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ck:databases

Design scalable MongoDB and PostgreSQL schemas with normalization, indexing, and migrations.

Updated Aug 23, 2026
Search Tags:#analytics#indexing#postgresql#migrations#mongodb#database-design#sql-nosql
One-click install
npx skills add https://github.com/johngrandson/orkestry --skill ck-databases-johngrandson
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ck:databases
Source: https://github.com/johngrandson/orkestry/tree/main/.claude/skills/databases
Command: npx skills add https://github.com/johngrandson/orkestry --skill ck-databases-johngrandson

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Design and optimize database schemas across document and relational models to improve data quality, performance, and maintainability.

Core Features & Use Cases

  • OLTP/OLAP guidance: normalization, denormalization patterns, indexing strategies, and migration practices for MongoDB and PostgreSQL.
  • Reference patterns: design fact/dimension tables for analytics, transactional schemas, and cross-database considerations.
  • Use Case: architect a new product catalog with MongoDB for flexible docs and PostgreSQL for structured reports, including migrations and performance checks.

Quick Start

Architect a sample schema for a new e-commerce feature in both MongoDB and PostgreSQL and implement a small migration plan.

Frequently Asked Questions about ck:databases

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

FAQPage Schema
How do I design scalable schemas for both MongoDB and PostgreSQL?▼

Designing scalable schemas across MongoDB and PostgreSQL involves applying cross-database normalization, denormalization patterns, and indexing strategies tailored for OLTP and OLAP workflows to ensure performance and maintainability.

What's the best way to structure fact and dimension tables for analytics?▼

Structuring fact and dimension tables for analytics requires following specific reference patterns for transactional schemas, ensuring your data model supports efficient queries and reporting across your structured database environment.

How do I plan database migrations for SQL and NoSQL environments?▼

Planning database migrations for SQL and NoSQL environments involves applying structured migration practices that maintain data integrity while transitioning schemas between document and relational models.

When should I use MongoDB vs PostgreSQL for product catalog design?▼

Use MongoDB for flexible document storage and PostgreSQL for structured reports when designing product catalogs, leveraging cross-database considerations to optimize both transactional and analytical query performance.

What indexing strategies work best for OLTP and OLAP workflows?▼

Effective indexing strategies for OLTP and OLAP workflows depend on applying targeted normalization and denormalization patterns across your relational and document databases to optimize query speed and data retrieval.

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