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ktx/packages/context/skills/looker_ingest/SKILL.md
Andrey Avtomonov 3ce510b55b rename klo to ktx
2026-05-10 23:51:24 +02:00

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name description callers
looker_ingest Extract durable KTX knowledge and semantic-layer contribution proposals from staged Looker runtime dashboard, Look, and explore JSON. Load for WorkUnits whose raw files are under explores/, dashboards/, or looks/.
memory_agent

Looker Runtime Ingest

Looker runtime ingest turns API-staged dashboards, Looks, and explores into durable KTX memory. Runtime entities are evidence. They are not themselves the final knowledge shape.

Required Workflow

  1. Read every rawFiles entry for the WorkUnit.
  2. Read relevant dependencyPaths before making a decision. For dashboard and Look WUs this usually includes the referenced explore JSON, signal files, folders/tree.json, and users/<id>.json.
  3. Treat signals/*.json, owners, folders, schedules, and favorites as prioritization or provenance context only.
  4. Extract generalizable metric formulas, segment definitions, field semantics, and domain conventions.
  5. Use wiki_search, sl_discover, and sl_read_source before writing so new content merges with existing memory instead of duplicating it.
  6. Use context_evidence_search or context_evidence_read to obtain evidence chunk IDs for any wiki-bound knowledge candidate.
  7. Use context_candidate_write for durable wiki-bound knowledge. Do not call wiki_write from a Looker WorkUnit; Stage 4 reconciliation promotes candidates and writes wiki pages.
  8. Use looker_query_to_sl for each Look query or dashboard tile query that has a query object.
  9. Write SL from Looker runtime evidence only through the staged warehouse target contract. For explores and inherited dashboard/Look queries, branch on targetTable.ok; when it is true, write on targetWarehouseConnectionId and use targetTable.canonicalTable as source.table. When it is false or missing, write wiki knowledge candidates and record emit_unmapped_fallback with the staged reason.
  10. Run sl_validate after every SL write. If validation fails, fix the source or roll it back before the WorkUnit ends.

Explore WorkUnits

Explore WUs have raw files like explores/<model>/<explore>.json and usually depend on lookml_models.json.

Use the deterministic API-derived source key:

looker__<model>__<explore>

For example, modelName: "b2b" and exploreName: "sales_pipeline" map to looker__b2b__sales_pipeline.

Mapped explore write shape:

{
  "connectionId": "22222222-2222-4222-8222-222222222222",
  "sourceName": "looker__b2b__sales_pipeline",
  "source": {
    "name": "looker__b2b__sales_pipeline",
    "table": "proj.dataset.opportunities",
    "grain": ["opportunity_id"],
    "columns": [
      {
        "name": "opportunity_id",
        "type": "string"
      },
      {
        "name": "arr",
        "type": "number"
      }
    ],
    "measures": [
      {
        "name": "total_arr",
        "expr": "sum(arr)"
      }
    ]
  }
}

Every concrete value in that example must be backed by raw Looker field SQL, source_tables preflight, source_columns, or existing SL when applied to a real WorkUnit. If the evidence is not present, write wiki candidates and emit emit_unmapped_fallback.

The staged explore file carries warehouse target fields populated before the WU starts:

  • connectionName: the Looker runtime connection name.
  • targetWarehouseConnectionId: the resolved warehouse connection id, or null when the Looker connection is unmapped.
  • rawSqlTableName: Looker's verbatim sql_table_name. Keep it as provenance only.
  • targetTable: the parsed target-table union. Use this as the sole branch condition.

When targetTable.ok === true, the explore has a complete KTX backing target. Before writing:

  1. Use targetTable.catalog, targetTable.schema, and targetTable.name for source_tables preflight matching through sl_discover or sl_read_source.
  2. Use Looker field sql, labels, descriptions, and type metadata to derive source columns, measures, segments, joins, and grain.
  3. Call sl_write_source or sl_edit_source with connectionId: targetWarehouseConnectionId.
  4. Set source.name to the deterministic API-derived source key, for example looker__b2b__sales_pipeline.
  5. Set source.table to targetTable.canonicalTable.
  6. Run sl_validate after every SL write.

The table field is targetTable.canonicalTable, not rawSqlTableName. Raw Looker values can contain aliases such as schema.table AS x, Looker templates such as ${TABLE}, or derived-table SQL. Those raw forms do not compose safely with SL generation. targetTable.canonicalTable is the dialect-quoted identifier rebuilt by the parser.

Use targetTable.{catalog,schema,name} only for source_tables preflight. Do not put those tuple fields separately into the SL source unless the SL schema already asks for them.

When targetTable.ok === false, keep the WU wiki-only for SL purposes. Capture durable domain semantics with context_candidate_write, then emit a fallback with the EXACT structured reason code from targetTable.reason. Put any human-readable context in detail, NOT in reason:

{
  "rawPath": "explores/b2b/sales_pipeline.json",
  "reason": "no_connection_mapping",
  "detail": "Looker connection b2b_sandbox_bq is not mapped to a warehouse connection",
  "fallback": "wiki_only"
}

Valid reason codes (use exactly one, no other strings allowed): no_connection_mapping, looker_template_unresolved, derived_table_not_supported, no_physical_table, multiple_table_references, unsupported_dialect, parse_error, missing_target_table.

