test: split cli tests from source tree (#216)

* feat(cli): define full warehouse dialect contract

* test(cli): keep dialect edge tests focused

* fix(cli): stabilize dialect contract foundation

* refactor(connectors): own read-only query preparation

* refactor(connectors): resolve dialects through registry

* refactor(connectors): keep concrete dialect classes internal

* chore(workspace): enforce dialect import boundary

* refactor(cli): resolve relationship dialect at scan boundary

* refactor(cli): use dialect display parsing for entity details

* refactor(cli): use dialect display parsing for warehouse catalog

* refactor(cli): use dialect SQL in relationship workflows

* test(cli): verify solid dialect scan workflow closure

* test: split cli tests from source tree

* refactor(cli): standardize BigQuery scope listing

* feat(sqlite): implement connector scope listing

* test(connectors): cover required table listing

* feat(cli): add warehouse driver registry

* refactor(setup): route scope discovery through driver registry

* refactor(cli): route local query execution through driver registry

* refactor(historic-sql): route dialect support through driver registry

* refactor(cli): test warehouse connections through driver registry

* fix(cli): close driver registry type export gaps

* Improve setup daemon diagnostics

* refactor(setup): centralize rail-prefixed diagnostics + query-history fallback

Extract errorMessage, writePrefixedLines, and flushPrefixedBufferedCommandOutput
into clack.ts so the setup wizard, managed daemons, and embedding/agent steps
share one rail-formatted writer. setup-databases.ts also adds a
"disable query history and retry" option when the schema-context build fails
and query history is the likely culprit, surfaced via a new
failed-query-history-unavailable status.

* fix(cli): carry catalog through the picker so BigQuery/Snowflake/SQL Server scope filters match

The setup picker's KtxTableListEntry was a 2-level { schema, name }, so
qualifiedTableId always wrote db.name into enabled_tables. When BigQuery,
Snowflake, or SQL Server later ran fast ingest, their introspect step filtered
the scope set with scopedTableNames(scope, { catalog: projectId|database, db })
— catalog was non-null on the introspect side but null in the scope refs, so
every entry was rejected, the live-database adapter staged zero table files,
and detect() failed with 'Adapter "live-database" did not recognize fetched
source output'.

Align the picker boundary with the canonical 3-level KtxTableRef:

- Add catalog: string | null to KtxTableListEntry.
- BigQuery/Snowflake/SQL Server listTables populate catalog from the
  resolved projectId / database; Postgres/MySQL/ClickHouse/SQLite set null.
- qualifiedTableId emits catalog.schema.name when catalog is non-null
  (resolveEnabledTables already accepts the 3-part shape) and
  schemasFromEnabledTables now goes through parseDottedTableEntry so it
  recovers the schema correctly from both 2-part and 3-part entries.
- Export parseDottedTableEntry from enabled-tables.ts (@internal) for picker
  reuse.

Update listTables expectations in all seven connector tests and the setup /
picker test fixtures. Add a picker regression test that covers the
catalog-bearing round-trip (save + refine).

* fix(cli): allow debug telemetry under opt-out env

packages/cli/src/connectors/bigquery/dialect.test.ts

@@ -1,52 +0,0 @@
-import { describe, expect, it } from 'vitest';
-import { KtxBigQueryDialect } from './dialect.js';
-
-describe('KtxBigQueryDialect', () => {
-  const dialect = new KtxBigQueryDialect();
-
-  it('quotes identifiers and formats project.dataset.table names', () => {
-    expect(dialect.quoteIdentifier('order`items')).toBe('`order\\`items`');
-    expect(dialect.formatTableName({ catalog: 'project-1', db: 'analytics', name: 'orders' })).toBe(
-      '`project-1`.`analytics`.`orders`',
-    );
-    expect(dialect.formatTableName({ db: 'analytics', name: 'orders' })).toBe('`analytics`.`orders`');
-    expect(dialect.formatTableName({ name: 'orders' })).toBe('`orders`');
-  });
-
-  it('maps native BigQuery types to normalized types and scan dimensions', () => {
-    expect(dialect.mapDataType('INT64')).toBe('BIGINT');
-    expect(dialect.mapDataType('STRUCT')).toBe('JSON');
-    expect(dialect.mapDataType('GEOGRAPHY')).toBe('GEOGRAPHY');
-    expect(dialect.mapToDimensionType('TIMESTAMP')).toBe('time');
-    expect(dialect.mapToDimensionType('NUMERIC')).toBe('number');
-    expect(dialect.mapToDimensionType('BOOL')).toBe('boolean');
-    expect(dialect.mapToDimensionType('JSON')).toBe('string');
-  });
-
-  it('generates sampling, cardinality, and distinct-value SQL', () => {
-    expect(dialect.generateSampleQuery('`p`.`d`.`orders`', 5, ['id', 'status'])).toBe(
-      'SELECT `id`, `status` FROM `p`.`d`.`orders` ORDER BY RAND() LIMIT 5',
-    );
-    expect(dialect.generateColumnSampleQuery('`p`.`d`.`orders`', 'status', 10)).toBe(
-      "SELECT `status` FROM `p`.`d`.`orders` WHERE `status` IS NOT NULL AND TRIM(CAST(`status` AS STRING)) != '' ORDER BY RAND() LIMIT 10",
-    );
-    expect(dialect.generateCardinalitySampleQuery('`p`.`d`.`orders`', '`status`', 100)).toContain(
-      'SELECT APPROX_COUNT_DISTINCT(val) AS cardinality',
-    );
-    expect(dialect.generateDistinctValuesQuery('`p`.`d`.`orders`', '`status`', 20)).toContain(
-      'SELECT DISTINCT CAST(`status` AS STRING) AS val',
-    );
-  });
-
-  it('rewrites colon parameters to BigQuery named parameters', () => {
-    expect(dialect.prepareQuery('SELECT * FROM orders WHERE id = :id AND id_2 = :id_2', { id: 1, id_2: 2 })).toEqual({
-      sql: 'SELECT * FROM orders WHERE id = @id AND id_2 = @id_2',
-      params: { id: 1, id_2: 2 },
-    });
-    expect(dialect.prepareQuery('SELECT * FROM orders')).toEqual({ sql: 'SELECT * FROM orders', params: undefined });
-  });
-
-  it('keeps unsupported statistics explicit', () => {
-    expect(dialect.generateColumnStatisticsQuery('analytics', 'orders')).toBeNull();
-  });
-});