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psql

Join PostgreSQL fact and dim tables to retrieve measures with dimension labels.

Updated Aug 23, 2026
Search Tags:#postgresql#reporting#data-analysis#psql#data-model#sql-queries#fact-dim
One-click install
npx skills add https://github.com/josca42/varro --skill psql-josca42
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: psql
Source: https://github.com/josca42/varro/tree/main/.agents/skills/psql
Command: npx skills add https://github.com/josca42/varro --skill psql-josca42

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Query and interpret data from PostgreSQL fact and dim schemas, turning raw table data into meaningful insights.

Core Features & Use Cases

  • Dimensional joins: Retrieve dimension labels from dim.{table_id} for any fact query.
  • Fact data extraction: Access measures from fact.{table_id}, including indhold and tid, with optional filters.
  • Use Case: Generate a readable report mapping each measure to its description in the dim table.

Quick Start

Query the connected database to join fact and dim tables and return the measure values with their corresponding labels.

Frequently Asked Questions about psql

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

FAQPage Schema
How do I query PostgreSQL fact and dim schemas to map measures to dimension labels?▼

To join PostgreSQL fact and dim tables, you connect fact.{table_id} with dim.{dim_table} using a shared key column, retrieving measures like indhold and tid alongside their corresponding dimension labels to produce a readable result set.

What is the standard join pattern for extracting fact data and dimension labels in PostgreSQL?▼

The standard join pattern links the fact table to the dimension table via a key column. This maps raw measures from fact.{table_id} to their descriptive labels in dim.{dim_table} for meaningful reporting.

Can I apply filters when retrieving measures from fact tables in psql?▼

Yes, you can apply optional filters when retrieving measures. The approach supports straightforward lookups and joined queries across multiple tables, allowing you to scope the extracted measures and dimension labels.

Does this approach support joined queries across multiple fact and dimension tables?▼

Yes, the approach supports joined queries across multiple tables. It enforces the join pattern between fact and dim schemas using a key column, allowing you to map multiple measures to their descriptions in the dimension tables.

What is the best way to generate a readable report from PostgreSQL fact and dim tables?▼

The best way to generate a readable report is to join fact and dim tables, returning measure values with their corresponding labels from the dimension tables to turn raw data into meaningful insights.

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