Data & Database Review for AI Agents

Data & Database Review for AI Agents

A complete reference for AI coding agents that review data code: database migrations, SQL and indexes, transactions and locking, Postgres and Supabase, ORMs, Redis, replication and events, batch ETL/ELT, dbt and warehouse models, Airflow and Dagster, Spark, Kafka and stream processing, data contracts and quality checks, metrics and governance-in-code, and tests for data code. 18 rule chapters plus a reading map and appendices, with 184 rules (8 CRITICAL, 81 HIGH, 84 MEDIUM, 11 LOW). Every rule has concrete thresholds, BAD/GOOD examples in SQL, Python, YAML and TypeScript (TypeScript only where it is real: Prisma, Supabase JS, ioredis, kafkajs), machine-checkable verification criteria, and a page citation into a named source book.

This is the companion to Code Review for AI Agents. The parent book stays the first stop for every review, and its Chapter 16 (Database & ORM Patterns) and Chapter 19 (Distributed Systems & Service Boundaries) cover the basics. Load this book when a diff touches migrations, SQL, a schema file, a cache, an event consumer, a pipeline, a dbt project, a DAG, a Spark job, a Kafka client, a data contract or a metric definition. Rules here deepen parent rules and never restate them; Chapter 1 lists the file signals and Chapter 2 maps every parent rule to the companion rules that extend it.

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