Build something that is not there yet
Define governed metrics as a MetricFlow semantic model over a gold mart
- When revenue is defined six different ways across six dashboards and nobody can say which one is right.
- When a metric keeps getting redefined per report instead of being declared once over the mart.
Define entities, dimensions, measures, and metrics once as a MetricFlow semantic model over an agreed gold mart, with example queries that reproduce a known figure. Covers the semantic model as code, not publishing it into any BI tool.
Sample This Recipe has not been materialized in the Cookbook repository yet. Its trigger, description, prompt, agent guidance and acceptance conditions, and the explanation below, are prototype drafts. Its name, job, area and readiness come from the reconciled Cookbook seed snapshot. Readiness is a separate question from this one: it says whether the capability exists, not whether the writing has been reviewed.
Review planned Recipe
At least one capability or object type this Recipe needs is not supported yet.
Read the VibeData Recipe `metricflow-semantic-model` at https://getvibedata.ai/cookbook/metricflow-semantic-model At least one capability or object type this Recipe needs is not supported yet. Treat it as a reference, not as work to execute.
Recipe id metricflow-semantic-model · Not yet materialized in the Cookbook repository, so the pointer addresses this page.
Verified by
What has to be observably true before this Recipe is finished.
- the example queries reproduce the agreed reference figure for the agreed period
- every metric resolves to exactly one definition in the semantic model
- entities and dimensions match the declared keys on the gold mart
- the semantic model builds without publishing to a BI workspace
Recipe promptThe task specification the agent reads. Reference only — it is not what you copy.
Deliver: Define governed metrics as a MetricFlow semantic model over a gold mart. Define entities, dimensions, measures, and metrics once as a MetricFlow semantic model over an agreed gold mart, with example queries that reproduce a known figure. Covers the semantic model as code, not publishing it into any BI tool. Execute inside the current Intent. Its Domain, repository, platform, environment and attached sources are the context for this work — read them rather than asking for them. The work is done when: - the example queries reproduce the agreed reference figure for the agreed period - every metric resolves to exactly one definition in the semantic model - entities and dimensions match the declared keys on the gold mart - the semantic model builds without publishing to a BI workspace Report the evidence for each condition above with the result. A condition you cannot meet is something to say, not something to work around.
Agent guidanceHow the agent approaches the work, and what it will not do.
Profile the inputs the grain, joins and measures actually depend on before proposing a model. Put the design up for review — grain first — then build in an isolated copy with tests and documentation landing beside the model rather than after it.
Composes
- metric definition over governed tables
- MetricFlow in the project
- isolated-copy execution and gate verification
Asks first
Semantic decisions the Intent cannot supply. Never context Studio already holds.
- which Fabric target this work lands on, when the Domain carries both a Lakehouse and a Warehouse
Guardrails
- Build in an isolated copy. Production is read, never written.
- At least one capability this Recipe needs is not supported yet. Say so and stop rather than substituting a different approach.
What you need
- Microsoft Fabric Lakehouse, Microsoft Fabric Warehouse, MotherDuck, or DuckDB.
- Governed tables to define metrics over, and agreement on what each metric means.
- MetricFlow in the project, or the intent to add it.
- Code only; does not publish to a BI workspace.
How it goes
- State the outcome in one sentence, in the language the request arrived in.
- Let it profile the inputs the grain, the joins and the measures actually depend on.
- Review the design. Disagreeing about grain here costs a sentence; after the model exists it costs a rewrite.
- Let it build in an isolated copy, with the tests and the documentation landing beside the model rather than after it.
- Read the acceptance conditions against the run.
What you end up with
The deliverable, in your own repository, as semantic layer work a reviewer who knows the project reads as native to it. Alongside it, the evidence for every one of the acceptance conditions above — which is the part that is still there in three weeks when somebody asks.