ktx/packages/context/skills/dbt_ingest/SKILL.md

name	description	callers
dbt_ingest	Map dbt `schema.yml` / `properties.yml` models and sources into KTX semantic-layer overlays and column notes. Covers `sources:` vs `models:`, column `data_tests` (not_null, unique, accepted_values, relationships), and how bundle-time writes complement manifest backfill from git sync. Load when the WorkUnit's `skillNames` includes `dbt_ingest` or when raw files are dbt YAML under `models/` / `sources/`.	memory_agent

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)

dbt	KTX	Notes
models: entry with columns:	Overlay on the manifest table with the same name (after discover_data / entity_details)	One SL source per physical table; model name may differ from DB name — resolve with read_raw_file + warehouse context.
sources: → tables:	Same as models; use identifier when present instead of logical name.	Schema + name must match how the connection sees tables.
Column description	descriptions.user or merged descriptions map on the column	Do not overwrite dbt description keys from sync.
data_tests: not_null / unique	Short hint in column descriptions or notes: “dbt: not null”, “dbt: unique”	Full structured metadata lands in manifest via sync; the skill keeps bundle-time SL text useful for the agent.
accepted_values	Add a brief line in the column description: allowed values (truncate long lists)	Also mention enum-like use in discover_data / filters.
relationships	Add or confirm joins: on the overlay only when to resolves to a real table via read_raw_file + discover_data / entity_details	If the ref cannot be resolved, capture the intent in a wiki page instead.

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 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({connectionName, 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 name: sql_execution({connectionName, sql: "SELECT DISTINCT <col> FROM <ref> LIMIT 50"}).
4. If the candidate identifier still does not resolve, do one of:
   • Use sql_execution({connectionName, 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 columns[].descriptions (e.g. 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 columns:, 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.