Overview
Comprehensive database workflow for database design, development, optimization, migrations, and data engineering. Covers SQL, NoSQL, and modern data platforms.
When to Use This Workflow
Use this workflow when:
- Designing database schemas
- Implementing database migrations
- Optimizing query performance
- Setting up data pipelines
- Managing database operations
- Implementing data quality
Workflow Phases
Phase 1: Database Design
Skills to Invoke
database-architect- Database architecturedatabase-design- Schema designpostgresql- PostgreSQL designnosql-expert- NoSQL design
Actions
- Gather requirements
- Design schema
- Define relationships
- Plan indexing strategy
- Design for scalability
Copy-Paste Prompts
Use @database-architect to design database schema
Use @postgresql to design PostgreSQL schema
Phase 2: Database Implementation
Skills to Invoke
prisma-expert- Prisma ORMdatabase-migrations-sql-migrations- SQL migrationsneon-postgres- Serverless Postgres
Actions
- Set up database connection
- Configure ORM
- Create migrations
- Implement models
- Set up seed data
Copy-Paste Prompts
Use @prisma-expert to set up Prisma ORM
Use @database-migrations-sql-migrations to create migrations
Phase 3: Query Optimization
Skills to Invoke
database-optimizer- Database optimizationsql-optimization-patterns- SQL optimizationpostgres-best-practices- PostgreSQL optimization
Actions
- Analyze slow queries
- Review execution plans
- Optimize indexes
- Refactor queries
- Implement caching
Copy-Paste Prompts
Use @database-optimizer to optimize database performance
Use @sql-optimization-patterns to optimize SQL queries
Phase 4: Data Migration
Skills to Invoke
database-migration- Database migrationframework-migration-code-migrate- Code migration
Actions
- Plan migration strategy
- Create migration scripts
- Test migration
- Execute migration
- Verify data integrity
Copy-Paste Prompts
Use @database-migration to plan database migration
Phase 5: Data Pipeline Development
Skills to Invoke
data-engineer- Data engineeringdata-engineering-data-pipeline- Data pipelinesairflow-dag-patterns- Airflow workflowsdbt-transformation-patterns- dbt transformations
Actions
- Design data pipeline
- Set up data ingestion
- Implement transformations
- Configure scheduling
- Set up monitoring
Copy-Paste Prompts
Use @data-engineer to design data pipeline
Use @airflow-dag-patterns to create Airflow DAGs
Phase 6: Data Quality
Skills to Invoke
data-quality-frameworks- Data qualitydata-engineering-data-driven-feature- Data-driven features
Actions
- Define quality metrics
- Implement validation
- Set up monitoring
- Create alerts
- Document standards
Copy-Paste Prompts
Use @data-quality-frameworks to implement data quality checks
Phase 7: Database Operations
Skills to Invoke
database-admin- Database administrationbackup-automation- Backup automation
Actions
- Set up backups
- Configure replication
- Monitor performance
- Plan capacity
- Implement security
Copy-Paste Prompts
Use @database-admin to manage database operations
Database Technology Workflows
PostgreSQL
Skills: postgresql, postgres-best-practices, neon-postgres, prisma-expert
MongoDB
Skills: nosql-expert, azure-cosmos-db-py
Redis
Skills: bullmq-specialist, upstash-qstash
Data Warehousing
Skills: clickhouse-io, dbt-transformation-patterns
Quality Gates
- Schema designed and reviewed
- Migrations tested
- Performance benchmarks met
- Backups configured
- Monitoring in place
- Documentation complete
Related Workflow Bundles
development- Application developmentcloud-devops- Infrastructureai-ml- AI/ML data pipelinestesting-qa- Data testing
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.