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

name	description	callers
metabase_ingest	Convert Metabase questions, models, and metrics into KTX Semantic Layer source definitions. Covers result-metadata to KSL column type mapping, FK/PK detection, near-duplicate deduplication, pre-aggregation decomposition, join-graph connectivity, and how to react to priorProvenance from earlier ingest syncs. Load when the WorkUnit contains `cards/<id>.json` files under a Metabase bundle.	memory_agent

Metabase to KTX Semantic Layer

Each WorkUnit represents one Metabase collection's cards for one Metabase database (mapped to exactly one KTX connection). Every cards/<id>.json file carries the resolved SQL, result_metadata, card type, collection path, and referenced-card ids. The WU's sync-config.json tells you which sync mode is active and which selections apply. databases/<id>.json tells you the target KTX connection.

Context format

Each card JSON looks like:

{
  "metabaseId": 7,
  "name": "Daily orders",
  "description": "Orders by day",
  "type": "model",
  "databaseId": 42,
  "collectionId": 5,
  "resolvedSql": "SELECT ...",
  "templateTags": [{"name": "ref", "type": "card", "cardReference": 10}],
  "resultMetadata": [
    {"name": "day", "base_type": "type/DateTime", "semantic_type": "type/CreationTimestamp"},
    {"name": "order_count", "base_type": "type/Integer"}
  ],
  "collectionPath": ["Data", "Orders Team"],
  "referencedCardIds": [10]
}

Use resultMetadata to:

• Map base_type to KSL column type: type/Integer, type/Float, type/Decimal, type/BigInteger → number; type/Text, type/TextLike → string; type/DateTime, type/Date, type/DateTimeWithTZ → time; type/Boolean → boolean.
• Identify grain candidates: columns with semantic_type: type/PK.
• Identify join candidates: columns with semantic_type: type/FK plus fk_target_field_id.
• Identify time columns: semantic_type: type/CreationTimestamp or type/UpdatedTimestamp → set role: time.
• Use display_name for measure descriptions when available.

Additional card metadata

• parameters: list of card-level parameters with widget types and defaults. When SQL resolution fell back to unresolved SQL, use this to drive Step A of the SQL-translation workflow (drop optional clauses): knowing each {{ var }} is type: "date/range" vs type: "category" tells you what kind of clause it is.
• resultMetadata[i].field_ref: Metabase's canonical reference to the source warehouse field. Shape ["field", <field_id>, <options>]. When this is set, the column maps directly to a warehouse field, which is useful for declaring joins from FK metadata without re-parsing SQL.
• lastRunAt: ISO timestamp of the card's last execution. If null or very old, the card may be dead; prefer skipping over creating a source.
• dashboardCount: number of dashboards referencing the card. Cards with dashboardCount: 0 and a stale lastRunAt are strong skip signals.

Decision tree

For each card:

1. Analyze resolvedSql + resultMetadata: identify base tables, aggregations, joins, filters, column types.
2. Check sl_discover and sl_read_source for existing sources that overlap.
3. Decide:
   • Simple aggregation on a table that already has a source → sl_edit_source to add a measure.
   • Join between tables that should be linked in the SL graph → sl_edit_source to add a join.
   • Complex derived SQL (CTEs, multi-layer aggregation, scoring models) → sl_write_source with source_type: sql. When the SQL projects/filters from a single manifest-backed base table, set inherits_columns_from: <manifest_key> so columns inherit type and description from the manifest — see sl_capture skill for the slim form. Use sl_discover to discover the manifest key from the table reference in the SQL (it accepts MARTS.CONSIGNMENTS, ANALYTICS.MARTS.CONSIGNMENTS, or CONSIGNMENTS).
   • New base table not yet in the semantic layer → sl_write_source with source_type: table.
   • Trivial query (SELECT *, simple COUNT(*) with no business logic) → do nothing; the runner will record this card as action_type='skipped'.
   • Duplicate of an existing measure → same as trivial; do nothing for this card.

Manifest-only names need an overlay first. If sl_discover shows a source name with Type: table but sl_read_source returns "Source not found", the source lives only in the schema manifest (no standalone overlay yet). sl_edit_source cannot edit manifest-only names — you must bootstrap an overlay with sl_write_source using the overlay shape:

name: <SOURCE_NAME>
measures:
  - name: <measure_name>
    expr: "<expression>"

Overlay shape: name: plus any of measures:, segments:, description:, joins:, disable_joins:. Never include sql:, table:, grain:, or columns: on a manifest-backed name — those would shadow the manifest's schema and drop its joins. Overlay joins: are merged additively with the manifest's joins (deduped by to + on); use disable_joins: ["<on-clause>"] to suppress a specific manifest join. After the overlay exists, use sl_edit_source for further tweaks. See sl_capture skill for the canonical overlay rule.

