Dbt Ingest

dbt → KTX (bundle ingest)

Use this skill for uploaded dbt projects (dbt_project.yml at stage root, models/**, sources/**, schema.yml). There is no fetch() in v1 - scheduled dbt parse / manifest.json pulls are out of scope; host-provided dbt sync may still backfill structured test metadata into _schema on the next sync.

Mapping (models / sources → SL)

Physical schema grounding

dbt YAML is documentation and test metadata; it is not permission to invent physical columns. Before writing any table-backed SL source, confirm the real warehouse shape with discover_data, sl_discover, or entity_details and use only confirmed column names in column_overrides:, computed-only columns:, grain:, joins:, segments:, and measures[].expr.

For dbt context-source ingest, the dbt connection is usually not the warehouse connection. Call sl_discover without connectionId first, then write overlays to the connection that owns the matching manifest-backed source (for example postgres-warehouse), not to the dbt connection (for example dbt-main). If no matching manifest-backed source is visible on any warehouse connection, do not call sl_write_source; record emit_unmapped_fallback and keep the fact wiki-only.

If a models: entry has no columns: block, or the available raw files do not confirm the physical column names, do not synthesize a full standalone source. Write a wiki note or a description-only overlay for the resolved manifest table instead. If a business metric is described but its referenced column is not confirmed in the warehouse schema, omit the measure and capture the unresolved intent in the wiki.

Include rawPaths on every wiki_write, sl_write_source, and sl_edit_source call with only the dbt YAML files that directly support the action.

After every sl_write_source, call sl_validate. A validation error saying a declared column or measure reference is absent from the physical table is a hard stop: re-read the warehouse-backed source and rewrite with confirmed names, or remove the invalid SL fields.

Identifier Verification Protocol

Before writing a wiki page or SL source on any topic:

1. discover_data({query: "<topic>"}) - see what wikis, SL sources, and raw tables already exist. Prefer updating existing pages over creating new ones.

Before emitting any schema.table or schema.table.column into a wiki body, SL source, tables: frontmatter, sl_refs, or emit_unmapped_fallback:

2. entity_details({connectionId, targets: [{display: "<identifier>"}]}) - confirm the identifier resolves; inspect native types, FK/PK, and sampleValues.
3. For literal values from the source, such as status codes or plan tiers, check whether they appear in entity_details sampleValues for the relevant column. If sampleValues is short or the sample may have missed real values, run a sql_execution probe with the same warehouse connection id: sql_execution({connectionId, sql: "SELECT DISTINCT <col> FROM <ref> LIMIT 50"}).
4. If the candidate identifier still does not resolve, do one of:
   • Use sql_execution({connectionId, sql: "SELECT 1 FROM <ref> LIMIT 0"}). If it errors, the identifier is fictional.
   • Wrap the identifier in [unverified - from <rawPath>] in the wiki body, citing the exact raw path that mentioned it.
   • When recording emit_unmapped_fallback with no_physical_table, include the failing probe error in clarification.
5. Never copy <schema>.<table> placeholder strings from these instructions into output.

1.1 test hints (descriptions / meta)

When YAML shows accepted_values or not_null, add short hints into column_overrides[].descriptions (for example under user) or freeform column notes so chat and validation see intent before the next git sync refreshes constraints / enum_values in _schema. Keep hints under a few words when possible.

Overlap with MetricFlow

If the same bundle also has MetricFlow semantic_models: / metrics:, the metricflow_ingest skill owns semantic/metric shapes. This skill focuses on raw dbt schema YAML (models, sources, tests). If both apply, load metricflow_ingest first when the file is clearly MetricFlow; otherwise use dbt_ingest for schema.yml without semantic_models.

Do not

• Do not run dbt CLI or assume target/ / manifest.json exists in the upload.
• Do not invent column names, grain keys, or measure expressions from dbt model names, descriptions, tests, or common naming patterns.
• Do not write computed columns:, column_overrides:, grain:, or measures: for a dbt model unless those exact column names are confirmed by dbt YAML columns or warehouse schema discovery.
• Do not invent joins from relationships tests if the target model/table is not found in SL or the warehouse.
• Do not read peerFileIndex paths - use read_raw_file only on rawFiles and dependencyPaths from the WorkUnit.