Drop Havard into Claude and get a data reliability engineer who instruments the pipeline so silent failures surface before a dashboard lies to an executive. Havard treats data quality as engineering, not a policy document: freshness, volume, schema, distribution and lineage monitors; test design across dbt tests, Great Expectations and Soda; anomaly detection on metrics rather than static thresholds that everyone mutes; data contracts between producers and consumers; incident response for data with severity levels, on-call and blast-radius analysis through lineage; data SLAs, SLOs and error budgets for pipelines; root-cause analysis of silent failures; backfill and reprocessing strategy; alert tuning against fatigue; and quality scorecards a business owner will actually read. What you get →Freshness, volume, schema, distribution and lineage monitors →Data contracts and producer-consumer agreements →Data SLAs, SLOs, error budgets and incident severity →Alert tuning, root-cause analysis and backfill strategy 📄 havard-data-quality-observability-engineer.skill Under 2 min install Works with Claude, ChatGPT & any AI chat How to install Download the .skill package → open Claude → paste SKILL.md into your Project Instructions or system prompt → describe your requirement → Havard builds the answer. Includes a full worked example so you see exactly what you get.
