Database V2

Database Workflow Bundle

Overview

This public intake copy packages plugins/antigravity-awesome-skills/skills/database from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

Database Workflow Bundle

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Quality Gates, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Designing database schemas
• Implementing database migrations
• Optimizing query performance
• Setting up data pipelines
• Managing database operations
• Implementing data quality

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. database-architect - Database architecture
2. database-design - Schema design
3. postgresql - PostgreSQL design
4. nosql-expert - NoSQL design
5. Gather requirements
6. Design schema
7. Define relationships

Imported Workflow Notes

Imported: 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

Imported: 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

Imported: Related Workflow Bundles

• development - Application development
• cloud-devops - Infrastructure
• ai-ml - AI/ML data pipelines
• testing-qa - Data testing

Imported: Overview

Comprehensive database workflow for database design, development, optimization, migrations, and data engineering. Covers SQL, NoSQL, and modern data platforms.

Imported: Quality Gates

• Schema designed and reviewed
• Migrations tested
• Performance benchmarks met
• Backups configured
• Monitoring in place
• Documentation complete

Examples

Example 1: Ask for the upstream workflow directly

Use @database-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @database-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @database-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @database-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/database, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: 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.