Can it handle complex validation logic?

Absolutely. It supports built-in Mongoose validators, custom validation functions, regex matching for strings, and pre-save hooks for conditional validation.

Does it support soft deletes?

Yes, the skill includes implementation patterns for 'deletedAt' timestamps and query middleware that automatically filters out deleted records from standard 'find' operations.

When should I choose embedding over referencing in MongoDB?

You should embed data when it is small, bounded, and almost always accessed alongside the parent document (one-to-few). Use referencing when the related data is queried independently or grows indefinitely (one-to-many).

How does this skill improve query performance?

It implements strategic indexing to avoid collection scans and suggests patterns like .lean() for read-only operations, which reduces memory overhead by returning plain JavaScript objects instead of Mongoose documents.

MongoDB Schema Architect

Name: MongoDB Schema Architect
Author: chavangorakh1999

bychavangorakh1999

0•

データベース管理

Generates optimized Mongoose schemas with robust validation, efficient indexing strategies, and performance-tuned data relationships.

This skill empowers Claude to design professional-grade MongoDB data models using Mongoose for Node.js applications. It provides expert guidance on choosing between embedded and referenced documents, implements complex validation rules, and configures advanced indexing strategies like TTL, text, and partial indexes. By incorporating performance best practices like the use of .lean() and strategic population, this skill ensures your MERN stack data layer is scalable, maintainable, and highly performant from the start.

主な機能

010 GitHub stars

02Robust schema validation using Mongoose built-ins, custom validators, and regex

03Advanced indexing configurations including compound, sparse, partial, and TTL indexes

04Performance optimization patterns including .lean() and efficient population

05Intelligent embedding vs. referencing decision logic based on data access patterns

06Pre-save and query middleware for soft deletes and automated data processing

ユースケース

01Designing scalable user authentication and role-based access control systems

02Modeling complex relational data for e-commerce or content management platforms

03Implementing temporary data storage for session tokens or verification codes using TTL

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add chavangorakh1999/sde-skills mongodb-schema

For use in Claude.ai and ChatGPT

主な機能

010 GitHub stars

02Robust schema validation using Mongoose built-ins, custom validators, and regex

03Advanced indexing configurations including compound, sparse, partial, and TTL indexes

04Performance optimization patterns including .lean() and efficient population

05Intelligent embedding vs. referencing decision logic based on data access patterns

06Pre-save and query middleware for soft deletes and automated data processing

ユースケース

01Designing scalable user authentication and role-based access control systems

02Modeling complex relational data for e-commerce or content management platforms

03Implementing temporary data storage for session tokens or verification codes using TTL

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add chavangorakh1999/sde-skills mongodb-schema

For use in Claude.ai and ChatGPT