Redis expert for caching, pub/sub, data structures, and distributed systems patternsUse when "redis, caching strategy, cache invalidation, pub/sub, rate limiting, distributed lock, session storage, leaderboard, message queue, upstash, redis, caching, pub-sub, session, rate-limiting, distributed-lock, upstash, elasticache, memorystore" mentioned.
Analyze a Redis dataset and recommend an optimal RediSearch index schema
Use when you need to work with database indexing. This skill provides index design and optimization with comprehensive guidance and automation. Trigger with phrases like "create indexes", "optimize indexes", or "improve query performance".
Implement intelligent API response caching with Redis, Memcached, and CDN integration. Use when optimizing API performance with caching. Trigger with phrases like "add caching", "optimize API performance", or "implement cache layer".
Use when you need to implement multi-tier caching to improve database performance. This skill sets up Redis, in-memory caching, and CDN layers to reduce database load. Trigger with phrases like "implement database caching", "add Redis cache layer", "improve query performance with caching", or "reduce database load".
Query relationships in SpiceDB/Kessel via the RBAC relations API
Implements caching solutions using Redis patterns (cache-aside, write-through, write-behind), Memcached, cache invalidation strategies, TTL design, cache stampede prevention, CDN configuration, and distributed caching. Use when optimizing read performance, reducing database load, designing cache layers, or configuring CDN rules.
Master SurrealDB 2.3.x with Python for multi-model database operations including CRUD, graph relationships, vector search, and real-time queries. Use when working with SurrealDB databases, implementing graph traversal, semantic search with embeddings, or building RAG applications.
Create and manage Dexie/IndexedDB repositories with type-safe interfaces, converters, and standardized CRUD operations. Use when (1) adding entity storage, (2) implementing save/load/delete operations, (3) designing database schema and indexes, (4) converting between database (Db*) and domain types, (5) handling database errors or migrations, (6) using existing repositories (SettingsRepository, WorkoutsRepository, TemplatesRepository, CustomExercisesRepository, BenchmarksRepository, ActiveWorkoutRepository). Triggers include "database", "repository", "save data", "fetch from database", "delete from storage", "database schema", "database table", "indexes", "migration", "persist", "convert workout", "converter", "buildPartialUpdate", "mock repository", "database error", "bulk operations", "import/export", or specific repository names.
Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin). Use when designing database schemas, writing queries and aggregations, optimizing indexes for performance, performing database migrations, configuring replication and sharding, implementing backup and restore strategies, managing database users and permissions, analyzing query performance, or administering production databases.
Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin). Use when designing database schemas, writing queries and aggregations, optimizing indexes for performance, performing database migrations, configuring replication and sharding, implementing backup and restore strategies, managing database users and permissions, analyzing query performance, or administering production databases.
MdsSignal for fetching MDSplus tree data — constructor args, location formats, TDI expressions, dims, and DIII-D efit01 signals via FDP/Pelican
Add a new entity to the global search (CMD+K) system. Updates backend search controller with permission-gated search and frontend search config with entity type, icon, and colors.
Weaviate vector database for semantic search, hybrid queries, and AI-native applications. Use for embeddings storage, similarity search, RAG pipelines, and multi-modal retrieval.
Evaluate complex text criteria on each item in a Collection and return a new Collection of matching items. Use to identify relevant items in a Collection
sqlite-vec extension for vector similarity search in SQLite. Use when storing embeddings, performing KNN queries, or building semantic search features. Triggers on sqlite-vec, vec0, MATCH, vec_distance, partition key, float[N], int8[N], bit[N], serialize_float32, serialize_int8, vec_f32, vec_int8, vec_bit, vec_normalize, vec_quantize_binary, distance_metric, metadata columns, auxiliary columns.
Write secure and performant database queries using ORMs or raw SQL. Use this skill when writing database queries, implementing data access logic, or optimizing query performance. Apply when working on files like repositories/*.ts, queries/*.ts, services/*.ts with database operations, or any code that interacts with the database. Essential for preventing SQL injection with parameterized queries, avoiding N+1 query problems with eager loading, selecting only needed columns, using transactions for related operations, implementing query timeouts, and caching expensive queries.
