What problem does it solve? Malloy models often lack human-readable descriptions, making fields and sources hard to discover through natural-language search. This Skill guides the systematic addition of #(doc) tags so analysts can find and understand data using plain-English questions. ## Core Features & Use Cases - Doc Tag Authoring: Add #(doc) annotations to sources, dimensions, measures, views, and joins with correct tag ordering and formatting. - Retrieval-Optimized Descriptions: Write doc strings that match how analysts search, including business meaning, units, and categorical values while avoiding Malloy jargon. - Access Modifiers: Use internal: and private: in include {} blocks to curate which columns appear in a source's public API. - Use Case: After building a Malloy orders model, run this Skill to document every public field so downstream tools and AI agents can answer questions like "what was our revenue?" against the model. ## Quick Start Add #(doc) documentation tags to all sources and fields in my Malloy model so they are searchable in plain language.
