The body is dense reference material — an overview table, tool signatures, and an executable example — that assumes Claude's competence without explaining concepts like RAG or SQL; the minor "Best Practices" list is short and task-tied, so overall every section earns its place, matching the lean/efficient anchor.

Concrete tool signatures with parameters, actions, and return values plus a full copy-paste-ready `manage_mas(...)` Python example with realistic arguments meet the fully-executable anchor; not score 2 because the guidance is complete rather than pseudocode.

The "Typical Workflow" lists a clear sequence (generate data → create brick → wait for provisioning → add examples) with status states, but there are no explicit validation checkpoints or fix/retry feedback loops despite destructive `delete` and batch `create_or_update` actions, capping it at 2 per the destructive-operations guideline.

"See Also" signals `1-knowledge-assistants.md` and `2-supervisor-agents.md`, but no bundle directories or referenced files exist in the skill, and the full inline tool reference (manage_ka/manage_mas parameter lists) is content that could live one level deeper — fitting the anchor of references present but content that should be separate kept inline.

"Create and manage Databricks Agent Bricks: Knowledge Assistants (KA) for document Q&A, Genie Spaces for SQL exploration, and Supervisor Agents (MAS) for multi-agent orchestration" lists multiple concrete actions (create, manage) across three specifically-named brick types with their purposes, matching the score-3 anchor of listing several specific concrete actions.

It explicitly answers what ("Create and manage... Knowledge Assistants... Genie Spaces... Supervisor Agents") and when ("Use when building conversational AI applications on Databricks"), satisfying the anchor for clearly answering both what AND when with explicit triggers.

Natural terms a Databricks user would say are well covered — "Agent Bricks", "Knowledge Assistants", "Genie Spaces", "Supervisor Agents", "conversational AI applications", "Databricks" — rather than jargon, fitting the good-coverage anchor; not score 2 because common variations of the brick names are present.

The "Databricks Agent Bricks" framing plus the "building conversational AI applications on Databricks" trigger carves a clear niche unlikely to fire for unrelated skills; not score 2 because the brick-management scope is distinct even though Genie overlaps a sibling skill.