pg

Enhances AI coding tools with intelligent, version-aware PostgreSQL code generation, providing semantic search and best practice recommendations for database schemas and queries.

Enables semantic search in PostgreSQL by storing and efficiently querying vector embeddings using pgvector, supporting RAG, similarity search, and AI-powered content retrieval.

Provides AI coding agents with deep, version-aware PostgreSQL expertise through semantic search over the official manual and curated best-practice skills, helping generate dramatically better schemas with more constraints, indexes, and modern PG features.

Designs PostgreSQL tables with best practices, focusing on normalization, data types, constraints, indexing, and schema optimization strategies.

Configures and optimizes TimescaleDB hypertables for high-performance time-series, IoT, and metrics data storage with automatic partitioning and compression.

Converts identified PostgreSQL tables to TimescaleDB hypertables with optimal configuration, migration planning, and performance validation.

A hypertable candidate finder skill from pg-aiguide, providing AI-optimized PostgreSQL expertise with semantic search across official docs and Timescale extension guides.

A PostgreSQL skill for implementing hybrid search that combines BM25 keyword search (via pg_textsearch) with semantic vector search (via pgvector), using Reciprocal Rank Fusion (RRF) to merge results into a single ranked list for improved retrieval relevance.

Skill