Agent skill

data-quality

Data quality testing with dbt tests, Great Expectations, and monitoring.

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Install this agent skill to your Project

npx add-skill https://github.com/timequity/vibe-coder/tree/main/skills/data/data-quality

SKILL.md

Data Quality

Quality Dimensions

Dimension Description Test
Completeness No missing values NOT NULL, count checks
Uniqueness No duplicates UNIQUE, distinct counts
Validity Values in range Range checks, regex
Consistency Matches across sources Cross-table checks
Timeliness Data is fresh Freshness checks

dbt Tests

Schema Tests

yaml
models:
  - name: fct_orders
    columns:
      - name: order_id
        tests:
          - unique
          - not_null
      - name: status
        tests:
          - accepted_values:
              values: ['pending', 'completed', 'cancelled']
      - name: amount
        tests:
          - not_null
          - dbt_utils.accepted_range:
              min_value: 0
              max_value: 1000000

Custom Tests

sql
-- tests/assert_positive_revenue.sql
select *
from {{ ref('fct_orders') }}
where amount < 0

Relationship Tests

yaml
- name: customer_id
  tests:
    - relationships:
        to: ref('dim_customer')
        field: customer_id

Great Expectations

python
import great_expectations as gx

context = gx.get_context()

validator = context.sources.pandas_default.read_csv("data.csv")

validator.expect_column_values_to_not_be_null("order_id")
validator.expect_column_values_to_be_unique("order_id")
validator.expect_column_values_to_be_between("amount", 0, 1000000)

results = validator.validate()

Monitoring

  • Row count trends
  • Null percentage trends
  • Schema drift detection
  • Freshness SLAs
  • Anomaly detection

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