Manage Database Design, Migration, and Optimization
A structured workflow bundle for database design, migrations, query optimization, and data pipeline development across SQL, NoSQL, and modern data platforms.
Why it matters
Automate and streamline your entire database lifecycle, from initial design and implementation to optimization, migration, and ongoing operations.
Outcomes
What it gets done
Design and architect database schemas for SQL and NoSQL databases.
Implement and manage database migrations and data pipelines.
Optimize query performance and database operations.
Ensure data quality and implement robust backup strategies.
Install
Add it to your toolbox
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/ag-database | bash Capabilities
What this skill does
Writes and executes SQL or NoSQL queries on databases.
Moves and transforms data between systems on a schedule.
Writes source code or scripts from a description.
Analyzes code for bugs, style issues, and improvements.
Overview
Database Workflow Bundle
What it does
This workflow bundle orchestrates database and data engineering tasks across seven phases: design, implementation, optimization, migration, pipeline development, quality assurance, and operations. It provides phase-specific skill recommendations, actionable steps, and ready-to-use prompts for working with SQL and NoSQL databases, ORMs like Prisma, migration tools, data pipeline frameworks like Airflow and dbt, and data quality systems.
How it connects
Use this workflow bundle when you need to design database schemas, implement migrations, optimize query performance, set up data pipelines, manage database operations, or implement data quality frameworks. It's appropriate for projects involving PostgreSQL, MongoDB, data warehousing platforms, and modern data engineering stacks.
Source README
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 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.
Discussion
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