Agent skill · data analytics · samhvw8
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.
Why this skill is useful
Provides specific commands and best practices for MongoDB and PostgreSQL that the AI wouldn't reliably generate on its own.
What it needs
Requires mongodb, postgresql installed locally. Requires mongodb account access. About 4k tokens when loaded. Last updated 2025-12-09. 10 stars on the source repository.
What this skill does
Databases Skill Unified guide for working with MongoDB (document-oriented) and PostgreSQL (relational) databases. Choose the right database for your use case and master both systems. When to Use This Skill Use when: Designing database schemas and data models Writing queries (SQL or MongoDB query language) Building aggregation pipelines or complex joins Optimizing indexes and query performance Implementing database migrations Setting up replication, sharding, or clustering Configuring backups and disaster recovery Managing database users and permissions Analyzing slow queries and performance issues Administering production database deployments Database Selection Guide Choose MongoDB When: Schema flexibility: frequent structure changes, heterogeneous data Document-centric: natural JSON/BSON data model Horizontal scaling: need to shard across multiple servers High write throughput: IoT, logging, real-time analytics Nested/hierarchical data: embedded documents preferred Rapid prototyping: schema evolution without migrations Best for: Content management, catalogs, IoT time series, real-time analytics, mobile apps, user profiles Choose PostgreSQL When: Strong consistency: ACID transactions critical Complex relationships: many-to-many joins, referential integrity SQL requirement: team expertise, reporting tools, BI systems Data integrity: strict schema validation, constraints Mature ecosystem: extensive tooling, extensions Complex queries: window functions, CTEs, analytical workloads Best for: Financial systems, e-commerce transactions, ERP, CRM, data warehousing, analytics Both Support: JSON/JSONB storage and querying Full-text search capabilities Geospatial queries and indexing Replication and high availability ACID transactions (MongoDB 4.0+) Strong security features Quick Start MongoDB Setup PostgreSQL Setup Common Operations Create/Insert Read/Query Update Delete Indexing Reference Navigation MongoDB References mongodb-crud.md - CRUD operations, query operators, atomi …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills samhvw8/databases