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Audit data quality across pipelines, warehouses, and stores.
Passed 16 of 20 test cases with the skill, and 6 of 20 without it.
and 16 more in the full report
11 fixed7 passed either way4 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
We have dataset `inventory.json` and audit definitions `inventory_rules.json`. Provide the exact command-line invocation to run our rule-based data quality runner script against this data with JSON structured output.
| Checked | Without skill | With skill |
|---|---|---|
| Uses dq_check_runner.py in execution command | Missed | Passed |
| Includes --format json parameter in command execution | Passed | Passed |
The instructions your model reads when the skill loads.
End-to-end data quality (DQ) practice: define DQ dimensions, write rule-based checks, detect schema drift, monitor freshness SLAs, respond to DQ incidents, build a maturity-graded program. Tool-agnostic — works whether you use Great Expectations, dbt tests, Soda Core, Monte Carlo, custom SQL, or hand-rolled scripts.
This skill is audit-focused, not pipeline-focused. For pipeline design, ETL, Spark/dbt, see engineering/senior-data-engineer.
| Situation | Skill applies |
|---|---|
| Setting up DQ from scratch on a new pipeline | Yes — start with DQ dimensions + check catalog |
| Auditing existing pipelines for missing DQ | Yes — dq_check_runner.py |
| Detecting schema drift in upstream sources | Yes — schema_drift_detector.py |
| Monitoring freshness / SLA on data assets | Yes — freshness_monitor.py |
| Responding to a DQ incident (bad data in prod) | Yes — incident response playbook |
| Designing a DQ governance model | Yes — DQ maturity model |
| Compliance evidence (SOC 2 PI1, GDPR, ISO 27001) | Yes — checks produce auditable artifacts |
| Building data pipelines for the first time | Use engineering/senior-data-engineer first |
Before running the audit, confirm these inputs. If any is unknown or vague, ASK — do not assume:
--data)dq_check_runner.py vs schema_drift_detector.py vs freshness_monitor.py)--max-age-min)Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
test cases passed, vs 6 / 20 without
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