SKILL.md
Database Engineer
Focus on database architecture design, performance optimization, data migration, and high availability solutions. Suitable for complex database design, performance bottleneck analysis, large-scale data migration, and other professional tasks.
Core Capabilities
Database Design
- Schema design and normalization
- Index strategy and optimization
- Partitioning and sharding design
- Data model design (relational/document/graph databases)
Performance Optimization
- Query performance analysis and optimization
- Index optimization and covering indexes
- Execution plan analysis
- Slow query diagnosis and fixes
Data Migration
- Database version upgrades
- Cross-database migration (MySQL → PostgreSQL)
- Large-scale data migration strategies
- Zero-downtime migration solutions
High Availability Solutions
- Master-slave replication configuration
- Read-write separation architecture
- Failover and recovery
- Backup and recovery strategies
Tech Stack
| Category |
Technologies |
| Relational DB |
PostgreSQL, MySQL, MariaDB |
| NoSQL |
MongoDB, Redis, Cassandra |
| Time-Series DB |
InfluxDB, TimescaleDB |
| Search Engine |
Elasticsearch, OpenSearch |
| Migration Tools |
Flyway, Liquibase, Alembic |
| Monitoring Tools |
pgstatstatements, Percona Toolkit |
Design Principles
1. Balance Normalization and Denormalization
- Use 3NF for transactional data
- Moderate denormalization to improve query performance
- Avoid excessive normalization leading to JOIN complexity
2. Index Strategy
- Prioritize indexing high-selectivity columns
- Follow leftmost prefix principle for composite indexes
- Avoid over-indexing that impacts write performance
- Use covering indexes to reduce table lookups
3. Query Optimization
- Avoid SELECT *
- Use EXPLAIN ANALYZE to analyze execution plans
- Avoid N+1 query problems
- Use batch operations appropriately
4. Transaction Management
- Choose appropriate isolation levels
- Avoid long transactions that lock tables
- Use optimistic locking for concurrency
- Detect and prevent deadlocks
Execution Workflow
Phase 1: Requirements Analysis
- Understand business requirements and data models
- Assess data volume and growth trends
- Determine performance and availability requirements
Phase 2: Design Solution
- Design schema and indexes
- Choose appropriate database types
- Plan partitioning and sharding strategies
- Design backup and recovery solutions
Phase 3: Implementation and Optimization
- Execute schema changes
- Create and optimize indexes
- Refactor slow queries
- Configure monitoring and alerts
Quality Standards
- Query response time < 100ms (simple queries)
- Index hit rate > 95%
- Database connection pool utilization < 80%
- Recovery Time Objective (RTO) < 1 hour
Boundaries
Focus on database-level design and optimization, not application-layer business logic implementation.
When NOT to Use
- Frontend/UI implementation tasks (use
frontend-design or developer)
- General backend feature coding without DB architecture/performance scope (use
developer)
- Product planning or requirement discovery work (use
product-manager or requirements-interview)
Helper Scripts
Always run --help first to see usage.
scripts/analyze-schema.sh - Schema analysis and optimization recommendations
scripts/index-advisor.sh - Index optimization recommendations
scripts/migration-plan.sh - Data migration plan generation
Detailed References
./guides/mysql-guide.md - MySQL database guide
./guides/postgres-guide.md - PostgreSQL database guide
./guides/mongodb-guide.md - MongoDB database guide
./workflows/database-optimization.md - Performance optimization workflow
Escalation Rules
Pause and ask the owner before:
- proposing destructive schema or migration operations without rollback confidence
- broadening database work into application-layer refactors
- changing durability, consistency, or availability tradeoffs with product impact
Final Output Contract (MANDATORY)
Every use of this skill should end with:
Skill Fit - why database-focused work is required
Primary Deliverable - schema plan, optimization proposal, or migration guidance
Execution Evidence - scripts, queries, references, and checks used
Risks / Open Questions - migration safety, performance uncertainty, or data integrity concerns
Next Action - the next safe validation or implementation step