Conciseness

The body is dense with non-obvious platform specifics Claude cannot know (1 MB analytics-event payload cap, CLI >= v1.20.0 auth-mode gate, getByLabelText not existing in Playwright, no-double-type-assertion lint), so most tokens earn their place. However, real repetition exists: the 26-character app-name rule appears three times (Generic Guidelines, Scaffolding, Directory Naming sections) and the dashboard-vs-app disambiguation overlaps the frontmatter description. Anchor 4 ('efficient; minor instances that could be trimmed') fits better than 3, since the padding is duplication rather than explanation of known concepts.

Actionability

Copy-paste-ready commands throughout: exact 'databricks apps init --name <NAME> --features ... --set ...' invocations, wrong-vs-correct examples for common mistakes, quoted error strings ('INVALID_REQUEST: Event exceeds max size of 1048576 bytes'), and post-deploy verification commands with specific fields to check ('app_status.state: RUNNING'). Fully executable and covers the common cases.

Workflow Clarity

'Development Workflow (FOLLOW THIS ORDER)' with numbered steps, a REQUIRED Data Access Decision Gate before scaffolding, explicit validation checkpoints ('databricks apps validate', 'verify all queries show ✓', 'DO NOT write UI code before running typegen'), a conflict-detection STOP rule in the Scaffolding Rules Protocol, and post-deploy verification. Clear sequencing with explicit validation and error-recovery feedback loops.

Progressive Disclosure

A 'Required Reading by Phase' table maps each task to exactly one reference file; all 18 referenced paths (references/*.md and references/appkit/*.md) exist on disk and are one level deep. The body stays an overview/routing layer while details (SQL, frontend, Genie, Lakebase, platform rules) are appropriately split into separate files.

Specificity

Names the domain ('Build apps on Databricks Apps platform') and several concrete actions ('create data apps, analytics tools, or custom interactive visualizations', 'Evaluates data access patterns (analytics vs Lakebase synced tables) before scaffolding'), but coverage stays high-level — no scaffolding/deploy specifics. Anchor 4 ('several specific actions; minor gaps') fits better than 5, which demands comprehensive concrete action coverage.

Completeness

Explicitly answers both: what — 'Build apps on Databricks Apps platform'; when — 'Use when asked to create data apps, analytics tools, or custom interactive visualizations', with concrete trigger phrases. This matches the anchor-5 example pattern exactly.

Trigger Term Quality

Natural user phrases like 'create data apps', 'analytics tools', 'custom interactive visualizations', and 'create a dashboard' (used for routing away) are present and natural. A few common variations users would say — e.g. 'Databricks app', 'deploy an app', or framework names like Streamlit — are missing, matching anchor 4 rather than 5.

Distinctiveness Conflict Risk

It pre-empts the most likely mis-trigger explicitly: 'A plain "create a dashboard" request means a managed AI/BI (Lakeview) dashboard → use databricks-aibi-dashboards, not this skill.' A clear niche with distinct triggers and minimal conflict risk.