data-governance

More from dtsong/data-engineering-skills

• data-observability

  Use this skill when implementing monitoring, alerting, and incident response for data pipelines. Covers freshness monitoring, volume anomaly detection, schema change detection, alerting patterns, and incident response workflows. Common phrases: \"data freshness\", \"pipeline monitoring\", \"data anomaly\", \"schema drift\", \"data alerting\", \"incident response\", \"data observability\", \"stale data\". Do NOT use for writing dbt models (use dbt-transforms), pipeline scheduling (use data-pipelines), or data quality testing as deliverables (use data-testing).

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• client-delivery

  Use this skill when managing a consulting data cleaning engagement. Covers engagement setup, schema profiling, security tier selection, project scaffolding, deliverable generation, and client handoff. Common phrases: \"set up a cleaning project\", \"profile this schema\", \"data cleaning engagement\", \"generate deliverables\", \"client handoff\". Do NOT use for writing dbt models (use dbt-transforms), DuckDB queries (use duckdb), or pipeline orchestration (use data-pipelines).

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• tsfm-forecast

  Use this skill when generating time-series forecasting pipelines using foundation models. Covers TimesFM, Chronos, MOIRAI, and Lag-Llama model selection, DuckDB-based preprocessing code, Python inference generation, backtesting harnesses, multi-model comparison, and client forecast deliverables. Common phrases: \"time-series forecast\", \"demand forecasting\", \"TimesFM\", \"Chronos\", \"predict future values\", \"zero-shot forecast\". Do NOT use for ML model training or fine-tuning (use python-data-engineering), real-time/streaming forecasts (use event-streaming), or pipeline scheduling (use data-pipelines).

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