MongoDB Aggregation Pipeline Optimization
General MongoDB aggregation pipeline optimization techniques including early filtering, index usage, array operators vs $unwind, $lookup optimization, and performance debugging. Use when writing aggregation queries for ANY MongoDB project, debugging slow pipelines, or analyzing query performance. For M32RIMM-specific patterns, use mongodb-m32rimm-patterns skill.
Azure Data Services
This skill should be used when the user asks about "Azure SQL", "SQL Database", "Cosmos DB", "Redis Cache", "database on Azure", "NoSQL", "document database", "caching", or mentions Azure database and data services. Provides best practices and MCP tool guidance for Azure data services.
databases
MongoDB and PostgreSQL database administration. Databases: MongoDB (document store, aggregation, Atlas), PostgreSQL (relational, SQL, psql). Capabilities: schema design, query optimization, indexing, migrations, replication, sharding, backup/restore, user management, performance analysis. Actions: design, query, optimize, migrate, backup, restore, index, shard databases. Keywords: MongoDB, PostgreSQL, SQL, NoSQL, BSON, aggregation pipeline, Atlas, psql, pgAdmin, schema design, index, query optimization, EXPLAIN, replication, sharding, backup, restore, migration, ORM, Prisma, Mongoose, connection pooling, transactions, ACID. Use when: designing database schemas, writing complex queries, optimizing query performance, creating indexes, performing migrations, setting up replication, implementing backup strategies, managing database permissions, troubleshooting slow queries.
database-design-patterns
Database schema design patterns and optimization strategies for relational and NoSQL databases. Use when designing database schemas, optimizing query performance, or implementing data persistence layers at scale.
database-patterns
Database design, optimization, and caching strategies for SQL, NoSQL, and Redis
nosql-databases
MongoDB, Redis, Cassandra, DynamoDB, and distributed database patterns for scalable applications
mongodb
MongoDB fundamentals including document model, CRUD operations, querying, indexing, and aggregation framework for NoSQL database applications.
database-design
Production-grade database design skill for SQL/NoSQL selection, schema design, indexing, and query optimization
databases
Master relational and NoSQL databases. Learn PostgreSQL, MySQL, MongoDB, Redis, and other technologies for data persistence, optimization, and scaling.
mongodb-schema-design
Master MongoDB schema design and data modeling patterns. Learn embedding vs referencing, relationships, normalization, and schema evolution. Use when designing databases, normalizing data, or optimizing queries.
mongodb-find-queries
Master MongoDB find queries with filters, projections, sorting, and pagination. Learn query operators, comparison, logical operators, and real-world query patterns. Use when retrieving data from MongoDB collections.
mongodb-crud-operations
Master MongoDB CRUD operations, document insertion, querying, updating, and deletion. Learn BSON format, ObjectId, data types, and basic operations. Use when working with documents, collections, and fundamental MongoDB operations.
mongodb-index-creation
Master MongoDB index creation and types. Learn single-field, compound, unique, text, geospatial, and TTL indexes. Optimize query performance dramatically with proper indexing.
mongodb-app-development
Master MongoDB integration in applications with Node.js, Python, and Java drivers. Learn connections, transactions, error handling, and best practices. Use when building applications with MongoDB.
mongodb-indexing-optimization
Master MongoDB indexing and query optimization. Learn index types, explain plans, performance tuning, and query analysis. Use when optimizing slow queries, analyzing performance, or designing indexes.
mongodb-aggregation-pipeline
Master MongoDB aggregation pipeline for complex data transformations. Learn pipeline stages, grouping, filtering, and data transformation. Use when analyzing data, creating reports, or transforming documents.
optimizing-queries
Analyzes and optimizes SQL/NoSQL queries for performance. Use when reviewing query performance, optimizing slow queries, analyzing EXPLAIN output, suggesting indexes, identifying N+1 problems, recommending query rewrites, or improving database access patterns. Supports PostgreSQL, MySQL, SQLite, MongoDB, Redis, DynamoDB, and Elasticsearch.
graph-database-expert
Expert in graph database design and development with deep knowledge of graph modeling, traversals, query optimization, and relationship patterns. Specializes in SurrealDB but applies generic graph database concepts. Use when designing graph schemas, optimizing graph queries, implementing complex relationships, or building graph-based applications.
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