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Found 705 Skills
Reviews PostgreSQL code for indexing strategies, JSONB operations, connection pooling, and transaction safety. Use when reviewing SQL queries, database schemas, JSONB usage, or connection management.
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.
For writing and executing SQL queries - from simple single-table queries to complex multi-table JOINs and aggregations
Security best practices for web applications. Use when handling user input, authentication, or sensitive data. Covers XSS, SQL injection, CSRF, environment variables, and secure coding patterns.
Database schema design for PostgreSQL/MySQL with normalization, relationships, constraints. Use for new databases, schema reviews, migrations, or encountering missing PKs/FKs, wrong data types, premature denormalization, EAV anti-pattern.
SQL Server index design and optimization strategies. Use this skill when: (1) User needs help designing indexes, (2) User asks about clustered vs nonclustered indexes, (3) User wants to optimize columnstore indexes, (4) User needs filtered or covering indexes, (5) User asks about index maintenance and fragmentation.
FastAPI with PostgreSQL, async SQLAlchemy 2.0, Alembic, and Docker.
Guidelines for developing with Kysely, a type-safe TypeScript SQL query builder with autocompletion support
PostgreSQL database helper. Use when writing SQL queries, exploring schema, or working with the database.
Query Apple Health SQLite database for vitals, activity, sleep, and workouts. Supports Markdown, JSON, and FHIR R4 output formats. This skill should be used when analyzing health metrics, generating health reports, answering questions about fitness or sleep patterns, or exporting health data in standard formats.
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.
PostgreSQL + Redis database design patterns. Use for data modeling, indexing, caching strategies. Covers JSONB, tiered storage, cache consistency.