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Found 705 Skills
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.
Integrate MercadoPago Checkout Pro (redirect-based) into Next.js applications with any PostgreSQL database (Supabase, AWS RDS, Neon, PlanetScale, self-hosted, Prisma, Drizzle, or raw pg). Use when the user needs to: (1) Add MercadoPago payment processing to a Next.js app, (2) Create a checkout flow with MercadoPago, (3) Set up payment webhooks for MercadoPago, (4) Build payment success/failure pages, (5) Create a shopping cart with payment integration, (6) Troubleshoot MercadoPago integration issues (auto_return errors, webhook failures, hydration mismatches, double submissions). Triggers on requests mentioning MercadoPago, Mercado Pago, payment integration with MP, Argentine/Latin American payment processing, or checkout with MercadoPago. Supports all MercadoPago currencies (ARS, BRL, MXN, CLP, COP, PEN, UYU).
Use when analyzing FileMaker DDR to extract calculations, custom functions, and business logic for PostgreSQL import processes or maintenance scripts - focuses on understanding and adapting FileMaker logic rather than direct schema migration
T-SQL query optimization techniques for SQL Server and Azure SQL Database. Use this skill when: (1) User needs to optimize slow queries, (2) User asks about SARGability or index seeks, (3) User needs help with query hints, (4) User has parameter sniffing issues, (5) User needs to understand execution plans, (6) User asks about statistics and cardinality estimation.