reference/terminology/naic_lob.csv, peril.csv) — nothing to load into your warehouse. Ana loads the CSV into her Python sandbox and joins it to a read-only warehouse pull in memory (see ontology/notes/terminology-join-pattern.md), so the grouping logic works with zero warehouse writes. The shipped ratio surfaces filter line_of_business on the fact, so they run with no seed dependency at all — the seeds add the rollups.
4.1 · Prove the rollups work
Prompt
You’ll see: raw product and cause-of-loss codes resolved to meaningful NAIC lines and peril groups, with the federated join-in-sandbox pattern explained — and a reminder to analyze peril groups, never free-text cause-of-loss.
4.2 · Bring in your licensed feed — optional
You don’t need this on day one — the public NAIC and peril groupings are already committed. Come back when you want the granular ISO peril / cause-of-loss codes, PCS catastrophe codes, or ISO rating territories. These are licensed (Verisk/ISO) — the repo ships the structure and join logic, and the data comes from your carrier’s licensed feed:Prompt
You’ll see: the structural model light up with your licensed data — the modeling logic versioned in git, the licensed codes staying in your warehouse, same governance motion as everything else.
✅ Checkpoint
- A NAIC-line and peril-group rollup worked against your data with no warehouse writes
- You can explain the federated join-in-sandbox pattern in one sentence
- You know which classification ships public (NAIC/peril) vs. licensed (ISO/PCS, from your feed)