AI导哈

后端与数据库

postgres

timescale/pg-aiguide

Use this skill for any PostgreSQL database work — table design, indexing, data types, constraints, extensions (pgvector, PostGIS, TimescaleDB), search, and migrations. **Trigger when user asks to:** - Explore an existing PostgreSQL database to understand its objects and relationships - Design or modify PostgreSQL tables, schemas, or data models - Choose data types, constraints, indexes, or partitioning strategies - Work with pgvector embeddings, semantic search, or RAG - Set up full-text search, hybrid search, or BM25 ranking - Use PostGIS for spatial/geographic data - Set up TimescaleDB hypertables for time-series data - Migrate tables to hypertables or evaluate migration candidates - Plan or execute safe schema migrations with zero downtime **Keywords:** PostgreSQL, Postgres, SQL, schema, table design, indexes, constraints, pgvector, PostGIS, TimescaleDB, hypertable, semantic search, hybrid search, BM25, time-series, migration

  • 1394 次安装
  • 1900 星标
  • 收录于

安装命令

npx skills add https://github.com/timescale/pg-aiguide --skill postgres

SKILL.md

PostgreSQL Expert Skills

This skill provides comprehensive PostgreSQL expertise through specialized references. Load the appropriate reference based on the task.

Available References

Existing Databases

  • schema-exploration — Read-only, question-driven exploration of schemas, tables, views, routines, triggers, RLS, and extensions using pg_catalog. Use to understand an existing database before designing changes or writing queries.

Table Design

  • design-postgres-tables — Data types, constraints, indexes, JSONB patterns, partitioning, and PostgreSQL best practices. Use for any general table/schema design task.
  • design-postgis-tables — PostGIS spatial table design: geometry vs geography types, SRIDs, spatial indexing, and location-based query patterns. Use when the task involves geographic or spatial data.

Search

  • pgvector-semantic-search — Vector similarity search with pgvector: HNSW/IVFFlat indexes, halfvec storage, quantization, filtered search, and tuning. Use for embeddings, RAG, or semantic search.
  • postgres-hybrid-text-search — Hybrid search combining BM25 keyword search with pgvector semantic search using RRF. Use when combining keyword and meaning-based search.

相关 Skills

pgvector-semantic-searchtimescale/pg-aiguideUse this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.后端与数据库954design-postgres-tablestimescale/pg-aiguideUse this skill for general PostgreSQL table design. **Trigger when user asks to:** - Design PostgreSQL tables, schemas, or data models when creating new tables and when modifying existing ones. - Choose data types, constraints, or indexes for PostgreSQL - Create user tables, order tables, reference tables, or JSONB schemas - Understand PostgreSQL best practices for normalization, constraints, or indexing - Design update-heavy, upsert-heavy, or OLTP-style tables **Keywords:** PostgreSQL schema, table design, data types, PRIMARY KEY, FOREIGN KEY, indexes, B-tree, GIN, JSONB, constraints, normalization, identity columns, partitioning, row-level security Comprehensive reference covering data types, indexing strategies, constraints, JSONB patterns, partitioning, and PostgreSQL-specific best practices.后端与数据库626setup-timescaledb-hypertablestimescale/pg-aiguideUse this skill when creating database schemas or tables for Timescale, TimescaleDB, TigerData, or Tiger Cloud, especially for time-series, IoT, metrics, events, or log data. Use this to improve the performance of any insert-heavy table. **Trigger when user asks to:** - Create or design SQL schemas/tables AND Timescale/TimescaleDB/TigerData/Tiger Cloud is available - Set up hypertables, compression, retention policies, or continuous aggregates - Configure partition columns, segment_by, order_by, or chunk intervals - Optimize time-series database performance or storage - Create tables for sensors, metrics, telemetry, events, or transaction logs **Keywords:** CREATE TABLE, hypertable, Timescale, TimescaleDB, time-series, IoT, metrics, sensor data, compression policy, continuous aggregates, columnstore, retention policy, chunk interval, segment_by, order_by Step-by-step instructions for hypertable creation, column selection, compression policies, retention, continuous aggregates, and indexes.后端与数据库514