Build something that is not there yet
Build overall equipment effectiveness and a downtime Pareto
- When OEE comes from a spreadsheet a shift supervisor fills in by hand.
- When the downtime Pareto changes shape depending on who built it that week.
Compute availability, performance, and quality per line and shift from machine or MES logs, roll them into OEE, and build a downtime Pareto by reason. Covers the OEE calculation and downtime attribution, not maintenance scheduling.
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 `manufacturing-oee-downtime` at https://getvibedata.ai/cookbook/manufacturing-oee-downtime Read the Recipe and execute it in the context of the current Intent.
Recipe id manufacturing-oee-downtime · 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.
- availability, performance, and quality each fall between zero and one for every line-shift
- every production hour maps to exactly one shift using the shift calendar
- a reproduced shift's OEE matches the plant's previously reported figure
- downtime reasons are attributed per the agreed taxonomy, not left uncategorized
Recipe promptThe task specification the agent reads. Reference only — it is not what you copy.
Deliver: Build overall equipment effectiveness and a downtime Pareto. Compute availability, performance, and quality per line and shift from machine or MES logs, roll them into OEE, and build a downtime Pareto by reason. Covers the OEE calculation and downtime attribution, not maintenance scheduling. 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: - availability, performance, and quality each fall between zero and one for every line-shift - every production hour maps to exactly one shift using the shift calendar - a reproduced shift's OEE matches the plant's previously reported figure - downtime reasons are attributed per the agreed taxonomy, not left uncategorized 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 downtime-reason taxonomy and ideal cycle times per product.
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.