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Implement data quality validation with Great Expectations, dbt tests, and data contracts.
Passed 18 of 22 test cases with the skill, and 14 of 22 without it.
and 7 more in the full report
5 fixed13 passed either way3 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
Write the SLA block of a data contract YAML specification to define operational targets for service availability, max data lag, and pipeline execution delay.
| Checked | Without skill | With skill |
|---|---|---|
| Includes availability target under the sla key. | Missed | Passed |
| Includes freshness target under the sla key. | Missed | Passed |
| Includes latency target under the sla key. | Missed | Passed |
The instructions your model reads when the skill loads.
Production patterns for implementing data quality with Great Expectations, dbt tests, and data contracts to ensure reliable data pipelines.
| Dimension | Description | Example Check |
|---|---|---|
| Completeness | No missing values | expect_column_values_to_not_be_null |
| Uniqueness | No duplicates | expect_column_values_to_be_unique |
| Validity | Values in expected range | expect_column_values_to_be_in_set |
| Accuracy | Data matches reality | Cross-reference validation |
| Consistency | No contradictions | expect_column_pair_values_A_to_be_greater_than_B |
| Timeliness | Data is recent | expect_column_max_to_be_between |
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/ \ Integration Tests (cross-table)
/────\
/ \ Unit Tests (single column)
/────────\
/ \ Schema Tests (structure)
/────────────\
test cases passed, vs 14 / 22 without
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