Join discovery: When your card's SQL references warehouse tables (e.g. in FROM or JOIN clauses), call sl_discover({ query: '<table>' }) before writing. The matching manifest entry's name is the value you put in joins: [- to: <name>]. Use many_to_one for FK-to-dimension joins, one_to_many for the reverse.

priorProvenance

If the WU prompt includes a priorProvenance section for a card, it tells you what happened on prior ingest syncs. Treat it as advisory:

• action_type: source_created on source X → prefer editing X with sl_edit_source rather than writing a new source.
• action_type: measure_added on source X → you already contributed to X; add only measures that aren't present.
• action_type: subsumed or merged → this card was folded into another source last time; unless its SQL has changed structurally, keep it subsumed (no new write).
• action_type: skipped → last time we decided not to ingest this card; re-read the SQL and confirm the decision still holds. If the card now has non-trivial business logic, ingest it.

Deduplication

Before writing, scan all cards in this WU for near-duplicate groups — cards whose resolvedSql shares the same CTEs, base tables, joins, and aggregation structure but differs only in:

• Trailing filters (e.g. date_trunc(week, date) vs date_trunc(month, date)).
• Minor WHERE clause variations.
• Column aliases or output column subsets.
• Aggregation granularity (daily vs weekly vs monthly).

When you find a group of near-duplicates:

1. Create ONE generalized source from the most comprehensive card in the group.
2. Strip card-specific trailing filters from the SQL so the source covers all variants (e.g. keep daily grain instead of filtering to week/month).
3. If each card had a distinct measure or filter, add them as separate measures on the single source.
4. For all cards except the canonical one, do nothing — they'll be recorded as action_type='skipped' automatically by the runner.

Do NOT merge cards with fundamentally different business logic, even if they share CTEs.

Pre-aggregation decomposition

When a card's resolvedSql contains GROUP BY with aggregation functions (SUM, COUNT, AVG, …):

1. Detect: simple aggregation on base tables/joins — SELECT with GROUP BY, no complex CTEs or window functions.
2. Decompose: strip the GROUP BY and aggregation functions. Keep FROM, JOIN, and WHERE intact.
3. Expose row-level columns: include the grouped-by columns AND the raw columns being aggregated (e.g. money_out instead of SUM(money_out) AS total_money_out).
4. Define aggregations as measures: convert each aggregation into a KSL measure (e.g. sum(money_out)).
5. Add joins: with FK columns now exposed, declare joins to dimension sources.

Exception: keep the pre-aggregated SQL when the query involves multi-CTE pipelines, window functions, or recursive logic where decomposition would lose business logic.

SQL translation from raw native to KSL

Every card carries a resolvedSql field. Check the staged card's resolutionStatus first:

• resolutionStatus: "resolved" — {{#N}} references are inlined and [[ ... ]] optional clauses have been dropped locally. If the resolved SQL contains no other parameters the SQL is executable as-is. If the card had required (non-bracketed) {{ var }} placeholders, the SQL is prefixed with a placeholder-warning comment block listing every dummy substitution Metabase made — see "Step A" below.
• resolutionStatus: "fallback" — Metabase failed to resolve. The SQL still contains {{#N}}, {{#N-name}} alias, {{ var }}, and [[ ... ]] syntax. Do the translation steps below before writing a source.

Step A — Handle dummy-substituted placeholders (resolved cards only)

When a card has a required {{ var }} outside any [[ ]] block, the resolver substitutes a dummy value purely so Metabase's parser will accept the query. The resulting SQL is prefixed with a comment like:

-- PLACEHOLDER_WARNING: this SQL was extracted from a Metabase card with
-- unbound template parameters. The placeholders below were substituted with DUMMY
-- values to satisfy Metabase's parser — they DO NOT represent intended filters.
-- Drop the corresponding clauses (or expose them as runtime SL filters) before
-- persisting this SQL as a semantic-layer source.
--   {{ auction_end }} (type=dimension, widget=date/all-options) → '2020-01-01~2020-12-31'
--   {{ status }} (type=text) → 'placeholder'
SELECT ...
WHERE start_date >= '2020-01-01' AND start_date < '2021-01-01' AND status = 'placeholder'

For each listed placeholder: locate the WHERE clause(s) in the SQL that reference the dummy literal and drop them, then strip the warning comment. SL chat-time filters compose narrowing predicates dynamically, so the source should represent the unfiltered dataset.

