What problem does it solve?
Data agents often write incorrect SQL because they lack knowledge of approved metric definitions, joinable columns, and business logic. This Skill provides a structured semantic layer catalog of sources, measures, joins, and segments so agents can query metrics like ARR, churn, retention, and LTV accurately without hand-writing fragile SQL.
Core Features & Use Cases
- Schema Reference: Defines three source flavors in YAML - overlays that enrich manifest-backed tables, standalone table sources, and standalone SQL sources - with columns, grain, joins, measures, and segments.
- Structured Querying via sl_query: Generates correct SQL from a structured query, automatically handling join path resolution, fanout prevention, aggregation correctness, and WHERE/HAVING filter classification.
- Use Case: Ask for monthly ARR filtered to paid, non-refunded subscriptions. The Skill resolves the measure
subscriptions.arr, applies the paid_non_refunded segment, groups by month, and produces correct SQL without manual join or aggregation logic.
Quick Start
Use the sl skill to query monthly ARR from the subscriptions source filtered to paid non-refunded orders.