Database

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

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Cache workflow configurations and automation patterns. Retrieve prior pipeline designs to avoid re-building similar flows from scratch.

# Check for prior workflow/automation context before starting
python3 execution/memory_manager.py auto --query "automation patterns and workflow configurations for Database"

Storing Results

After completing work, store workflow/automation decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Workflow: automated data pipeline with retry logic, dead-letter queue, and Slack alerts on failure" \
  --type technical --project <project> \
  --tags database workflow

Multi-Agent Collaboration

Share workflow state with other agents so they can trigger, monitor, or extend the automation.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Workflow automation deployed — pipeline processing 1000+ events/day with 99.9% success rate" \
  --project <project>

Playbook Engine

Combine this skill with others using the Playbook Engine (execution/workflow_engine.py) for guided multi-step automation with progress tracking.