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Found 412 Skills
Apply when improving VTEX IO Node or .NET services for latency, throughput, and resilience: in-process LRU, VBase, stale-while-revalidate, AppSettings loading, request context, parallel client calls, and avoiding duplicate work. Covers application-level performance patterns that complement edge/CDN caching. Use when optimizing backends beyond route-level Cache-Control.
Use Riverpod family providers to pass parameters and cache per parameter; FutureProvider.family, NotifierProvider.family, autoDispose with family, overriding in tests. Use when fetching data by ID, pagination, or any provider that depends on a parameter. Use this skill when the user asks about family, provider parameters, or caching by ID.
Use when writing or editing a system prompt for any LLM API or SDK (any code passing a `system=` / `system` role parameter, or a `.txt`/`.md` file holding such a prompt). Applies prompt-engineering and prompt-caching best practices.
Redis client and connection guidance covering connection pooling, multiplexing, pipelining, client-side caching with RESP3, avoiding slow commands (KEYS, SMEMBERS, HGETALL), and tuning socket timeouts. Use when configuring a Redis client (redis-py, Jedis, Lettuce, NRedisStack), batching commands for throughput, eliminating per-request connection creation, iterating large keyspaces with SCAN, enabling client-side caching for read-heavy workloads, or setting connect and read timeouts.
HTTP Networking with Dio, Retry & Caching Patterns
Implement caching strategies for HTTP, service workers, and memoization
Use this skill when designing distributed systems, architecting scalable services, preparing for system design interviews, or making infrastructure decisions. Triggers on load balancing, CAP theorem, sharding, replication, caching strategies, message queues, microservices architecture, database selection, rate limiting, and any task requiring high-level system architecture decisions.
Production-grade YouTube transcript extraction with multi-format output, language selection, auto-generated support, intelligent caching, rate limiting, and retry logic. Supports SRT, VTT, JSON, CSV, TSV, and plain text.
Work with the Upstash Redis TypeScript/JavaScript SDK for serverless Redis operations. Use for caching, session storage, rate limiting, leaderboards, full-text search (querying, filtering, aggregating) with Upstash Redis Search (different from regular FT.SEARCH), and all Redis data structures. Supports automatic serialization/deserialization of JavaScript types. Upstash Redis Search also available via @upstash/search-redis and @upstash/search-ioredis adapters for TCP clients.
Core Redis modeling guidance — choose the right data structure (String, Hash, List, Set, Sorted Set, JSON, Stream, Vector Set) and use consistent colon-separated key names. Use when designing a Redis data model, caching objects, deciding between Hash and JSON, building counters, leaderboards, membership sets, or session stores, or when reviewing/cleaning up Redis key naming.
Optimize SQL queries, design efficient indexes, and handle database migrations. Solves N+1 problems, slow queries, and implements caching. Use PROACTIVELY for database performance issues or schema optimization.
Redis LangCache guidance for semantic caching of LLM responses on Redis Cloud — calling search/set via the SDK or REST API, tuning the similarity threshold, separating caches per task type, and filtering with custom attributes. Use when caching LLM completions or RAG answers to cut API cost and latency, building a cache-aside layer in front of OpenAI / Anthropic / etc., tuning hit rate vs precision, or splitting one app's LLM workloads into multiple LangCache caches.