For fallback cards, dropping is simpler — the SQL still has the [[ ... ]] brackets and {{ var }} placeholders intact:

-- before:
WHERE 1=1
  [[AND {{ auction_end }} ]]
  [[AND status = {{ status }} ]]

-- after:
WHERE 1=1

Step B — Inline {{#N}} references (fallback cards only)

Resolved cards already have {{#N}} inlined for you. For fallback cards, each {{#N}} (or {{#N-some-slug}}) in the SQL refers to another card's resolvedSql. The referenced card is in the WU's rawFiles or dependencyPaths. Read it with read_raw_file, then inline its SQL.

If the reference has an alias (from {{#5996-listing-interactions}} tb), the outer SQL probably uses that alias (select tb.* ..., tb.column_name, etc.). When you inline, you must EITHER:

1. Pick a single base table inside the inlined SQL and rename its alias to the outer alias. Useful when the inlined card is SELECT * FROM listings JOIN ... — set the LISTINGS alias to tb and tb.* keeps working in the outer query.
2. Replace the outer alias references with explicit columns from the inlined SQL. Useful when the inlined card has multiple JOINs and tb.* is ambiguous.

Never leave the outer alias dangling: after inlining, grep your SQL for the outer alias name and rewrite or remove every reference. A leftover tb.* with no tb table is the most common failure mode here.

Step C — Inlining cleanup checklist

After Steps A and B, your SQL must:

• Contain no placeholder-warning comment, no {{, }}, [[, or ]] characters anywhere.
• Reference no aliases that aren't defined inside the SQL itself.
• Be valid as a standalone subquery (the validator runs SELECT * FROM (your_sql) LIMIT 1).

If resolutionStatus: "fallback" and the SQL is still complex enough that you can't confidently translate it, skip the card rather than writing broken SQL. Call emit_unmapped_fallback with the staged card path as rawPath, reason: "metabase_sql_untranslated", and fallback: "flagged".

Join-graph connectivity

For source_type: table:

• Use FK columns (semantic_type: type/FK) to declare many_to_one joins to dimension sources.
• Match column names ending in _id against existing sources' grain columns.

For source_type: sql:

• The validator parses your SQL and rejects the write if any FROM/JOIN table has a manifest entry that you did not declare in joins:. The error names every missing join target — declare a many_to_one join for each and reissue.
• Tables outside the manifest (schemas not covered by this connection — e.g. staging.* referenced from a MARTS source) are not flagged. For those, write a single-line wiki_write with key unmapped-table-<table_name> so the gap is documented, then call emit_unmapped_fallback with the staged card path as rawPath, reason: "table_outside_manifest", and fallback: "wiki_only".

Joins on manifest-backed names compose: the manifest's joins are inherited automatically, and any overlay joins: are merged on top (deduped by to + on). Use disable_joins: ["<on-clause>"] in the overlay to suppress a specific manifest join. If sl_discover shows a manifest-backed source with Joins: 0 and the warehouse FK metadata is genuinely absent, declaring application-level joins via the overlay is fair game — bootstrap with sl_write_source (overlay shape above), then refine via sl_edit_source.

Cross-card references ({{#N}})

Resolved cards (resolutionStatus: "resolved") have these inlined for you. Unresolved cards (resolutionStatus: "fallback") need manual handling — see "SQL translation from raw native to KSL" above.

Provenance markers

Every SL source and wiki page you write carries HTML-comment provenance tags pointing to the cards/<id>.json files they derive from:

# <!-- from: raw-sources/<connId>/metabase/<syncId>/cards/7.json -->
name: orders
...

If a source is derived from multiple cards (e.g. a generalized source for a near-duplicate group), emit one tag per contributing card.

Quality standards

Source definitions must follow ktx-sl YAML conventions:

• source_type: "table" (physical table/view) or "sql" (arbitrary SQL / derived view).
• table: required when source_type: "table" (e.g. "public.orders").
• sql: required when source_type: "sql".
• grain: what one row represents (e.g. [id], [customer_id, product_id]).
• columns: all columns with correct types (string, number, time, boolean).
• Time columns: mark with role: time.
• joins: use correct relationship types (many_to_one for FK→PK, one_to_many for reverse).
• joins.on: local_column = TARGET_SOURCE.target_column — the right side MUST include the target source name.
• measures.expr: aggregation expression (e.g. "sum(amount)"); optional filter for business rules; required description.

Measure naming: descriptive snake_case (e.g. total_revenue, avg_order_value).

Rules

• Prefer adding measures to existing sources over creating new ones.
• Before editing, always sl_read_source the source to check for existing measures.
• Don't duplicate measures (same aggregation on the same column).
• If two measures differ only by a filter (e.g. revenue vs paid_revenue), they are distinct.
• Use the card's name + description to write meaningful measure descriptions.
• When multiple cards in a WU are near-duplicates, create ONE generalized source; the runner will skip the rest automatically.
• Process every card in the WU — don't stop early.