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ck:databases

Design database schemas and write optimized SQL and MongoDB queries.

Updated Aug 27, 2026
Search Tags:#performance#indexing#postgresql#migrations#sql#mongodb#schema-design
One-click install
npx skills add https://github.com/nam3886/golang-echo-boilerplate --skill ck-databases-nam3886
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ck:databases
Source: https://github.com/nam3886/golang-echo-boilerplate/tree/main/.claude/skills/databases
Command: npx skills add https://github.com/nam3886/golang-echo-boilerplate --skill ck-databases-nam3886

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Helps teams and engineers design robust database schemas, write efficient SQL and MongoDB queries, and manage migrations, backups, and performance tuning so applications run reliably at scale.

Core Features & Use Cases

  • Schema Design: OLTP and OLAP patterns, naming conventions, normalization vs denormalization, fact/dimension modeling.
  • Queries & Aggregations: SQL (CTEs, window functions, joins) and MongoDB aggregation pipelines with optimization suggestions.
  • Migrations & Backups: Generate migrations, apply/rollback logic, and backup/restore utilities for MongoDB and PostgreSQL.
  • Performance & Indexing: EXPLAIN analysis, index recommendations, sequential-scan detection, and ETL watermark patterns.
  • Operational Guidance: Replication, PITR, psql/mongosh tips, and cloud-specific rules (BigQuery, Atlas, D1).

Quick Start

Ask the databases skill to design a normalized PostgreSQL schema with DDL and index recommendations for orders and order_items.

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 normalized PostgreSQL schema with DDL and index recommendations?▼

PostgreSQL schema design generates normalized DDL proposals with naming conventions and index recommendations to optimize queries. You request table structures like orders and order_items, receiving fact/dimension models and performance tuning rules.

How do I optimize slow MongoDB aggregation pipelines and SQL queries?▼

Query tuning analyzes slow SQL and MongoDB aggregation pipelines using EXPLAIN plans to detect sequential scans. It provides optimization suggestions, index recommendations, and pipeline restructuring to improve query performance.

Can I use this to generate database migrations and backup utilities for PostgreSQL and MongoDB?▼

Migration generation creates apply and rollback logic for PostgreSQL and MongoDB schema changes. Backup and restore utilities are compatible with psql and mongodump/mongorestore, ensuring reliable database state management.

Does this support both OLTP and OLAP schema design patterns?▼

Schema design covers OLTP and OLAP patterns, including normalization versus denormalization and fact/dimension modeling. This supports transactional workloads and analytical processing for PostgreSQL and MongoDB databases.

How do I configure replication and point-in-time recovery for PostgreSQL?▼

Operational guidance provides PostgreSQL replication and PITR configuration instructions. It includes psql and mongosh tips, ensuring reliable backups and recovery setups across managed cloud environments like Atlas.

What is the best way to analyze EXPLAIN plans and detect sequential scans in PostgreSQL?▼

EXPLAIN analysis parses PostgreSQL query plans to identify sequential scans and missing indexes. It recommends specific index structures and query rewrites to reduce latency and improve execution efficiency.

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