malloy-scope

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.