Conciseness

The body is mostly dense and actionable and assumes framework knowledge ("LLMs already know these frameworks — the challenge is Databricks platform integration"), but it could be tightened: the dashboard-vs-app disambiguation repeats the description at length, the ≤26-char app-name rule is stated three times (Generic Guidelines, scaffolding section, Directory Naming), and raw version requirements (CLI >= v0.294.0, >= v1.20.0) sit outside any compatibility/deprecated section.

Actionability

Fully executable, copy-paste-ready commands throughout: `databricks apps manifest --profile <PROFILE>`, `databricks apps init --name <NAME> --features <plugin1> --set <plugin1>.<resourceKey>.<field>=<value>`, `npx @databricks/appkit docs`, `databricks apps validate --profile <PROFILE>`, plus wrong/right scaffolding examples and a concrete routing table — matches the anchor for fully executable guidance covering common cases.

Workflow Clarity

"Development Workflow (FOLLOW THIS ORDER)" gives clear sequencing with validation checkpoints (verify typegen ✓, update smoke selectors BEFORE validation, `apps validate` before deploy, post-deploy `apps get` status check) and explicit decision gates, but there is no explicit fix-and-revalidate error-recovery loop, so it fits anchor 4 rather than 5.

Progressive Disclosure

The "Required Reading by Phase" table routes each task to a specific one-level-deep reference (e.g. [SQL Queries Guide](references/appkit/sql-queries.md), [Platform Guide](references/platform-guide.md)), all of which exist in the bundle, while SKILL.md stays an overview with routing and decision gates — matching the anchor for a clear overview with well-signaled one-level-deep references.

Specificity

"Build apps on Databricks Apps platform... Evaluates data access patterns (analytics vs Lakebase synced tables) before scaffolding" names the domain plus 1-2 concrete actions, but does not list several specific actions (scaffolding with `apps init`, writing type-safe SQL queries, deploying, validating), fitting anchor 3 rather than 4.

Completeness

Explicitly answers both what ("Build apps on Databricks Apps platform") and when ("Use when asked to create data apps, analytics tools, or custom interactive visualizations"), with concrete trigger phrases plus a negative trigger ("A plain 'create a dashboard' request means a managed AI/BI (Lakeview) dashboard → use databricks-aibi-dashboards") and ordering guidance ("Invoke BEFORE starting implementation") — a clear anchor-5 match, not merely adequate like anchor 4.

Trigger Term Quality

Natural trigger phrases are present — "create data apps", "analytics tools", "custom interactive visualizations", "create a dashboard" — giving good coverage, though common variations users would say ("Databricks app", "Streamlit/FastAPI app on Databricks", "chat app") are missing, placing it noticeably above the midpoint between anchors 3 and 4.

Distinctiveness Conflict Risk

The platform-specific niche and explicit dashboard disambiguation give mostly distinct triggers with minimal conflict risk, but closely related sibling skills (databricks-app-design, databricks-lakebase) overlap without disambiguation in the description itself, so anchor 4 fits better than 5.