caching-strategy
Use when implementing caching - Redis, CDN, HTTP cache headers, application-level memoization, or cache invalidation patterns
Use when implementing caching - Redis, CDN, HTTP cache headers, application-level memoization, or cache invalidation patterns
Build MongoDB aggregation pipelines — $match, $group, $lookup, $project, index optimization. Triggers: 'aggregation', 'pipeline', 'complex query', '$group', '$lookup', 'reporting query'.
Design MongoDB schemas — Mongoose models with validation, indexes, relationships. Triggers: 'schema', 'model', 'data model', 'collection design', 'Mongoose schema'.
Comprehensive reference documentation for Redis Docs.
Guides Firestore document modeling, index strategy, transactional patterns, and JVM integration for scalable backend systems. Use when: building document-oriented services, read projections, and low-latency event-driven views on GCP.
Spring Cache + Redis caching patterns for Kotlin. Use when implementing caching strategies, cache invalidation, TTL management, or Redis integration in multi-module projects.
Navigate Solid Pod containers — follow LDP structure, read .meta sidecars, use affordances to discover content
**Spring Cache Skill**: Production-grade caching with Redis and Spring Cache abstraction for Java 21 + Spring Boot 3.x. Covers @Cacheable, @CachePut, @CacheEvict, multi-level caching, cache-aside pattern, TTL configuration, cache warming, conditional caching, distributed cache with Lettuce, cache serialization, key generation, monitoring with Micrometer, and cache stampede prevention. MANDATORY TRIGGERS: @Cacheable, @CachePut, @CacheEvict, @Caching, @EnableCaching, CacheManager, RedisCacheManager, CacheConfiguration, RedisTemplate, Spring Cache, Redis, Lettuce, cache, TTL, cache eviction, cache hit, cache miss, cache invalidation, cache stampede, dogpile, multi-level cache, L1 cache, L2 cache, Caffeine, local cache, distributed cache, cache-aside, read-through, write-through, cache warming, preload, cache key, KeyGenerator, cache serialization, Jackson2JsonRedisSerializer, cache monitoring, cache metrics.
Mongoose/MongoDB patterns for schema design, queries, indexes, aggregations. Use when working with MongoDB through Mongoose.
MongoDB data access layer patterns. Use when creating database services, collections, queries, indexes, or any MongoDB interaction.
Scans the current Go project and identifies the Service → Module → Resource hierarchy for tenant-manager registration. Detects service name and type, modules (via WithModule or component structure), resources per module (PostgreSQL, MongoDB, RabbitMQ), and database names for each resource. Redis is excluded (managed via key prefixing). Produces a visual HTML report for human decision-making. Additionally detects MongoDB index definitions (both in-code EnsureIndexes and scripts/mongodb/*.js files), generates index creation scripts for any gaps, and uploads them to S3 for use during dedicated tenant database provisioning. Also detects database names per module, cross-references them across modules to find shared databases, and flags them for single-provision in tenant-manager.
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.
Add or update a key-value pair in the memory persistence system. Performs upsert - updates existing keys or adds new ones. Requires memory.md to exist.
Use parse_embedding_from_db when reading embedding columns from Supabase. Supabase/PostgREST returns pgvector as string; normalize to list of floats before sending to Elasticsearch or other consumers.
Guides caching strategy selection and implementation across the full stack including HTTP caching, application-level caching (Redis, in-memory), frontend data caching (SWR, TanStack Query), LLM response caching (prompt caching, semantic caching), database query caching, cache invalidation patterns, and distributed cache architectures. Covers cache-aside, read-through, write-through, write-behind patterns, eviction policies (LRU/LFU), and agentic workflow caching considerations. Use when adding caching to an application, choosing a caching strategy, debugging stale data, optimizing API response times, reducing LLM costs, or designing distributed cache topologies. Triggers: cache, caching, Redis, CDN, TTL, cache invalidation, stale data, Cache-Control, ETag, SWR, TanStack Query, prompt caching, semantic cache, LRU, write-through, cache-aside, materialized view.
Guides caching strategy selection and implementation across the full stack including HTTP caching, application-level caching (Redis, in-memory), frontend data caching (SWR, TanStack Query), LLM response caching (prompt caching, semantic caching), database query caching, cache invalidation patterns, and distributed cache architectures. Covers cache-aside, read-through, write-through, write-behind patterns, eviction policies (LRU/LFU), and agentic workflow caching considerations. Use when adding caching to an application, choosing a caching strategy, debugging stale data, optimizing API response times, reducing LLM costs, or designing distributed cache topologies. Triggers: cache, caching, Redis, CDN, TTL, cache invalidation, stale data, Cache-Control, ETag, SWR, TanStack Query, prompt caching, semantic cache, LRU, write-through, cache-aside, materialized view.
Generate Redis key patterns and data structures. Use when designing Redis architecture.
Padrões de Database layer: Entities, Migrations, Repositories, Kysely types, JSONB.
Set up Everything for read-only inventory via ES CLI or HTTP server. Use when integrating Everything, configuring search providers, or setting up fast file indexing. Supports ES CLI (recommended), HTTP Server, and SDK with automatic fallback.
Redis expert. Caching strategies, session storage, rate limiting, pub/sub. Use for caching implementation and Redis configuration.