qa-data-quality
Data quality testing for analytical pipelines: dbt tests, Great Expectations, Soda checks, data contracts, and a go / no-go release gate. Extracts data contracts from data-product specs and covers tool choice and severity conventions. Agents (schema-diff-reviewer, data-anomaly-triager) review schema diffs for breaking changes and classify failed checks by anomaly type and likely owner.
Install this plugin
View source (opens in a new tab)/plugin install qa-data-quality@testland-qaFirst time? Add the marketplace with /plugin marketplace add testland/qa
Plugin
- Category
- Quality engineering
- Version
v1.1.3- Contents
- 6 skills · 2 agents
- Part of
- qa-role-ai
Skills6
data-contract-extractorUse when scoping a new data product or formalizing checks on an existing one.skilldata-quality-conventionsUse when designing data-quality coverage for a new data product or auditing an existing one: choosing dbt tests vs Great Expectations vs Soda, column vs table-level checks, severity tiers, SLA and freshness conventions, and anti-patterns to avoid.skilldata-quality-gateUse when a data pipeline needs one CI step that fails the build when data quality drops.skilldbt-testingUse whenever a dbt project needs data tests or CI gates: generic, singular and custom-macro tests, model assertions, reading failures from run_results.json and gating dbt build on the results.skillgreat-expectationsUse whenever data validation uses Great Expectations (GX Core) on Pandas, SQL or Spark: ExpectationSuites, ValidationDefinitions and Checkpoints, running validations on batches and reading the JSON result to gate CI.skillsoda-checksUse when warehouse data quality (Snowflake, BigQuery, Postgres, Redshift and others) should be checked with YAML-based SodaCL: writing checks, configuration.yml data sources, soda scan runs and CI gating on the exit code, with Soda Core or Soda Cloud.skill
Agents2
data-anomaly-triagerReads 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.agentschema-diff-reviewerReviews 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.agent
About qa-data-quality
Data quality testing for analytical pipelines: data-contract extraction, dbt-tests, Great Expectations, Soda, schema drift detection, anomaly triage, and schema-diff review.