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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/.
Passed 10 of 17 test cases with the skill, and 5 of 17 without it.
and 8 more in the full report
9 fixed4 passed either way7 still failing2 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
An explicit canonical segment filter slugged `enterprise_accounts` is extracted from explore `b2b_sales`. What is the API-derived source key convention for this segment?
| Checked | Without skill | With skill |
|---|---|---|
| Formats segment key as looker__b2b_sales__enterprise_accounts | Missed | Passed |
The instructions your model reads when the skill loads.
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.
rawFiles entry for the WorkUnit.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.signals/*.json, owners, folders, schedules, and favorites as prioritization or provenance context only.wiki_search, sl_discover, and sl_read_source before writing so new content merges with existing memory instead of duplicating it.context_evidence_search or context_evidence_read to obtain evidence chunk IDs for any wiki-bound knowledge candidate.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.looker_query_to_sl for each Look query or dashboard tile query that has a query object.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.sl_validate after every SL write. If validation fails, fix the source or roll it back before the WorkUnit ends.For every Looker field reference, call entity_details on the underlying schema.table.column before promoting it to sl_refs or quoting it in wiki body.
Before writing a wiki page or SL source on any topic:
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:
entity_details({connectionId, targets: [{display: "<identifier>"}]}) -
confirm the identifier resolves; inspect native types, FK/PK, and
sampleValues.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"}).sql_execution({connectionId, sql: "SELECT 1 FROM <ref> LIMIT 0"}).test cases passed, vs 5 / 17 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.