Conciseness

Core content is high-value non-obvious gotchas, but there is noticeable redundancy: the managed-dashboard-vs-app disambiguation appears in the description, in the 'Is this even a Databricks App?' paragraph, and again in the data-UI-design paragraph; Lakebase reuse/create guidance repeats across the State Storage and Genie sections; and the auth-mode and Scaffolding Rules Protocol sections are padded. This exceeds 'minor instances of over-explanation' (4) but is far from 'extensively explains concepts Claude already knows' (1).

Actionability

Fully executable throughout: exact CLI commands with flags (databricks apps init --name ... --features ... --set ...), wrong-vs-right examples ('name is NOT a positional argument'), exact error strings ('INVALID_REQUEST: Event exceeds max size of 1048576 bytes'), concrete smoke-test locator APIs, and post-deploy verification commands. Copy-paste ready and covers the common cases — matches the top anchor.

Workflow Clarity

'Development Workflow (FOLLOW THIS ORDER)' sequences the decision gate, typegen-before-UI, selector updates, and validation; checkpoints are explicit ('DO NOT write UI code before running typegen', 'databricks apps validate --profile <PROFILE>', post-deploy status checks), with STOP-and-ask error-recovery rules for manifest conflicts and ambiguous routing — matching the 'clear sequence with explicit validation steps; feedback loops; checklists' anchor. Validation is present, so the missing-validation cap does not apply.

Progressive Disclosure

A 'Required Reading by Phase' table routes each development phase to one of 18 well-signaled, one-level-deep reference files (all verified present in references/appkit/, plus platform-guide.md, testing.md, other-frameworks.md); the body keeps only routing, rules, and gotchas while all detail lives in the bundle — matching the 'clear overview with well-signaled one-level-deep references' anchor.

Specificity

Names the domain ('Build apps on Databricks Apps platform') with 1-2 concrete actions ('evaluates data access patterns (analytics vs Lakebase synced tables) before scaffolding'), but does not enumerate several specific actions like scaffolding, validating, or deploying — matching the '1-2 concrete actions, not comprehensive' anchor rather than the 'several specific actions' anchor above.

Completeness

Explicitly answers both what ('Build apps on Databricks Apps platform. Evaluates data access patterns... before scaffolding') and when ('Use when asked to create data apps, analytics tools, or custom interactive visualizations') with concrete trigger phrases — matching the top anchor; the when-clause is explicit, so the weakly-implied anchor (3) and the could-be-more-explicit anchor (4) both understate it.

Trigger Term Quality

Includes natural user phrases ('create data apps, analytics tools, or custom interactive visualizations') plus dashboard negative-routing, giving good keyword coverage, but misses common variations users would say such as 'deploy a Databricks app' or specific framework names. It exceeds 'some relevant keywords but missing variations' (3) yet is short of comprehensive synonym coverage (5).

Distinctiveness Conflict Risk

Actively disambiguates its nearest neighbor ('A plain "create a dashboard" request means a managed AI/BI (Lakeview) dashboard → use databricks-aibi-dashboards, not this skill') and names the platform-specific niche, giving a clear niche with distinct triggers and minimal conflict risk.