location-system
Countries, states, cities, ResolveLocationAction, Location base model, LocationType enum, geocoding, and location-level Redis data.
Countries, states, cities, ResolveLocationAction, Location base model, LocationType enum, geocoding, and location-level Redis data.
Vercel storage expert guidance — Blob, Edge Config, and Marketplace storage (Neon Postgres, Upstash Redis). Use when choosing, configuring, or using data storage with Vercel applications.
Guide for implementing MongoDB - a document database platform with CRUD operations, aggregation pipelines, indexing, replication, sharding, search capabilities, and comprehensive security. Use when working with MongoDB databases, designing schemas, writing queries, optimizing performance, configuring deployments (Atlas/self-managed/Kubernetes), implementing security, or integrating with applications through 15+ official drivers. (project)
Modern native JavaScript (ES2022-ES2025) utility patterns that replace lodash
MongoDB with Mongoose ODM - schemas, models, queries, aggregation, indexes, TypeScript typing, connection management
Serverless Redis-compatible key-value store via Upstash REST API -- edge-compatible, automatic JSON serialization, TTL-based caching
Cloudflare Vectorize vector database for semantic search and RAG. Use for vector indexes, embeddings, similarity search, or encountering dimension mismatches, filter errors.
Implements efficient API pagination using offset, cursor, and keyset strategies for large datasets. Use when building paginated endpoints, implementing infinite scroll, or optimizing database queries for collections.
The 4 layers of caching (Memoization, Data Cache, Full Route, Router Cache).
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.
Upstash Redis patterns for caching and rate limiting.
Implement caching strategies using @delon/cache. Use this skill when adding memory cache, LocalStorage cache, SessionStorage cache, or cache interceptors for HTTP requests. Supports TTL-based expiration, cache invalidation, cache grouping, and persistent storage. Optimizes performance by reducing redundant API calls and database queries.
Recommend caching strategies and invalidation patterns. Use when a mid-level developer needs performance guidance.
Redis caching and queue patterns. Use when implementing caching, rate limiting, session storage, pub/sub, or background job queues with Redis.
Caching patterns for web applications, APIs, and data layers. Use this skill when implementing memoization, HTTP cache headers, Redis caching, CDN configuration, or in-memory caches. Trigger whenever code deals with Cache-Control headers, ETags, functools.lru_cache, React useMemo, TanStack Query cache, or any caching strategy. Also applies to cache invalidation, TTL policies, and cache-aside patterns.
Use when implementing Redis caching, cache invalidation, or distributed locking in Frappe. Prevents stale cache bugs, race conditions from missing locks, and memory bloat from unbounded cache keys. Covers frappe.cache(), @redis_cache decorator, cache.get_value/set_value, cache invalidation patterns, frappe.lock, TTL strategies. Keywords: cache, Redis, redis_cache, invalidation, locking, frappe.cache, get_value, set_value, TTL, distributed lock, data not refreshing, stale data, cache not clearing, Redis error, slow repeated queries..
Caching Strategies knowledge base. Provides caching patterns (Cache-Aside, Read-Through, Write-Through, Write-Behind), invalidation approaches, multi-level caching, and Redis data structures for caching audits and generation.
Qdrant vector database integration patterns with LangChain4j. Store embeddings, similarity search, and vector management for Java applications. Use when implementing vector-based retrieval for RAG systems, semantic search, or recommendation engines.
Use when implementing Apollo caching strategies including cache policies, optimistic UI, cache updates, and normalization.
Redis development best practices for caching, data structures, and high-performance key-value operations
Elasticsearch development best practices for indexing, querying, and search optimization
Generate and execute ES|QL (Elasticsearch Query Language) queries from natural language and visualize results with Vega-Lite charts. Translates user intent into valid ES|QL queries, executes them against Elasticsearch, and can render professional data visualizations (bar, line, scatter, heatmaps, small multiples, etc.). Use when the user wants to query Elasticsearch data, analyze logs, aggregate metrics, explore data, or create charts and dashboards from ES|QL results.