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7.1 · Run the golden queries

The starter’s golden values are already pinned and verified against the synthetic warehouse (see validation/golden-queries.md, with the exact dim_*/fact_* mapping in validation/schema-mapping.md). Against your warehouse, re-pin them to numbers you trust:
Prompt
You’ll see: accuracy checked, not asserted — and a triage of any mismatch into data vs. definition vs. window. One invariant to expect: AUM ≠ Σ positions (governed AUM comes from account_value; positions exclude cash / held-away). A gap there is a data-quality signal, not a metric bug — notes/grain.md.

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7.2 · Customize a definition

Your firm inevitably defines something differently — an AUM basis, a TWR vs MWR choice, a house concentration threshold. Use the starter’s own scar as the worked example: the concentration threshold once flagged every holding because a bare DECIMAL cast is DECIMAL(10,0) on Spark, which rounded the 0.10 threshold to 0. The fix — CAST(${threshold} AS DECIMAL(18,6)) — is pinned in the golden file so the regression can never come back silently.
The field lesson — why golden values exist — Before the cast fix, concentration_risk returned the full 303,448 holdings (every holding “breached”). After fixing the precision to DECIMAL(18,6), it returns 201,120 — the real count over 10%. That delta is exactly why every governed surface gets a pinned golden value: a definition can be structurally right and numerically wrong, and only a pinned number catches it (notes/concentration-definition.md + the concentration_risk.tql header + golden-queries.md).
Prompt
You’ll see: the change land as a reviewable PR in your repo, the rejected default recorded, and a new pinned golden value — the template stays pristine upstream; your adaptations are yours. (Other good first customizations: TWR vs MWR as your client-report default, or point-in-time vs average-12m AUM for your KPI.)

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7.3 · Localize the vocabulary

ontology/notes/glossary.md holds the canonical wealth terms — client, household/relationship, account/portfolio, AUM, net flows, return, active return, asset class, concentration, effective fee rate, advisor/PM, security identifier — each with a sub-vertical variance column flagging where retail wealth, institutional asset management, and brokerage deployments diverge (households vs. one-entity clients, advisor vs. PM attribution, AUM vs. AUA/AUS…).
Prompt
You’ll see: the vocabulary localized in one reviewable pass — so “client,” “AUM,” and “return” mean your firm’s thing, everywhere, from now on.
Two habits as you make it yours — 1 · Write for the search box. As you extend the kit, keep a short README per folder and repeat the phrases your teams actually use (metric names, synonyms, team names) in the prose — future threads find context by search, not browsing.

2 · Let usage drive the roadmap. Stand up a weekly gap-review playbook: mine repeated questions, manual SQL, and mid-thread corrections; have Ana draft small reviewable patches; a named owner approves. The kit is the seed — usage is what grows it. (See Ontology Operations Module 4.)

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✅ Checkpoint

  • Golden queries ran; any drift was triaged (data / definition / window)
  • One definition is now yours — PR’d, noted, and pinned with a test