looker_ingest

Solid

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/.

Data & Documents 733 stars 42 forks Updated today Apache-2.0

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Quality Score: 95/100

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95
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100
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50
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Skill Content

# 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.canonica...

Details

Author
Kaelio
Repository
Kaelio/ktx-ai-data-agents-context
Created
3 weeks ago
Last Updated
today
Language
TypeScript
License
Apache-2.0

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