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Found 428 Skills
Use when you need to generate, validate, or deploy stored procedures for PostgreSQL, MySQL, or SQL Server. Creates database functions, triggers, and procedures with proper error handling and transaction management. Trigger with phrases like "generate stored procedure", "create database function", "write SQL procedure", "add trigger to table", or "create CRUD procedures".
Edit the Prisma Next data contract — add models, fields, relations, indexes, enums, type aliases, polymorphic types (`@@discriminator` / `@@base`), use extension namespaces (`pgvector.Vector(...)`, `cipherstash.EncryptedString(...)`), wire `prisma-next.config.ts` with `defineConfig` from the `@prisma-next/<target>/config` façade, and run `prisma-next contract emit`. Use for schema, models, fields, attributes, soft delete, paranoid, scopes, validations, callbacks, prisma schema, PSL, contract.prisma, contract.ts, contract.json, contract.d.ts, façade imports, `@prisma-next/postgres/config`, `@prisma-next/postgres/contract-builder`, `@prisma-next/postgres/control`, `@prisma-next/mongo/config`, `@prisma-next/mongo/contract-builder`, `extensions:`, `extensionPacks`, pgvector, cipherstash, postgis, paradedb, PN-CLI-4002, PN-CLI-4003, PN-CLI-4011.
Complete guide for using drift database library in Dart applications (CLI, server-side, non-Flutter). Use when building Dart apps that need local SQLite database storage or PostgreSQL connection with type-safe queries, reactive streams, migrations, and efficient CRUD operations. Includes setup with sqlite3 package, PostgreSQL support with drift_postgres, connection pooling, and server-side patterns.
Supabase open-source Firebase alternative with Postgres, authentication, storage, and realtime subscriptions. Use when building full-stack applications requiring integrated backend services with Next.js, React, or Vue.
Golang backend architecture expert. Use when designing Go services with Gin, implementing layered architecture, configuring sqlc with PostgreSQL/Supabase, or building API authentication.
Clean and format SQL migrations for Supabase - idempotency, RLS policies, formatting, schema fixes. Use when: fix this SQL, clean migration, RLS policy, Supabase schema, format postgres, prepare for SQL Editor, idempotent migration.
Grafana Cloud Database Observability — query-level performance insights for MySQL and PostgreSQL. Covers setup with Grafana Alloy, query samples, visual explain plans, RED metrics, pg_stat_statements and Performance Schema integration, and correlation with application traces. Use when monitoring database performance, diagnosing slow queries, setting up database observability for MySQL or PostgreSQL (self-managed, RDS, Aurora, Azure, Cloud SQL), or correlating DB metrics with APM data.
Wire the Prisma Next runtime — `db.ts` setup using `postgres<Contract>(...)` from `@prisma-next/postgres/runtime`, middleware composition (telemetry from `@prisma-next/middleware-telemetry`; lints and budgets), `DATABASE_URL` config, per-environment branching, switching between Postgres and Mongo façades. Use for db.ts, postgres(), mongo(), middleware, telemetry, lints, budgets, DATABASE_URL, .env, connection pool, poolOptions, dev vs prod config, transactions, db.transaction, read replicas, multi-database, script won't exit, hangs, close connection, db.end, db.close, pool.end, [Symbol.asyncDispose], await using.
List and test exposed PostgreSQL RPC functions for security issues and potential RLS bypass.
Эксперт DB replication. Используй для настройки репликации MySQL, PostgreSQL, MongoDB, failover и high availability.
Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.
Python full-stack with FastAPI, React, PostgreSQL, and Docker.