Lets your AI agent answer data questions against your warehouse accurately, using approved metric definitions, joinable columns and company knowledge, so it stops re-exploring tables and inventing its own numbers. Ingests dbt models and wiki pages to keep that context current.
@kaelioUse when answering a question that needs data from a ktx-connected database - investigating, analyzing, "how many", "show me",…
@kaelioMap dbt `schema.yml` / `properties.yml` models and sources into ktx semantic-layer overlays and column notes. Covers `sources:`…
@kaelioSynthesize durable KTX wiki pages from staged Google Drive document pulls. Load when a WorkUnit contains Google Doc raw files…
@kaelioIdentify recurring cross-table historic-SQL analytical intents from a bounded pattern shard and emit typed pattern evidence for…
@kaelioConvert one changed historic-SQL table usage bucket into typed table usage evidence for deterministic _schema projection.
@kaelioClassify and resolve conflicts detected during bundle ingest (structural duplicates, definitional contradictions, near-duplicate…
@kaelioInstalls and configures ktx, the open-source context layer for data agents — runs ktx setup non-interactively with hidden CLI…
@kaelioCapture semantic-layer and knowledge updates from a live database schema snapshot.
@kaelioExtract durable ktx knowledge and semantic-layer contribution proposals from staged Looker runtime dashboard, Look, and explore…
@kaelioMap a LookML view/model/explore into ktx semantic layer sources. Covers the LookML to ktx primitive table, provenance tagging,…
@kaelioConvert Metabase questions, models, and metrics into ktx Semantic Layer source definitions. Covers result-metadata to KSL column…
@kaelioMap a MetricFlow semantic_model or metric into ktx semantic layer sources. Covers the MetricFlow to ktx primitive table,…
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