Skill UI

Guides the user through configuring and tuning database connection pools across languages, from selecting pool sizes to setting timeouts and monitoring usage to boost application performance.

Provides a structured workflow to analyze and improve application performance across frontend, backend, and database layers, including profiling, bottleneck identification, optimization tactics, and validation steps.

Analyzes SQL statements, identifies bottlenecks, suggests indexes and rewrites, and leverages the sql-query-optimizer plugin so you can improve database responsiveness when queries run slowly.

Automates load, stress, spike, and endurance tests through the performance-test-suite plugin, collecting response time, throughput, and error-rate metrics to spot CPU/memory/database/network bottlenecks and deliver graphs plus optimization guidance.

Fully managed, auto-scaling vector database tailored for production AI workloads such as RAG, recommendations, and semantic search, offering hybrid dense+sparse search, namespaces, metadata filters, and <100ms latency on serverless infrastructure.

A comprehensive guide maintained by Supabase for optimizing Postgres performance. It provides critical rules and best practices across query performance, schema design, connection management, and security (including RLS). Use this skill when writing, reviewing, or optimizing complex SQL queries, database schemas, or configurations to ensure optimal database health and performance.

This skill provides comprehensive expertise for senior database administrators to optimize and manage PostgreSQL databases at an advanced level. Use it for deep query analysis via EXPLAIN ANALYZE, implementing advanced features like JSONB indexing, configuring streaming or logical replication, and maintaining database health through VACUUM tuning and monitoring pg_stat views. Ideal for building highly available, high-performance, and resilient system architectures.

PostgreSQL database optimization workflow for query tuning, indexing strategies, performance analysis, and production database management.

An expert assistant providing deep knowledge and best practices for Prisma ORM. It covers the entire lifecycle of database interaction, including optimal schema design, relation modeling, safe migration workflows, and advanced query optimization techniques (addressing N+1 issues and implementing raw queries) across PostgreSQL, MySQL, and SQLite.

This skill demonstrates the multi-model pattern for creating robust and clean API contracts using Pydantic. It guides the creation of separate models for creation requests (Create), updates (Update), responses (Response), and database documents (InDB), ensuring type safety and clear data validation for Python backends. It also covers best practices like handling camelCase aliases and optional fields.

An end-to-end workflow for building robust, production-ready Python backend APIs. This guide covers all stages, including project scaffolding, SQLAlchemy database integration, Pydantic validation, implementing JWT/OAuth2 authentication, comprehensive error handling, unit testing, and Docker deployment for modern microservices.

Qdrant is a high-performance, Rust-powered vector database designed for production-grade RAG (Retrieval-Augmented Generation) and semantic search applications. It provides fast nearest neighbor search, supports complex hybrid search using metadata filtering, and offers horizontal scalability through sharding and replication. Ideal for building real-time recommendation systems and enterprise-level knowledge retrieval.