Can this skill modify my MongoDB data?

No, this skill operates strictly in read-only mode to ensure data safety and integrity while exploring or querying your database.

How does this skill help with large datasets?

It provides specific patterns like the Bucket and Subset patterns to handle time-series data and prevent documents from exceeding the 16MB limit.

What is the ESR rule mentioned in the skill?

The ESR (Equality, Sort, Range) rule is a best practice for ordering fields in compound indexes to achieve maximum query performance.

Can it help write aggregation queries?

Yes, it provides optimized templates for $match, $project, $group, and $lookup stages to ensure efficient data processing without performance hits.

Does it support MongoDB Atlas?

Yes, it includes connection string best practices for MongoDB Atlas, local instances, and replica sets.

MongoDB Management & Optimization

Name: MongoDB Management & Optimization
Author: fcakyon

byfcakyon

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247

Gestión de Bases de Datos

Provides expert guidance on MongoDB schema design, indexing strategies, and aggregation pipeline optimization for high-performance database management.

Acerca de

This skill empowers Claude to assist with MongoDB database interactions, offering battle-tested patterns for data modeling, indexing, and complex aggregations. It helps developers choose between embedding and referencing, implement efficient indexing using the ESR rule, and construct performant aggregation pipelines while strictly adhering to read-only MCP constraints. Whether you are troubleshooting performance bottlenecks or exploring collection structures, this skill ensures best practices are followed to avoid common anti-patterns like unbounded arrays, massive documents, or missing indexes.

Características Principales

Performance-tuned aggregation pipeline construction and debugging
Optimized indexing strategies using the ESR (Equality, Sort, Range) rule
247 GitHub stars
Detailed schema design guidance including embedding vs. referencing patterns
Anti-pattern detection to prevent unbounded arrays and collection scans
Database exploration for collections, schemas, and existing indexes

Casos de Uso

Designing efficient data models for time-series data using the bucket pattern
Building and refining complex aggregation pipelines for data reporting and analysis
Analyzing existing MongoDB collection schemas and suggesting index improvements

Acerca de

Características Principales

Performance-tuned aggregation pipeline construction and debugging
Optimized indexing strategies using the ESR (Equality, Sort, Range) rule
247 GitHub stars
Detailed schema design guidance including embedding vs. referencing patterns
Anti-pattern detection to prevent unbounded arrays and collection scans
Database exploration for collections, schemas, and existing indexes

Casos de Uso

Designing efficient data models for time-series data using the bucket pattern
Building and refining complex aggregation pipelines for data reporting and analysis
Analyzing existing MongoDB collection schemas and suggesting index improvements