qa-data-quality
Data quality testing for analytical pipelines: 5 skills (dbt-testing, great-expectations, soda-checks, data-quality-gate, data-quality-conventions) and 2 agents (schema-diff-reviewer, data-anomaly-triager).
Install this plugin
/plugin install qa-data-quality@testland-qaPart of role bundle: qa-role-ai
qa-data-quality
Data quality testing for analytical pipelines: dbt-tests, Great Expectations, Soda, schema drift detection, anomaly triage, and schema-diff review.
Components
| Type | Name | Description |
|---|---|---|
| Skill | dbt-testing | Author and run dbt data tests (generic, singular, custom-macro), parse run_results.json, gate dbt build on test results. |
| Skill | great-expectations | Author GX Core ExpectationSuites + Checkpoints; run validations on Pandas/SQL/Spark batches; parse JSON results for CI gating. |
| Skill | soda-checks | Author SodaCL checks against SQL warehouses; configure scan profiles; gate CI on soda scan exit code. |
| Skill | data-quality-gate | Aggregate dbt / GX / Soda check results into a single severity-aware go/no-go gate with markdown + JSON artifact for CI. |
| Agent | schema-diff-reviewer | Review a DB schema diff for breaking-vs-additive changes, missing data tests, and downstream consumer impact; returns a Critical/Warning/Info findings table. |
| Agent | data-anomaly-triager | Classify a data-quality failure (dbt/GX/Soda) into drift / outlier / missing / referential / freshness with owner routing and remediation. |
| Skill | data-quality-conventions | Reference catalog: engine selection, column/table coverage, severity tiering, freshness/SLA conventions, anti-patterns. |
Install
/plugin marketplace add testland/qa
/plugin install qa-data-quality@testland-qaSkills
data-quality-conventions
Reference catalog of data-quality conventions - when to choose dbt-tests vs Great Expectations vs Soda, column-level vs table-level coverage, severity tiering, SLA and freshness conventions, and common anti-patterns to avoid. Use when designing coverage for a new data product or auditing an existing one.
data-quality-gate
Builds a release-readiness gate for a data pipeline by gathering check results from one or more engines (dbt, Great Expectations, Soda), applying severity-aware pass/fail thresholds, and emitting a single go / no-go decision with per-check rationale. Use when authoring a CI step that must fail the build when data quality drops below thresholds.
dbt-testing
Authors and runs dbt data tests (generic, singular, and custom-macro), parses test failure output from run_results.json, and gates dbt build on test results. Use when the user works with a dbt project, asks about model assertions, or needs CI gates on a data pipeline.
great-expectations
Authors Great Expectations (GX Core) ExpectationSuites, builds ValidationDefinitions and Checkpoints, runs validation against tabular batches, and parses the JSON result for CI gating. Use when the user works with Great Expectations on Pandas, SQL, or Spark data.
soda-checks
Authors and runs SodaCL (Soda Checks Language) checks against SQL warehouses (Snowflake, BigQuery, Postgres, Redshift, etc.) via `soda scan`, configures scan profiles in configuration.yml, and gates CI on scan exit code. Use when the user works with Soda Core / Soda Cloud or needs YAML-driven warehouse data quality.
Agents
data-anomaly-triager
Reads a single data-quality failure (a dbt test result, a Great Expectations validation result, or a Soda scan line) and classifies the anomaly into one of five categories - drift, outlier, missing, referential, freshness - then proposes the likely owner and a remediation step. Use proactively after a data pipeline failure to route the failure to the right team.
schema-diff-reviewer
Reviews a database-schema diff (PR migration files or `dbt run-operation` output) for breaking changes vs additive changes, missing data tests on new/changed columns, and downstream consumer impact. Use proactively before merging schema migrations. Returns a Critical / Warning / Info findings table with file:line references.