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Implement data quality validation with Great Expectations, dbt tests, and data contracts.
Passed 19 of 22 test cases with the skill, and 19 of 22 without it.
1 fixed18 passed either way2 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
A test case the skill fixed. Same model, same prompt, with and without the skill.
In Great Expectations Python API, how is a pre-configured validation checkpoint triggered programmatically?
| Checked | Without skill | With skill |
|---|---|---|
| Calls context.run_checkpoint with the target checkpoint name | 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)
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/ \ Unit Tests (single column)
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/ \ Schema Tests (structure)
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test cases passed, vs 19 / 22 without
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