When targetTable is null, read the raw explore file again. If the target is still absent, emit the same fallback with "reason": "missing_target_table".

Look And Dashboard WorkUnits

Looks have raw files like looks/<id>.json. Dashboards have raw files like dashboards/<id>.json. Dashboard tiles with inline query objects follow the same decision rules as Looks.

For each query:

  1. Call looker_query_to_sl with the query JSON, title, content type, and usage counts if available.
  2. Read the proposal's targetStatus, targetWarehouseConnectionId, targetTable, sourceTable, and canWriteStandaloneSource.
  3. If canWriteStandaloneSource is true, use targetWarehouseConnectionId for SL tools and sourceTable / targetTable.canonicalTable as the source table. Verify the proposal against the parent explore dependency and existing SL before writing.
  4. If the proposal decision is measure_added, add or edit a measure only after verifying the expression against the explore field SQL or an existing source column.
  5. If the proposal decision is source_created, create a source only when canWriteStandaloneSource is true and the filter is canonical. Use source.table = targetTable.canonicalTable.
  6. If targetStatus is unmapped, unparseable, or missing_target_table, keep SL wiki-only for this query and call emit_unmapped_fallback with the proposal's target reason or status.
  7. If the proposal decision is wiki_only, write a context candidate only when the Look or dashboard names a reusable business concept.

Capture Rules

Write SL for:

  • reusable aggregations with clear formulas;
  • reusable segment predicates that appear canonical;
  • calculated dimensions that are stable and backed by raw Looker query evidence;
  • joins or source relationships that are explicit in the explore JSON.

Write wiki for:

  • metric definitions in dashboard or Look titles, descriptions, axis labels, and filter semantics;
  • business meaning of an explore;
  • concept aliases used by teams;
  • caveats about multiple competing definitions.

Skip:

  • point-in-time values and chart screenshots;
  • dashboard layout, tile positions, colors, visualization types, and render settings;
  • owner names, top users, recipient counts, favorite counts, schedules, and usage counts as narrative content;
  • ad-hoc low-usage queries with no durable business semantics;
  • simple saved views of fields with no metric, segment, or concept definition.

Usage Signals

Use usage only to prioritize:

  • zero or near-zero usage lowers priority and often means skip;
  • high usage raises confidence that a metric or segment is canonical;
  • schedules and favorites can break ties between otherwise similar candidates.

When calling context_candidate_write, usage can affect scoring:

  • High usage (queryCount30d >= 10 or uniqueUsers30d >= 3) can justify authorityScore: 3 and reuseScore: 3 when the evidence is otherwise durable.
  • Zero recent usage should usually use actionHint: "skip" or lower reuseScore unless the content clearly defines a canonical business concept.
  • Schedules and favorites can raise reuseScore by 1 when deciding between otherwise similar candidate scores.

Never include the usage counts themselves in assertion, rationale, or eventual wiki prose.

Never write usage numbers, owner names, folder names, top users, schedule counts, or recipients into wiki article prose. If attribution is needed, keep it in provenance through the normal ingest action trail.

Provenance And Cross-References

When writing candidates from Looker evidence, cite chunk IDs from context_evidence_search or context_evidence_read. Stage 4 reconciliation writes wiki pages from promoted candidates and sets sl_refs when the source exists or was created in the run.

When an SL action is written on targetWarehouseConnectionId, the runner records targetConnectionId on the action and syncs knowledge_sl_refs to the warehouse connection. The wiki article still belongs to the Looker run connection; the SL ref belongs to the warehouse. Do not rewrite the source name or connection id in wiki frontmatter by hand. Use normal SL tool calls and let Stage 4 reconcile refs from actions.

Use these source-key conventions:

  • API-derived explore source: looker__<model>__<explore>
  • API-derived segment source: looker__<explore>__<slug>
  • File-adapter source, when present: <model>__<explore> without the looker__ prefix

During Stage 4 reconciliation, when both looker__<model>__<explore> and <model>__<explore> exist for the same connection, treat the unprefixed file-adapter source as canonical. Rewrite wiki sl_refs to the unprefixed source, remove the API-derived source if it was created in this run, and call emit_artifact_resolution with actionType: "subsumed", artifactKind: "sl", artifactKey: "looker__<model>__<explore>", and the raw explore path that produced it.

If a file-adapter source already exists and clearly subsumes the API-derived source, prefer the file-adapter source in sl_refs and mention the API entity only as evidence in the wiki content.

Examples

Measure proposal from a Look:

{
  "title": "Open Pipeline ARR",
  "query": {
    "model": "b2b",
    "view": "sales_pipeline",
    "fields": ["opportunities.arr", "opportunities.stage"],
    "filters": { "opportunities.stage": "open" }
  }
}

Expected handling:

  • call looker_query_to_sl;
  • verify opportunities.arr and opportunities.stage against the explore dependency and existing SL;
  • add or update a measure only if the resulting expression validates;
  • write wiki for the durable definition "open pipeline ARR" if it is not already captured;
  • avoid mentioning query counts or users in wiki prose.

Simple saved view:

{
  "title": "Accounts By Region",
  "query": {
    "model": "b2b",
    "view": "accounts",
    "fields": ["accounts.region", "accounts.segment"],
    "filters": {}
  }
}

Expected handling:

  • no SL write;
  • wiki only if the title or description defines a reusable company concept;
  • otherwise skip.