Claude Skill
timescale/pg-aiguide
通过 PostgreSQL 专业知识增强 AI 编程工具。这款 MCP 服务器与 Claude Skill 提供文档和技能,以自动生成更优质、更准确的 PostgreSQL 代码。
概览
仓库信息
安装这个 Skill
npx skills add timescale/pg-aiguide --skill postgresRegistry 信息
npx skills add timescale/pg-aiguide --skill postgresnpx skills add timescale/pg-aiguide
项目简介
pg-aiguide 是一款 MCP 服务器与 Claude Skill,旨在通过提供专业的 Postgres 技能和文档访问,增强 AI 编程工具生成高质量 PostgreSQL 代码的能力。
MCP server and Claude plugin for Postgres skills and documentation. Helps AI coding tools generate better PostgreSQL code.
要点
- 用于 PostgreSQL 集成的 MCP 服务器
- 用于 AI 辅助编程的 Claude Skill
- 提供 Postgres 文档与技能访问
- 旨在提升 AI 生成的 SQL 代码质量
- 基于 Python 实现
使用场景
- 增强 AI 编程助手在 PostgreSQL 开发中的能力
- 为 AI 工具提供实时的 Postgres 文档
- 提高 AI 生成的数据库查询的准确性
- 将 Postgres 专业知识集成到 Claude 工作流中
- 支持开发者使用 AI 生成 SQL 代码
README 摘要
# pg-aiguide **AI-optimized PostgreSQL expertise for coding assistants** pg-aiguide helps AI coding tools write dramatically better PostgreSQL code. It provides: - **Semantic search** across the official PostgreSQL manual (version-aware) - **AI-optimized “skills”** — curated, opinionated Postgres best practices used automatically by AI agents - **Extension ecosystem docs**, starting with TimescaleDB, with more coming soon Use it as: - **Agent Skills** via `npx skills` — works with Claude Code, Cursor, Codex, Gemini CLI, and 40+ other agents - a **public MCP server** that can be used with any AI coding agent, or - a **Claude Code plugin** optimized for use with Claude's native skill support. ## ⭐ Why pg-aiguide? AI coding tools often generate Postgres code that is: - outdated - missing constraints and indexes - unaware of modern PG features - inconsistent with real-world best practices pg-aiguide fixes that by giving AI agents deep, versioned PostgreSQL knowledge and proven patterns. ### See the difference https://github.com/user-attachments/assets/5a426381-09b5-4635-9050-f55422253a3d <details> <summary>Video Transcript </summary> Prompt given to Claude Code: > Please describe the schema you would create for an e-commerce website two times, first with the tiger mcp server disabled, then with the tiger mcp server enabled. For each time, write the schema to its own file in the current working directory. Then compare the two files and let me know which approach generated the better schema, using both qualitative and quantitative reasons. For this example, only use standard Postgres. Result (summarized): - **4× more constraints** - **55% more indexes** (including partial/expression indexes) - **PG17-recommended patterns** - **Modern features** (`GENERATED ALW