ck:databases

Design database schemas and queries for MongoDB and PostgreSQL.

Updated Apr 9, 2026
One-click install
npx skills add https://github.com/jangtrinh/demo-Lam --skill ck-databases-jangtrinh
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ck:databases
Source: https://github.com/jangtrinh/demo-Lam/tree/main/.claude/skills/databases
Command: npx skills add https://github.com/jangtrinh/demo-Lam --skill ck-databases-jangtrinh

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill reduces the time and guesswork of designing database schemas and writing correct queries across MongoDB and PostgreSQL by providing a structured workflow, naming conventions, and domain-specific reference material.

Core Features & Use Cases

  • Database schema design (OLTP/OLAP/ETL-ready): Produces maintainable table structures with consistent naming, keys, and index strategy.
  • Query authoring for SQL and MongoDB: Helps generate SQL queries (joins/CTEs/window functions) and MongoDB query/aggregation pipelines aligned to performance needs.
  • Performance + operational readiness: Supports index/performance tuning, incremental ETL design (watermarks and job logs), and operational concerns like backups, replication, and administration.

Quick Start

Use the ck:databases skill to design a transactional schema for an app, by asking: "Design the database schema for a 'blog' app with users, posts, and comments, including indexes and migrations strategy for PostgreSQL and MongoDB."

Frequently Asked Questions about ck:databases

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

FAQPage Schema
How do I design a database schema for both PostgreSQL and MongoDB?

To design database schemas across PostgreSQL and MongoDB, you must follow a strict requirements-gathering flow that produces maintainable table structures with consistent naming, keys, and index strategies for OLTP and OLAP models.

What is the best way to write safe SQL queries with complex joins and window functions?

Writing safe SQL queries with complex joins, CTEs, and window functions requires proposing query logic with clear reasoning and including safety guardrails like pausing before execution to ensure correctness and optimal performance.

How do I build a MongoDB aggregation pipeline for specific access patterns?

Building a MongoDB aggregation pipeline requires selecting appropriate indexes and constraints for your access patterns, proposing pipeline logic with reasoning, and applying safety guardrails before execution to ensure performance and correctness.

Can I use this approach for incremental ETL planning and watermarking?

Yes, incremental ETL design supports watermarks and job logs to manage data extraction efficiently. It addresses operational readiness including performance tuning, backups, and replication for your database administration needs.

Does database schema design work for both transactional and analytical modeling?

Yes, database schema design applies to both OLTP and OLAP modeling. It ensures correctness and performance by tailoring table structures, constraints, and indexes to your specific transactional or analytical access patterns.