'caching' 검색 결과

Cost optimization patterns for LLM API usage — model routing by task complexity, budget tracking, retry logic, and prompt caching.

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Cache Playwright browser binaries in CI/CD pipelines (GitHub Actions, Azure DevOps) to avoid 1-2 minute download overhead on every build.

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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.

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Maintain injected credential authenticators, method-isolated caching, request-context propagation, and the pure-Go SQLite API-key plugin. Excludes interactive portal password authentication and identity-store provisioning.

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设计和实现缓存策略时使用。覆盖 Redis / Memcached / Caffeine（Java）/ NestJS Cache / Spring Cache / Django Cache。融合 5 种缓存模式 + 三大缓存问题 + 失效策略。

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Use when the user says 'why is my checkout broken', 'audit my woocommerce store', 'cart problems woocommerce', or 'losing sales woocommerce'. Diagnoses checkout and cart failures, AJAX mismatches, caching conflicts, payment gateway setup, and SSL enforcement.

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Use for APIs, servers and app databases. Triggers: REST, GraphQL, migration, query optimization, middleware, caching, queues, 백엔드, API 작업, 마이그레이션, 쿼리 최적화.

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Backend development patterns for services, error handling, logging, caching. Use when building backend services, APIs, or microservices.

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Analyzes web performance using Chrome DevTools MCP. Measures Core Web Vitals (LCP, INP, CLS) and supplementary metrics (FCP, TBT, Speed Index), identifies render-blocking resources, network dependency chains, layout shifts, caching issues, and accessibility gaps. Use when asked to audit, profile, debug, or optimize page load performance, Lighthouse scores, or site speed. Biases towards retrieval from current documentation over pre-trained knowledge.

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Molecular featurization hub with one consistent interface over 100+ featurizers. Fingerprints (ECFP/Morgan, MACCS, atom pair, topological torsion, Avalon, RDKit, ERG), RDKit and Mordred descriptor sets, pharmacophore and 3D shape descriptors, scaffold keys, and pretrained embeddings (ChemBERTa, ChemGPT, MolT5, GIN, Graphormer) through a common transformer API with caching and parallelism. Use this skill to convert SMILES into model-ready feature matrices for QSAR, virtual screening, and molecular ML, and to choose between featurizer families. Also trigger on molfeat, MoleculeTransformer, FPVecTransformer, PretrainedHFTransformer, molfeat model store, or featurizer selection.

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State management decision tree for React SPAs: useState/useReducer, Context API, Zustand, Jotai, Redux Toolkit, TanStack Query, SWR. Detect what's installed and apply matching patterns. Use this skill to: - Pick the right state tool for the data shape (local / shared UI / server / form). - Use Context correctly (and know when to switch to Zustand/Jotai). - Implement a Zustand or Jotai store. - Set up TanStack Query for server state with caching, pagination, mutations. - Avoid common mistakes (overusing Redux, prop drilling, forgetting query invalidation). Do NOT use this skill for: - General hook conventions (see react-conventions). - Form state specifically (see react-forms). - Routing state (see react-routing). - Testing stores/queries (see react-testing).

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React Server Components vs Client Components in Next.js: the boundary discipline, "use client" / "use server" directives, Server Actions, what serializes across the boundary, common pitfalls. Use this skill to: - Decide whether a component should be RSC or Client. - Push the "use client" boundary as deep as possible. - Implement Server Actions correctly (auth, validation, revalidation). - Compose RSC and Client components without breaking the model. - Pass data across the boundary safely. Do NOT use this skill for: - General Next.js conventions (see nextjs-conventions). - Specific data-fetching APIs and caching (see nextjs-data-fetching). - Routing (see nextjs-routing).

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Optimize Docker build times using layer caching, multi-stage builds, BuildKit features, and dependency-first copy patterns. Applicable to R, Node.js, and Python projects. Use when Docker builds are slow due to repeated package installations, when rebuilds reinstall all dependencies on every code change, when image sizes are unnecessarily large, or when CI/CD pipeline builds are a bottleneck.

