one-level-deep references that all resolve to real files. The only deductions are minor phrasing redundancy and an implicit post-review feedback loop.

Suggestions

De-duplicate the Genie gate: the section states the all-five requirement twice ('implement ALL five' then 'ship all five') within consecutive sentences — keep one phrasing and drop the other to save tokens.

Make the workflow's feedback loop explicit: after the Review step, add 'if a checklist fails, revise the component plan and re-run the checklist' so the validate→fix→retry cycle is stated, not implied.

DimensionReasoningScore

Conciseness

The body is dense and assumes domain knowledge, but has minor redundancy: the Genie section says 'implement ALL five' and then 'ship all five' in consecutive sentences, and the frontmatter's dashboard-boundary is re-stated at length in the body where the skill has already fired. Not score 3: padding is minor and most tokens earn their place.

4 / 5

Actionability

For an instruction-only skill the guidance is fully actionable: exact component and hook names (Skeleton, Empty, Badge, Card, Alert, useAnalyticsQuery/queryKey, useGenieChat, colorPalette), an exact API route and headers (/api/whoami, x-forwarded-email), a concrete config key (user_api_scopes: [dashboards.genie]), and a copy-ready output-format template. Exact snippets are delegated to a verified reference file as directed.

5 / 5

Workflow Clarity

The 7-step Frame→Genre→Compose→Notation→Bind→States→Review sequence is clear and ends in a validation checkpoint ('run the checklists in both reference files'), but the recovery loop after a failed checklist — revising the component plan and re-checking — is implicit rather than an explicit fix-and-retry step. Not score 5 by the explicit-feedback-loop anchor; not score 3 since checkpoints and checklists are present.

4 / 5

Progressive Disclosure

The body is a true overview: all four cited paths (dashboard-patterns.md, ibcs-notation.md, appkit-cheatsheet.md, genie-ai-trust.md) exist in references/, are one level deep, and cross-reference only as siblings. The Genie section even gates loading its reference ('skip this section and references/genie-ai-trust.md entirely') — exemplary on-demand loading.

5 / 5

Total

18

/

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 exemplary description: concrete capabilities, explicit use/avoid triggers with quoted user phrasings, and hard routing boundaries against the closest sibling skills. Every clause carries routing or capability information with no padding.

DimensionReasoningScore

Specificity

The description names concrete deliverables ('KPI/overview pages, reports, charts, tables, and Genie/chat data assistants — mapped to concrete AppKit components') and enumerates specific design actions ('choosing genre, layout, charts, KPIs, semantic color, required states (loading/empty/error), IBCS notation, and AI-result trust'). Coverage is comprehensive with no generic filler, in third-person voice.

5 / 5

Completeness

It explicitly answers 'what' (design the UX of custom-code Databricks App data screens, mapped to AppKit components) and 'when' ('Use when BUILDING or reviewing the UI of an AppKit/React app that displays data or answers data questions'), plus explicit when-not routing to sibling skills.

5 / 5

Trigger Term Quality

It covers the natural terms a user would say — 'dashboard', 'chart', 'KPI', 'report', 'table', 'Genie/chat', 'build', 'reviewing the UI' — and even quotes exact user phrasings ('A plain "create a dashboard" request'). Synonyms and colloquial variants are comprehensively represented.

5 / 5

Distinctiveness Conflict Risk

It carves a clear niche (custom-code AppKit/React data screens) and explicitly disambiguates the nearest conflicts: managed AI/BI Lakeview dashboards → 'databricks-aibi-dashboards, NOT this skill', and scaffolding/build/deploy → 'databricks-apps'. Minimal conflict risk.

5 / 5

Total

20

/

20

Passed

Validation

87%

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

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

relative_links

Relative link issues: 1 suspicious

Warning

Total

14

/

16

Passed

Repository

databricks/databricks-agent-skills

Path

plugins/databricks/claude/skills/databricks-app-design/SKILL.md

Reviewed

Table of Contents

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