Provide PostgreSQL development guidance for Bun.sql projects.
npx skills add https://github.com/lammesen/skills --skill postgresql-expert
Please help me install this Agent Skill. Skill: postgresql-expert Source: https://github.com/lammesen/skills/tree/main/.claude/skills/postgresql-expert Command: npx skills add https://github.com/lammesen/skills --skill postgresql-expert
This Skill provides expert PostgreSQL guidance using Bun's native SQL client, covering patterns for queries, schema design, JSON/JSONB operations, full-text search, indexing, PL/pgSQL, pgvector, and performance optimization. It helps developers build scalable, maintainable database access in Bun-powered apps.
Start by connecting Bun.sql to your PostgreSQL database and applying the patterns to build a solid data layer, then iterate with small, safe migrations.
High-intent search queries and answers about installing and using this skill.
Query performance optimization with Bun.sql involves indexing strategies, query analysis, and schema design patterns. Apply selective indexing on frequently filtered columns, use EXPLAIN to identify bottlenecks, and leverage Bun.sql's tagged template queries for efficient execution. Pattern selection depends on your workload: read-heavy queries benefit from covering indexes, while write-heavy operations require careful index maintenance.
JSONB operations in PostgreSQL with Bun.sql combine native operators with Bun.sql's type-safe query interface. Store structured data as JSONB for indexing and query support, use containment and path operators for filtering, and leverage GIN indexes for performance. This approach handles semi-structured data without schema migration while maintaining query efficiency.
Yes, pgvector integrates with Bun.sql to enable vector search and similarity queries on embedding data. Configure pgvector extension, store vectors as native PostgreSQL columns, and query using distance operators through Bun.sql's tagged templates. This pattern supports semantic search, recommendation systems, and similarity-based filtering at scale.
Connection setup and transaction handling with Bun.sql requires configuring Bun's native SQL client with your PostgreSQL database endpoint. Manage transactions using explicit BEGIN/COMMIT patterns in Bun.sql queries, implement error handling for rollback scenarios, and configure connection parameters for your application's concurrency needs.
Schema design for Bun.sql emphasizes normalized structures with strategic denormalization, proper indexing, and JSONB for flexible attributes. Combine relational tables for transactional integrity with JSONB columns for semi-structured data, use appropriate constraints and foreign keys, and plan indexes aligned with your query patterns to balance write performance and query efficiency.
Full-text search in PostgreSQL with Bun.sql uses tsvector columns and GIN indexes for efficient text queries. Configure search configuration, index text columns as tsvectors, and execute search queries through Bun.sql templates. This pattern enables fast substring and phrase matching without external search services.
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