Conciseness

The body is dense with directives and avoids explaining known concepts ("LLMs already know these frameworks — the challenge is Databricks platform integration"), but the long AI/BI-vs-app disambiguation paragraph ("Don't assume an app is the only way to show data...") partially repeats the description, and the single-sentence data-UI-design paragraph is padded and hard to parse.

Actionability

Nearly every section gives copy-paste-ready commands: `databricks apps manifest`, full `databricks apps init --name ... --features ... --set ...` invocations, `npx @databricks/appkit docs` usage, `databricks apps validate --profile <PROFILE>`, plus a wrong-vs-correct scaffolding example — placeholders like `<pm>`/`<PROFILE>` are explicitly flagged and resolvable via stated rules.

Workflow Clarity

"Development Workflow (FOLLOW THIS ORDER)" gives a required pre-scaffolding Decision Gate, numbered analytics steps with typegen-before-UI ordering, smoke-test selector updates, validate-before-deploy, and post-deploy verification — but no explicit fix-and-retry loop when validation or typegen fails (recovery detail is delegated to references), so it falls just short of the anchor with feedback loops.

Progressive Disclosure

A "Required Reading by Phase" table routes each task to a one-level-deep reference; all 16 referenced files (references/appkit/*.md, platform-guide.md, testing.md, other-frameworks.md) exist in the bundle, and deep-link anchors like overview.md#scaffolding-rules-protocol and genie.md#agent-workflow-tables-first verify — the body holds only overview, routing, and gotchas.

Specificity

Concrete actions are stated — "Build apps on Databricks Apps platform", deliverables enumerated as "data apps, analytics tools, or custom interactive visualizations", and "Evaluates data access patterns (analytics vs Lakebase synced tables) before scaffolding" — several specific actions with minor coverage gaps (Genie, model serving, and other capabilities from the body are absent).

Completeness

Both halves are explicit: what ("Build apps on Databricks Apps platform") and when ("Use when asked to create data apps, analytics tools, or custom interactive visualizations"), reinforced with "Invoke BEFORE starting implementation" — a clear what-and-when pair with concrete trigger phrases.

Trigger Term Quality

Good natural-phrase coverage: "data apps", "analytics tools", "custom interactive visualizations", and the literal user request "create a dashboard". A few natural terms users would say are missing, e.g. "web app", "Streamlit", or "deploy an app", so it falls short of comprehensive synonym coverage.

Distinctiveness Conflict Risk

It carves a clear niche (custom apps on the Databricks Apps platform) and actively defuses the nearest conflict: "A plain 'create a dashboard' request means a managed AI/BI (Lakeview) dashboard → use databricks-aibi-dashboards, not this skill" — minimal risk of triggering the wrong skill.