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Vectle found the following relevant skills: - null value error failed not null constraint violated on insert - great expectations error: expect_column_values_to_not_be_null validation failed
Vectle found the following relevant skills: - agent hallucinating annotator IDs that do not exist caused routing failure - agent hallucinating a build number that was never uploaded: generated wrong values and validation failed with error
Vectle found the following relevant skills: - dashboard agent generated LookML with nonexistent dimensions: agent invented fields not in the warehouse schema - [agent hallucinating correlation error invented campaign causing revenue dip failure]…
Vectle found the following relevant skills: - agent hallucinating department names that do not exist: generated wrong values and validation failed with error - [agent hallucinating feature names that do not exist in registry error curation faile…
The insert is being rejected row by row, so find the offending rows before changing the constraint: run the load in a transaction or staging pass that captures the rows with nulls in the constrained column, trace them to the source or the transform that produced them, and fix the production of those rows. Use when an …
A BigQuery load that hits a null in a required field fails the job or the rows depending on configuration: identify the required fields from the table schema, find the source rows carrying nulls for them, and fix the producer or route those rows to a dead-letter table. Use when a BigQuery load failing on a null in a r…
Vectle found the following relevant skills: - great expectations error: expect_column_values_to_not_be_null validation failed - rails migration error PG NotNullViolation: column contains null values when adding NOT NULL constraint
Walk the chain in order and test each hand off with one known record, fix the first hand off where the record disappears, and add a log line at that hand off. Use when you see this exact failure in BigQuery and the affected record or object can be named. Not for a different BigQuery error with another message, or for …
Vectle found the following relevant skills: - agent dropped nullable columns to save tokens, schema drift alert fired - agent produced chart with 40k series from unaggregated query: Grafana render timeout on agent-built dashboard
A failed col_vals_not_null step in Pointblank comes with an interrogation extract: pull the failing rows from the validation report, count them against the step's thresholds, and trace the nulls to the data step that produced them. Use when a Pointblank validation where the col_vals_not_null step fails interrogation. …
Vectle found the following relevant skills: - great expectations error: expect_column_values_to_not_be_null validation failed - agent hallucinating approvals for users who already left: generated wrong values and validation failed with error
Vectle found the following relevant skills: - Database Error in test not_null_orders_customer_id" - dbt test accepted_values failed on nulls - [how to fix dbt test not_nul…
Staging is where source nulls first become visible, so query the raw source for the nulls before editing the model: if the source sends nulls, either filter or coalesce them in staging with a documented rule, or move the not_null test to the model where the field is truly required. Use when a dbt staging model failing…
Do not just rerun the suite: query the table for the null rows the expectation flagged, decide whether the nulls are a source bug or a rule that is too strict for a legitimately optional column, fix at that layer, and rerun only the failed expectation. Use when a Great Expectations suite guarding a production table. N…
Fixes an access review agent that generates wrong values, described as access expiry dates in the wrong year, and fails validation, by grounding every generated value in a source record and rejecting ungrounded output. Use when agent output invents people, dates or states that the source systems do not show. Not for s…
Fixes an agent that invents date ranges that exclude all data while building a dashboard, by grounding every generated value in a live lookup against Metabase and the warehouse. Use when validation rejects agent output for referencing things that do not exist. Not for a human authoring error in a hand written dashboar…
Vectle found the following relevant skills: - agent hallucinating manager names that are not in HR data URIs generated wrong values and validation failed with error - [agent hallucinating chart types that cannot render the data URIs generated wr…