Build something that is not there yet
Build fleet utilization by vehicle and day from explicit utilization rules
- When operations can't say which vehicles are underused without a manual pull against the trip log.
- When utilization gets computed on hours one month and capacity the next, with no record of which.
Build utilization per vehicle and day from trip and vehicle source data, on an explicit, documented utilization basis. Covers vehicle-day utilization only; lane-level on-time performance is a separate Recipe with its own grain.
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.
Use this Recipe
Use the VibeData Recipe `logistics-fleet-utilization` at https://getvibedata.ai/cookbook/logistics-fleet-utilization Read the Recipe and execute it in the context of the current Intent.
Recipe id logistics-fleet-utilization · 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.
- exactly one row exists per vehicle per day in the utilization output
- the utilization basis of hours, distance, or capacity is documented on the model and only one basis is in use
- a reproduced week's utilization matches operations' previously reported figure for that vehicle
- a vehicle with no trips on a day is recorded as zero utilization, not omitted
Recipe promptThe task specification the agent reads. Reference only — it is not what you copy.
Deliver: Build fleet utilization by vehicle and day from explicit utilization rules. Build utilization per vehicle and day from trip and vehicle source data, on an explicit, documented utilization basis. Covers vehicle-day utilization only; lane-level on-time performance is a separate Recipe with its own grain. 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: - exactly one row exists per vehicle per day in the utilization output - the utilization basis of hours, distance, or capacity is documented on the model and only one basis is in use - a reproduced week's utilization matches operations' previously reported figure for that vehicle - a vehicle with no trips on a day is recorded as zero utilization, not omitted 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
- dbt model authoring and layering
- dbt 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.
What you need
- Microsoft Fabric Lakehouse, Microsoft Fabric Warehouse, MotherDuck, or DuckDB.
- A dbt project you can build, and read access to the models it starts from.
- dbt in the project, or the intent to add it.
- Needs an agreed utilization basis.
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 transformation 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.