Scan assumptions and model logic for soundness
Packaged know-how that tells an agent how to do a job well.
You might say…
“The numbers add up but I'm not sure the model is actually doing what they think it's doing — I need a read on the logic and the assumptions before I sign off on it.”
What it does
Reads quantitative work and judges whether the model logic is coherent, segmentation is right, and implicit assumptions are reasonable and surfaced — flagging data gaps and silent assumptions.
Trigger: Use when reviewing data-heavy analysis to catch judgement errors that numerical reconciliation alone would miss.
I/O: Quantitative model or analysis + data inputs → Flagged model logic issues, segmentation concerns, implicit assumptions, and data gaps with suggested surfacing or fixes
Recognise the problem?
The primitives are the commodity part. The fastest next step is a conversation about composing them into something that works for you.
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