postgresql

Translate natural language questions into SQL queries joining multiple data sources.

Updated Feb 17, 2026

Search Tags:#postgresql #data-analysis #jsonb #cross-source #sql-translation #notebook-export

Ready to run

npx skills add https://github.com/gnestor/claude-workflow-plugin --skill postgresql-gnestor

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: postgresql
Source: https://github.com/gnestor/claude-workflow-plugin/tree/main/.claude/skills/postgresql
Command: npx skills add https://github.com/gnestor/claude-workflow-plugin --skill postgresql-gnestor

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires postgres, @std/dotenv, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Data warehouse for cross-source queries. Use when joining data across multiple sources or when no dedicated API skill exists for the data. Supports JSON/JSONB introspection and Jupyter notebook export. Not for single-source queries where a dedicated API skill is available.

Core Features & Use Cases

Cross-source SQL translation and orchestration across PostgreSQL and JSON/JSONB data
JSON/JSONB introspection, schema caching, and JSON field extraction
Notebook export for advanced analysis and reproducibility

Quick Start

Run a cross-source analysis by prompting the agent to join data from multiple sources.

Frequently Asked Questions about postgresql

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I write SQL queries that join data across multiple sources in PostgreSQL?▼

Cross-source SQL translation converts natural language questions into SQL queries that join multiple data sources. It leverages cached schemas for multi-source tables and supports JSON/JSONB inspection to navigate complex columns.

How do I extract and introspect JSONB columns when querying a PostgreSQL data warehouse?▼

JSONB introspection analyzes JSON columns by caching schemas and extracting specific JSON fields. This enables natural language prompts to translate into SQL queries that navigate nested JSONB data structures across multiple sources.

Can I export PostgreSQL query results to a Jupyter notebook for further analysis?▼

Notebook export sends cross-source query results directly to Jupyter notebooks for advanced analysis and reproducibility. This allows you to continue exploring joined PostgreSQL and JSONB data programmatically after generation.

Do I need a dedicated API skill for single-source PostgreSQL queries?▼

You should not use cross-source SQL translation for single-source queries where a dedicated API skill exists. This tool is designed specifically for joining data across multiple sources or navigating JSONB columns when no dedicated API is available.

How are PostgreSQL queries kept safe when translating natural language to SQL?▼

Safe querying is enforced through SQL parameterization, result size limits, and comprehensive error handling. This prevents unsafe query execution while providing recovery guidance if cross-source joins or JSONB extraction fail.

What is the best way to analyze JSON data scattered across multiple data sources?▼

Cross-source data analytics uses cached schemas to orchestrate queries across multiple sources and introspect JSON/JSONB columns. It translates natural language questions into safe SQL joins and exports results to Jupyter notebooks for analysis.