Build something that is not there yet
Compute quota attainment and commission-eligible bookings from explicit plan rules
- When comp plans are calculated in spreadsheets and a payout dispute surfaces after the fact.
- When a rep's territory changed mid-period and attainment can't be recomputed without redoing the spreadsheet by hand.
Build attainment by rep and period against quota, using territory as of the agreed reference point, and a commission-eligible bookings feed with plan rules encoded as tested logic. Covers attainment and eligibility, not the payout calculation or disbursement itself.
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 `revops-quota-commission` at https://getvibedata.ai/cookbook/revops-quota-commission Read the Recipe and execute it in the context of the current Intent.
Recipe id revops-quota-commission · 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.
- a past period's attainment reproduces the figure already paid out for that period
- each plan rule (eligibility, splits, clawbacks, accelerators) has its own passing unit test
- a rep's mid-period territory change is applied using the agreed as-of point, not the current territory
- commission-eligible bookings exclude everything the plan rules exclude, with no silent inclusion
Recipe promptThe task specification the agent reads. Reference only — it is not what you copy.
Deliver: Compute quota attainment and commission-eligible bookings from explicit plan rules. Build attainment by rep and period against quota, using territory as of the agreed reference point, and a commission-eligible bookings feed with plan rules encoded as tested logic. Covers attainment and eligibility, not the payout calculation or disbursement itself. 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: - a past period's attainment reproduces the figure already paid out for that period - each plan rule (eligibility, splits, clawbacks, accelerators) has its own passing unit test - a rep's mid-period territory change is applied using the agreed as-of point, not the current territory - commission-eligible bookings exclude everything the plan rules exclude, with no silent inclusion 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
- pytest 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
- the grain the requester expects, where the request leaves it open to more than one reading
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 and pytest in the project, or the intent to add it.
- Policy-gated: every plan rule must be supplied, never inferred.
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.