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Found 303 Skills
Use these skills when you need to troubleshoot performance bottlenecks, analyze query execution plans, identify resource-heavy processes, and monitor system-level PromQL metrics.
Use these skills to set up and optimize production-ready vector workloads by simply expressing your intent and performance requirements.
Generate SQL queries from natural language descriptions. Supports BigQuery, PostgreSQL, MySQL, and other dialects. Reads database schemas from uploaded diagrams or documentation. Use when writing SQL, building data reports, exploring databases, or translating business questions into queries.
Universal SQL code review assistant that performs comprehensive security, maintainability, and code quality analysis across all SQL databases (MySQL, PostgreSQL, SQL Server, Oracle). Focuses on SQL injection prevention, access control, code standards, and anti-pattern detection. Complements SQL optimization prompt for complete development coverage.
Deploy and manage relational databases using RDS with Multi-AZ, read replicas, backups, and encryption. Use for PostgreSQL, MySQL, MariaDB, and Oracle.
Configures Neon Serverless Driver for Next.js, Vercel Edge Functions, AWS Lambda, and other serverless environments. Installs @neondatabase/serverless, sets up environment variables, and creates working API route examples with TypeScript types. Use when users need to connect their application to Neon, fetch or query data from a Neon database, integrate Neon with Next.js or serverless frameworks, or set up database access in edge/serverless environments where traditional PostgreSQL clients don't work.
Complete Java Spring Boot skill set for building enterprise applications. Includes modular architecture with optional components: - PostgreSQL database with JPA/Hibernate + Flyway migration - Redis caching (optional) - Kafka/RabbitMQ messaging (optional, choose one) - JWT + OAuth2 authentication (optional OAuth2) - RBAC authorization (optional) - TDD with Mockito - Spec-First Development with OpenSpec
Comprehensive guide for Go database access. Covers parameterized queries, struct scanning, NULLable column handling, error patterns, transactions, isolation levels, SELECT FOR UPDATE, connection pool, batch processing, context propagation, and migration tooling. Use this skill whenever writing, reviewing, or debugging Golang code that interacts with PostgreSQL, MariaDB, MySQL, or SQLite. Also triggers for database testing or any question about database/sql, sqlx, pgx, or SQL queries in Golang. This skill explicitly does NOT generate database schemas or migration SQL.
Build robust backend systems with modern technologies (Node.js, Python, Go, Rust), frameworks (NestJS, FastAPI, Django), databases (PostgreSQL, MongoDB, Redis), APIs (REST, GraphQL, gRPC), authentication (OAuth 2.1, JWT), testing strategies, security best practices (OWASP Top 10), performance optimization, scalability patterns (microservices, caching, sharding), DevOps practices (Docker, Kubernetes, CI/CD), and monitoring. Use when designing APIs, implementing authentication, optimizing database queries, setting up CI/CD pipelines, handling security vulnerabilities, building microservices, or developing production-ready backend systems.
Build robust backend systems with modern technologies (Node.js, Python, Go, Rust), frameworks (NestJS, FastAPI, Django), databases (PostgreSQL, MongoDB, Redis), APIs (REST, GraphQL, gRPC), authentication (OAuth 2.1, JWT), testing strategies, security best practices (OWASP Top 10), performance optimization, scalability patterns (microservices, caching, sharding), DevOps practices (Docker, Kubernetes, CI/CD), and monitoring. Use when designing APIs, implementing authentication, optimizing database queries, setting up CI/CD pipelines, handling security vulnerabilities, building microservices, or developing production-ready backend systems.
Guidelines for developing with Sequelize, a promise-based Node.js ORM supporting PostgreSQL, MySQL, MariaDB, SQLite, and SQL Server
Configure LangChain4J vector stores for RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, Neo4j), implementing embedding storage/retrieval, setting up hybrid search, or optimizing vector database performance for production AI applications.