ck:databases

Name: ck:databases
Availability: InStock
Author: EdgeHunt

Design and query MongoDB and PostgreSQL schemas for OLTP and analytics.

Updated Apr 2, 2026

Search Tags:#indexing #postgresql #sql #nosql #mongodb #databases #schema-design

Ready to run

npx skills add https://github.com/EdgeHunt/EdgeHunt --skill ck-databases-edgehunt

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ck:databases
Source: https://github.com/EdgeHunt/EdgeHunt/tree/main/.claude/skills/databases
Command: npx skills add https://github.com/EdgeHunt/EdgeHunt --skill ck-databases-edgehunt

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pymongo, psycopg2, and includes scripts (resource) and references (resource) components.

What problem does it solve?

The Databases Skill provides a unified framework for designing robust schemas and crafting effective SQL and NoSQL queries across MongoDB and PostgreSQL, helping you optimize data structures, migrations, and performance.

Core Features & Use Cases

Schema design guidance for OLTP/OLAP, including normalization and denormalization trade-offs
Query crafting and optimization across SQL and MongoDB, including joins, aggregations, indexes, and migrations
Use cases: migrating between relational and document models, building analytics-ready schemas, and tuning performance for large-scale deployments

Quick Start

Design databases and write queries across MongoDB and PostgreSQL to meet transactional and analytical needs.

Frequently Asked Questions about ck:databases

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

FAQPage Schema

How do I design database schemas for both PostgreSQL and MongoDB?▼

Schema design applies normalization and denormalization trade-offs to optimize PostgreSQL relational models and MongoDB document models for OLTP and analytics workloads. It provides specific guidelines for both transactional and analytical requirements.

What's the best way to optimize SQL and NoSQL queries for large-scale deployments?▼

Query optimization for SQL and NoSQL involves crafting efficient joins, aggregation pipelines, and indexing strategies to tune performance. It supports cross-database workflows to handle large-scale data retrieval and processing effectively.

How do I migrate data between relational and document models?▼

Migrating between relational and document models requires mapping PostgreSQL schemas to MongoDB document structures and vice versa. The skill handles cross-database workflows and schema transformations to ensure data integrity during the transition.

Does this database skill support incremental ETL and analytics-ready schemas?▼

Incremental ETL and analytics-ready schemas are supported through dedicated guidelines in analytics.md and incremental-etl.md. It helps structure data specifically for OLAP querying and analytical processing alongside standard transactional operations.

When should I use indexing versus aggregation pipelines for MongoDB performance tuning?▼

Indexing improves MongoDB query speed by optimizing document lookups, while aggregation pipelines process and transform data across multiple stages. The skill provides best practices to apply both techniques appropriately for specific query patterns.

Can I use psycopg2 and pymongo for cross-database workflows?▼

Cross-database workflows utilize psycopg2 for PostgreSQL and pymongo for MongoDB connections to execute concurrent SQL and NoSQL operations. This allows simultaneous relational and document database interactions within a single integrated environment.