bigquery-sql

Use when designing or implementing data quality checks — freshness, volume, schema, uniqueness, referential integrity, distribution — and choosing where they…

Use when meeting an unfamiliar table or dataset — produce a structured profile (grain, per-column stats, coverage, red flags) before building on it, writing…

Use when defining a data contract between a producer and consumers, checking a dataset or schema change against an existing contract, or deciding whether a…

Use when the user wants a quick or fast check of their uncommitted or staged changes before committing.

Use when the user wants to review their uncommitted or staged changes before committing.

Use when working with Apache Airflow — creating DAGs, writing tasks, configuring operators, debugging pipelines, or applying scheduling best practices.

Use when creating or running database migrations with Alembic and SQLAlchemy 2.0 — setting up Alembic, resolving multiple heads and merge conflicts, or…

Use when creating interactive, web-ready charts, dashboards with linked charts, or Grammar-of-Graphics visualization work.

Use when comparing two tables or datasets — before/after a pipeline change, source vs destination after a migration, dev vs prod models — to prove they match…

Use when creating dbt models, writing dbt SQL, configuring dbt projects, debugging dbt runs, or writing dbt tests.

Use when working with Docker — writing Dockerfiles, debugging containers, optimizing builds, Docker Compose, or GPU container patterns.

Use when setting up .env files and pydantic-settings, creating .env.example, managing configuration across dev/staging/production, or testing with different…