Design and optimize MongoDB and PostgreSQL schemas, queries, and migrations.
npx skills add https://github.com/hidonguyen/trade-ops --skill ck-databases-hidonguyen
Please help me install this Agent Skill. Skill: ck:databases Source: https://github.com/hidonguyen/trade-ops/tree/main/.opencode/skills/databases Command: npx skills add https://github.com/hidonguyen/trade-ops --skill ck-databases-hidonguyen
Designing reliable schemas, writing efficient queries, and operating production-grade MongoDB and PostgreSQL databases is complex and error-prone; this Skill streamlines schema design, migrations, indexing, query optimization, backups, and performance troubleshooting so teams can ship data-driven features faster and with fewer regressions.
Use this skill to design a PostgreSQL OLTP schema for orders, propose indexes, and produce migration SQL plus a rollback plan.
High-intent search queries and answers about installing and using this skill.
To optimize PostgreSQL queries, you analyze execution plans using EXPLAIN, detect sequential scans, and resolve bottlenecks by recommending targeted indexes like GIN, BRIN, or GiST based on query patterns and pg_stat_statements data.
Designing MongoDB aggregation pipelines involves structuring lookups, array handling, and stage operations efficiently. You apply specific MongoDB index patterns to support pipeline operations and ensure queries avoid collection scans for optimal performance.
Generating database migrations involves producing DDL or migration files for schema changes. You apply these migrations safely while recording applied states, allowing you to execute rollback procedures to revert schema changes for both PostgreSQL and MongoDB.
Creating PostgreSQL backups involves generating backup files and defining restore workflows. You establish retention and cleanup strategies, verify backup integrity, and configure Point-in-Time Recovery using WAL to ensure reliable data restoration.
Yes, you can convert ad-hoc reporting workloads into star-schema fact tables by applying OLAP schema design. This process models fact and dimension tables, adds necessary indexes, and provides ETL watermarks to ensure dashboards meet SLA.
Yes, it handles both OLTP and OLAP schema modeling. For OLTP, it focuses on naming conventions and surrogate keys, while OLAP modeling involves fact and dimension table design with Slowly Changing Dimension strategies for analytics.
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