cosmosdb-datamodeling
Specialized guide for NoSQL data modeling (Adapted for MongoDB).
Specialized guide for NoSQL data modeling (Adapted for MongoDB).
Guide for using genies_cache dual-backend caching service. Use when implementing Redis or in-memory caching, managing TTL and atomic operations, or integrating caching into Genies microservices.
Redis performance optimization and best practices. Use this skill when working with Redis data structures, Redis Query Engine (RQE), vector search with RedisVL, semantic caching with LangCache, or optimizing Redis performance.
Episodic Memory Archiver. Stores full conversation transcripts with embeddings and analysis into ArangoDB. Tracks UNRESOLVED sessions for reflection with structured failure episodes (trigger/diagnosis/action/outcome), K~4 similar failure retrieval, user behavioral profiling, and federated taxonomy classification.
Build globally distributed applications with Azure Cosmos DB. Configure multi-region writes, consistency levels, partitioning, and change feed. Use for NoSQL databases, real-time analytics, and globally distributed data on Azure.
Comprehensive database architecture agent that provides expert guidance on database design, schema optimization, query performance tuning, and migration strategies. Covers relational and NoSQL databases, indexing strategies, normalization, data modeling, and scaling patterns. Use for database design, performance optimization, migration planning, or data architecture decisions.
Best practices dùng Elasticsearch 8+: index design, mappings, search queries, aggregations, relevance tuning và Node.js client patterns.
Database maintenance and health checks. Use when user needs migration safety, data integrity, index optimization, or says "check database", "optimize DB", "migration rollback", "data consistency", "database health".
Database integration patterns for SQL and NoSQL databases. Use when working with database schemas, queries, migrations, or ORM configurations. Supports PostgreSQL, BigQuery, MongoDB, MySQL with progressive disclosure.
Database integration patterns for PostgreSQL and MongoDB, connection pooling, query patterns, transactions, repositories, ORM setup (Prisma, TypeORM). Use when setting up database connections, writing database queries, implementing repositories, managing migrations, or handling transactions.
Standards Prisma et base de données. Use when "prisma", "database", "schema", "migration", "query".
Supabase + PostgreSQL database specialist. Use when designing schemas, writing migrations, implementing RLS policies, optimizing queries, or debugging database issues. Includes Row Level Security patterns, indexing strategies, and Supabase-specific features like Auth integration, Storage, and Edge Functions.
Write secure, performant, and optimized database queries using parameterized queries, eager loading, proper indexing, and transaction management. Use this skill when writing database queries in controllers, repositories, services, or model methods, when using query builders or ORM methods, when implementing filtering/sorting/pagination logic, when optimizing N+1 query problems with eager loading, when working with joins and complex queries, when implementing query caching, or when wrapping related operations in database transactions.
Use CloudBase document database Web SDK to query, create, update, and delete data. Supports complex queries, pagination, aggregation, and geolocation queries.
Query the database, run a query, look up data, search the database, or check data. Use when the user wants to query the database, run a SQL query, look up data, find data, search for records, check the database, or ask questions about data. Executes queries via CLI commands using natural language. Reads schema context from docs/DB.md. Supports MySQL, PostgreSQL, MongoDB, Elasticsearch, Redis, and BigQuery.
Schema design, migrations, queries, and indexing strategies. Use when designing database schemas, writing migrations, or optimizing queries.
Analyze, document, map, or scan the database schema. Use when the user wants to analyze the database, document the database, generate schema docs, map the database, create DB documentation, or inspect the database structure. Generates a docs/DB.md file with complete database schema documentation. Auto-detects language/framework. Supports MySQL, PostgreSQL, MongoDB, Elasticsearch, Redis, and BigQuery.
Design vector database ingestion and retrieval pipelines (points + payloads, filtered similarity search, multi-stage hybrid retrieval, index maintenance). Use when building RAG/vector search flows or debugging retrieval quality; triggers: vector database, RAG, embeddings, hybrid search, filtered search, Qdrant, Weaviate, Chroma.
Use when working with Qdrant vector database for semantic search and RAG. Covers collection setup, embedding generation, vector upsert/search, HNSW indexing, filtering, and integration with OpenAI embeddings for textbook content retrieval.