entity-creation

Solid

Guides you through defining a new Honeydew entity from a data warehouse source — covering source type, granularity key, and initial attribute mapping — then pushes to Honeydew via the MCP tools.

Data & Documents 39 stars 1 forks Updated 2 days ago Apache-2.0

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Skill Content

## Prerequisites Before creating entities, ensure you are on the correct workspace and branch. Use `get_session_workspace_and_branch` to check the current session context. For development work, create a branch with `create_workspace_branch` (the session switches automatically). See the `workspace-branch` skill for the full workspace/branch tool reference. --- ## Overview A Honeydew **entity** is the foundational modeling object — a named, governed representation of a business concept at a specific granularity. Every metric and calculated attribute is anchored to an entity. An entity maps to a data warehouse (Snowflake, Databricks, or BigQuery) table, view, custom SQL query, or a virtual derivation from the semantic model. When creating an entity you are answering three questions: 1. **What is this?** — the business concept (orders, customers, products, locations) 2. **What makes a row unique?** — the granularity key (primary key column(s)) 3. **What columns should be exposed?** — the initial attribute mapping from source columns > This skill focuses on the entity shell: source, key, and attribute mapping. > Use `attribute-creation` to add calculated attributes and `relation-creation` to wire up joins afterwards. --- ## Creation Methods ### Quick Import: import_tables Use `import_tables` to quickly import one or more data warehouse tables into the semantic model. Each table becomes an entity with its columns automatically mapped as attributes. This is the fastest wa...

Details

Author
honeydew-ai
Repository
honeydew-ai/honeydew-ai-coding-agents-plugins
Created
5 months ago
Last Updated
2 days ago
Language
Shell
License
Apache-2.0

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