vector-index-tuning
Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
Azure Resource Manager SDK for Redis in .NET.
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
Redis expert for data structures, caching patterns, Lua scripting, and cluster operations
Elasticsearch expert for queries, mappings, aggregations, index management, and cluster operations
Covers Dagger Engine caching internals including cache key derivation, invalidation, and the immutable DAG model. Use when debugging cache misses, unexpected invalidations, or implementing caching-related engine features.
ABP MongoDB patterns - AbpMongoDbContext, IMongoCollection, MongoDbRepository, no migrations, embedded documents vs references, manual UpdateAsync required. Use when working in MongoDB projects or implementing MongoDB repositories in ABP.
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
Unify 6+ memory systems into AgentDB with HNSW indexing for 150x-12,500x search improvements. Implements ADR-006 (Unified Memory Service) and ADR-009 (Hybrid Memory Backend).
Check Meilisearch index status, tasks, health, and settings. Use for debugging search issues, monitoring indexing tasks, and inspecting index configuration. Read-only admin operations.
Inspect Redis cache keys, values, and TTLs for debugging. Supports both main cache and system cache. Use for debugging cache issues, checking cached values, and monitoring cache state. Read-only by default.
High-performance temporary storage system using Redis. Supports namespaced keys (mema:*), TTL management, and session context caching. Use for: (1) Saving agent state, (2) Caching API results, (3) Sharing data between sub-agents.
Creating typed collections with createCollection. Adapter selection: queryCollectionOptions (REST/TanStack Query), electricCollectionOptions (ElectricSQL real-time sync), powerSyncCollectionOptions (PowerSync SQLite), rxdbCollectionOptions (RxDB), trailbaseCollectionOptions (TrailBase), localOnlyCollectionOptions, localStorageCollectionOptions. CollectionConfig options: getKey, schema, sync, gcTime, autoIndex (default off), defaultIndexType, syncMode (eager/on-demand, plus progressive for Electric). StandardSchema validation with Zod/Valibot/ArkType. Collection lifecycle (idle/loading/ready/error). Adapter-specific sync patterns including Electric txid tracking, Query direct writes, and PowerSync query-driven sync with onLoad/onLoadSubset hooks.
Serverpod caching — local and Redis caches, cache keys, lifetime, CacheMissHandler. Use when caching data, optimizing queries, or working with session.caches.
Add new Room database persistence to Trail-Sense Android app. Use when the user asks to create, add, or implement database persistence for a model, including Entity, DAO, Repository, and AppDatabase migration. Covers entity-to-model mapping, index configuration, and standard CRUD operations.
Lightweight guidance for using the Redis Rate Limit TypeScript SDK, including setup steps, basic usage, and pointers to advanced algorithm, features, pricing, and traffic‑protection docs.
Guided implementation for adding a new database engine to TablePro. Pre-loaded with all integration points, file locations, patterns, and the complete checklist derived from Redis implementation experience. Use when asked to add support for a new database type (e.g., Cassandra, DynamoDB, ClickHouse).
Manage redis cache manager operations. Auto-activating skill for Backend Development. Triggers on: redis cache manager, redis cache manager Part of the Backend Development skill category. Use when working with redis cache manager functionality. Trigger with phrases like "redis cache manager", "redis manager", "redis".
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 Set and Map for O(1) membership lookups instead of array.includes(). Apply when checking membership repeatedly or performing frequent lookups against a collection.