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Get Started Free →hashgraph-online/entity-creation
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.
Passed 10 of 16 test cases with the skill, and 2 of 16 without it.
and 9 more in the full report
11 fixed3 passed either way9 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
Before drafting the YAML for a new entity in Honeydew based on `warehouse_db.sales.order_events`, we need to inspect the column names and data types of that physical warehouse table. Which Honeydew discovery tool should be called?
| Checked | Without skill | With skill |
|---|---|---|
| Uses get_table_info to inspect column details of the warehouse table. | Missed | Passed |
The instructions your model reads when the skill loads.
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 model-exploration skill for the full workspace/branch tool reference.
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:
This skill focuses on the entity shell: source, key, and attribute mapping. Use
attribute-creationto add calculated attributes andrelation-creationto wire up joins afterwards.
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 way to bootstrap entities when you don't need custom YAML.
Parameters:
tables — List of fully qualified table names in the format <database>.<schema>.<table>Best for: bulk imports, quick prototyping, or when the default column-to-attribute mapping is sufficient.
Unlike metrics and attributes, entity creation always requires YAML — there is no simplified single-expression API. create_entity is the preferred MCP tool because it creates both the entity and its dataset in a single operation.
Parameters:
test cases passed, vs 2 / 16 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.