What problem does it solve? Blobby, the Omni Agent, gives wrong or inconsistent answers when the semantic model lacks proper AI context, field curation, and terminology mappings. This Skill guides you through optimizing your Omni model so the AI correctly interprets business language and queries the right fields. ## Core Features & Use Cases - AI Context Authoring: Write concise ai_context at model, topic, view, and field levels, including templating with user attributes, omni_llm tiers, omni_agent scoping, and reusable constants. - Field and Topic Curation: Control what the AI sees with ai_fields selectors and ai_chat_topics, while respecting the ~75K character context cap and pruning order. - Synonyms and Sample Queries: Map business terms to fields and teach Blobby by example with structured sample_queries for recurring questions. - Use Case: When users report that Blobby confuses "revenue" with "order count", use this Skill to add a topic-level ai_context mapping on a model branch, verify existing configuration first, and avoid redundant synonym writes. ## Quick Start Ask the AI to optimize the order_items topic in Omni so Blobby correctly maps revenue and order count terms using ai_context on a new model branch.
