Database

Database development and operations workflow covering SQL, NoSQL, database design, migrations, optimization, and data engineering.

Published by @sickn33 and contributors·0 agent reads / 30d·0 saves·

Database Workflow Bundle

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 architecture
  • database-design - Schema design
  • postgresql - PostgreSQL design
  • nosql-expert - NoSQL design
Actions
  1. Gather requirements
  2. Design schema
  3. Define relationships
  4. Plan indexing strategy
  5. 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 ORM
  • database-migrations-sql-migrations - SQL migrations
  • neon-postgres - Serverless Postgres
Actions
  1. Set up database connection
  2. Configure ORM
  3. Create migrations
  4. Implement models
  5. 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 optimization
  • sql-optimization-patterns - SQL optimization
  • postgres-best-practices - PostgreSQL optimization
Actions
  1. Analyze slow queries
  2. Review execution plans
  3. Optimize indexes
  4. Refactor queries
  5. 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 migration
  • framework-migration-code-migrate - Code migration
Actions
  1. Plan migration strategy
  2. Create migration scripts
  3. Test migration
  4. Execute migration
  5. Verify data integrity
Copy-Paste Prompts
Use @database-migration to plan database migration

Phase 5: Data Pipeline Development

Skills to Invoke
  • data-engineer - Data engineering
  • data-engineering-data-pipeline - Data pipelines
  • airflow-dag-patterns - Airflow workflows
  • dbt-transformation-patterns - dbt transformations
Actions
  1. Design data pipeline
  2. Set up data ingestion
  3. Implement transformations
  4. Configure scheduling
  5. 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 quality
  • data-engineering-data-driven-feature - Data-driven features
Actions
  1. Define quality metrics
  2. Implement validation
  3. Set up monitoring
  4. Create alerts
  5. Document standards
Copy-Paste Prompts
Use @data-quality-frameworks to implement data quality checks

Phase 7: Database Operations

Skills to Invoke
  • database-admin - Database administration
  • backup-automation - Backup automation
Actions
  1. Set up backups
  2. Configure replication
  3. Monitor performance
  4. Plan capacity
  5. 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 development
  • cloud-devops - Infrastructure
  • ai-ml - AI/ML data pipelines
  • testing-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.

Bundled with this artifact

2 files

Reference files that ship alongside this artifact. Agents pull these in only when the task needs them.

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