| name | malloy-scope |
| description | Present discovery findings and propose an analytical scope before modeling. Use after inspecting a package's model and data, to classify tables and recommend an analytical focus the user can pick from. |
Propose Analytical Scope
When: After you have inspected the model and its underlying data. You have read the package's sources and fields and looked at the data distributions.
Tool names are written bare here - get_context, execute_query, search_malloy_docs. The exact prefixed name depends on the host surface; match each against the tools you actually have.
Goal: Present what you found and recommend an analytical focus. The user selects a direction.
Ground yourself first with get_context: it returns the package's sources, views, and fields, so it tells you what data exists, how it relates, and what is already modeled. Query the data with execute_query to get row counts and spot data-quality issues. Keep your proposal and the user's decision in the conversation; there is no separate scope file to write.
What to Present
1. Table Summary
Present a table of the discovered tables with key metadata:
| Table | Rows | Columns | Role | Key Relationships |
|---|
| orders | 1.2M | 24 | Fact | FK: customer_id → customers, product_id → products |
| customers | 50K | 15 | Dimension | PK: customer_id |
| products | 2K | 12 | Dimension | PK: product_id |
| order_items | 3.5M | 8 | Bridge | FK: order_id → orders, product_id → products |
| audit_log | 10M | 6 | Operational | No joins to business tables |
Classify each table by role:
- Fact: the events or transactions you measure (orders, sessions, payments).
- Dimension: the entities you slice by (customers, products, regions).
- Bridge: many-to-many linking tables (order_items, tags).
- Operational: ETL, staging, or audit tables that aren't analytical.
2. Recommended Analytical Focus
Identify 2-3 analytical domains: categories of questions the data can answer.
- Order Analysis: Revenue, order trends, product performance, customer purchasing patterns. (Covers: orders, order_items, products, customers)
- Customer Health: Retention, segmentation, lifetime value, churn risk. (Covers: customers, orders)
- Inventory Management: Stock levels, reorder patterns, supplier performance. (Covers: products, inventory, suppliers)
Recommend one as the starting point with reasoning:
"I'd recommend starting with Order Analysis. It covers your most-used tables and the core business questions around revenue and performance. We can add Customer Health as a separate source later."
3. Tables to Skip
Flag tables that shouldn't be modeled (with reasoning):
- audit_log: Operational/ETL table, not analytical
- staging_orders: Staging table, use
orders instead
- monthly_summary: Pre-aggregated snapshot, compute fresh in Malloy instead
4. Scope Options
Present 2-3 concrete options as a numbered list the user can easily select from. Each option should be a single line with a label, tables included, and key questions it answers. Mark your recommendation.
Format choices for easy selection, so the user can reply "A", "B", or "C":
A. Order Analysis (recommended): orders + customers + products + order_items. Answers: What's selling? How is revenue trending? Who are the top customers?
B. Full Commerce: Everything in A plus inventory and suppliers. Broader but more complex.
C. Customer Focus: customers + orders only. Narrower, focused on retention and segmentation.
Pick one (or mix, e.g., "A plus suppliers").
User Interaction
Present choices in a format that's easy to type a short answer to. Avoid long prose that requires the user to read and synthesize. Numbered/lettered options with one-line descriptions.
The user will:
- Select one of the options (or combine them, e.g., "A plus inventory")
- Add tables you missed
- Remove tables they don't want
- Redirect to a different analytical focus entirely
After User Confirms
Restate the confirmed scope in the conversation so it's clear what you'll model next. A useful shape to summarize:
- Connection / schema: the connection name and schema the sources draw from.
- Tables in scope: table, row count, role (Fact/Dimension/Bridge), and any notes.
- Analytical focus: one line describing the analytical domain.
- Deferred: tables left out, with the reason.
Then hand off to modeling: use your modeling workflow to turn the confirmed scope into Malloy sources, dimensions, measures, and views.
Tips
- Don't overwhelm: if there are 20+ tables, group them by domain and focus on the most relevant cluster.
- Show evidence: mention row counts, column counts, relationship density to justify your recommendation.
- Be opinionated: recommend one option clearly, don't present them as equal.
- Flag data quality issues discovered while inspecting the data (e.g., "The
orders table has ~3% duplicate rows on order_id").
Done
Scope confirmed in the conversation: tables in scope, analytical focus, and what's deferred. Continue with your modeling workflow.
