database — quality + safety report

In the Skillier index (antigravity__database) · scanned 2026-06-03 · engine: builtin+triage

A
Quality
98/100
Safety

✓ Clean — no heuristic safety flags surfaced.

Heuristic flags from the builtin scanner, which is known to over-flag (it trips on legitimate env-reading integrations, security skills, and library .eval calls). This is NOT an authoritative malicious verdict — re-scan with SkillSpector for the authoritative result. Run the authoritative scan →

Skillproof quality grade A

📇 This skill is in the Skillier index (curated · deduped · quality-filtered). Install Skillier to route & load it into your AI client.

Quality notes

No explicit trigger / 'when to use'
low · quality · body
→ Add a 'When to use' section or 'Use this when …' line listing trigger conditions.

About this skill

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

📄 Read the SKILL.md
---
name: database
description: "Database development and operations workflow covering SQL, NoSQL, database design, migrations, optimization, and data engineering."
category: workflow-bundle
risk: safe
source: personal
date_added: "2026-02-27"
---

# 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.
Scan or optimize your own skill →

Want a live grade + an embeddable README badge? Run your skill through the free scanner.

Graded independently by Skillproof — nothing to sell the author. Quality is mechanical + corpus-grounded; safety flags are heuristic (builtin+triage), not a malicious verdict.