Manage Database Design, Migration, and Optimization

Database Workflow Bundle sequences a comprehensive database workflow across seven phases: Database Design (invoking database-architect, database-design, postgresql, nosql-expert to gather requirements, design schema, define relationships, and plan indexing/scalability), Database Implementation (prisma-expert, database-migrations-sql-migrations, neon-postgres to set up connections, configure the ORM, create migrations, and seed data), Query Optimization (database-optimizer, sql-optimization-patterns, postgres-best-practices to analyze slow queries, review execution plans, optimize indexes, and implement caching), Data Migration (database-migration, framework-migration-code-migrate to plan, script, test, execute, and verify migrations), Data Pipeline Development (data-engineer, data-engineering-data-pipeline, airflow-dag-patterns, dbt-transformation-patterns to design pipelines, set up ingestion, implement transformations, and configure scheduling/monitoring), Data Quality (data-quality-frameworks, data-engineering-data-driven-feature to define metrics, implement validation, and set up monitoring/alerts), and Database Operations (database-admin, backup-automation to configure backups, replication, capacity, and security). Each phase names its skills to invoke and provides copy-paste prompts, for example:

Use @database-architect to design database schema

It also groups skills by technology: PostgreSQL (postgresql, postgres-best-practices, neon-postgres, prisma-expert), MongoDB (nosql-expert, azure-cosmos-db-py), Redis (bullmq-specialist, upstash-qstash), and Data Warehousing (clickhouse-io, dbt-transformation-patterns).

Use this workflow when designing database schemas, implementing database migrations, optimizing query performance, setting up data pipelines, managing database operations, or implementing data quality. It is not for tasks outside database design, implementation, optimization, migration, and operations - related concerns route to the separate development, cloud-devops, ai-ml, and testing-qa workflow bundles.

Input is the target database task - schema design, migration, optimization, pipeline, or operations need. Output is a sequenced list of sub-skills to invoke per phase, concrete actions for each phase (five per phase), and ready-to-use @skill-name prompts for each. A quality-gates checklist tracks completion: schema designed and reviewed, migrations tested, performance benchmarks met, backups configured, monitoring in place, and documentation complete.

Chains together named sub-skills across the database lifecycle: database-architect, database-design, postgresql, nosql-expert, prisma-expert, database-migrations-sql-migrations, neon-postgres, database-optimizer, sql-optimization-patterns, postgres-best-practices, database-migration, framework-migration-code-migrate, data-engineer, data-engineering-data-pipeline, airflow-dag-patterns, dbt-transformation-patterns, data-quality-frameworks, data-engineering-data-driven-feature, database-admin, and backup-automation. Technology-specific groupings also reference azure-cosmos-db-py, bullmq-specialist, upstash-qstash, and clickhouse-io. Related workflow bundles are development, cloud-devops, ai-ml, and testing-qa.

Teams and engineers running an end-to-end database initiative - from initial schema design through implementation, optimization, migration, data-pipeline build-out, and ongoing operations - who want a single sequenced checklist naming which specialist skill to invoke at each phase, with a ready-to-paste prompt for each, rather than assembling one from scratch or guessing which sub-skill applies at a given stage.