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Profile and optimize Shiny application performance using profvis, bindCache, memoise, async/promises, debounce/throttle, and ExtendedTask for long-running computations. Use when the app feels slow or unresponsive during user interaction, when server resources are exhausted under concurrent load, when specific operations create bottlenecks, or when preparing an app for production deployment with many concurrent users.

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GraphQL expert including schema design, Apollo Client/Server, and caching

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Compress large context before reasoning to reduce token usage while preserving evidence. Use this whenever the user mentions huge files, long prompts, RAG payloads, prompt caching, expensive sessions, codebase context, chat history compaction, or wants the same answer quality with fewer tokens.

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Generate a GitHub Actions workflow for Android — JDK + Gradle caching, build, unit + instrumented tests, Android Lint, run the Android pre-flight checks, and (optionally) sign + upload a signed app bundle to a Play track. Use when setting up CI for an Android app.

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Generate a GitHub Actions workflow for iOS — macOS runner, SPM/DerivedData caching, build, test on a simulator matrix, run the iOS pre-flight checks, and (optionally) sign + upload to TestFlight. Use when setting up CI for an iOS app.

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Use when the user is deciding, reviewing, or second-guessing a software architecture or tech-stack choice — monolith vs microservices vs serverless vs event-driven, backend language, database type, caching, CQRS/event sourcing, polyglot, or a migration like Strangler Fig. Triggers on "should I use microservices", "which database", "tech stack for", "system design", or "architecture review". For how to deploy, operate, and scale that design (CI/CD, cloud, Kubernetes, IaC), use devops.

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Use when managing Cargo workspaces, feature flags, build scripts, CI caching, dependency auditing, or Cargo.lock with Rust.

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Turborepo monorepo build system guidance. Triggers on: turbo.json, task pipelines, dependsOn, caching, remote cache, the "turbo" CLI, --filter, --affected, CI optimization, environment variables, internal packages, monorepo structure/best practices, and boundaries. Use when user: configures tasks/workflows/pipelines, creates packages, sets up monorepo, shares code between apps, runs changed/affected packages, debugs cache, or has apps/packages directories.

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Rate limiting for serverless and edge apps with the @upstash/ratelimit TypeScript/JavaScript SDK backed by Upstash Redis. Use when adding a rate limiter or throttling to an API route, Next.js middleware, Vercel Edge, Cloudflare Workers, or any HTTP endpoint; returning 429 Too Many Requests; choosing between fixed window, sliding window, and token bucket algorithms; limiting per user, IP, API key, or tenant with prefixes and custom keys; protecting login, signup, form, or AI endpoints from abuse, bots, and brute force; using deny lists, ephemeral caching, analytics, timeouts, and multi-region rate limits; or estimating the Redis command cost of rate limiting. Also use when the user says rate limit, rate-limiting, throttle, quota, request limits, or traffic protection.

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Work with the @upstash/redis TypeScript/JavaScript SDK, a serverless HTTP-based Redis client for Next.js, Vercel, Cloudflare Workers, edge runtimes, and Node.js. Use when adding a cache (cache-aside, write-through, TTL and expiration strategies), session storage and user sessions, a key-value store, leaderboards and rankings with sorted sets, counters, distributed locks, queues with lists, streams and consumer groups, sparse index-addressed arrays and ring buffers (ARSET, ARINSERT, ARRING, ARGREP, AROP), embeddings and nearest-neighbour vector search stored inside Redis (VECTOR commands via redis.vector, separate from @upstash/vector), JSON documents, pipelines and MULTI/EXEC transactions, Lua scripting, read replicas, or full-text search, typo-tolerant search, facets, aggregations, and search over Redis stream entries with Upstash Redis Search (different from regular FT.SEARCH; also available for TCP clients via @upstash/search-redis and @upstash/search-ioredis). Also use when migrating from ioredis or node-redis, when a Redis connection is needed from a serverless function without connection pooling, when integrating @upstash/ratelimit, or when the user says Redis cache, KV store, session store, serverless Redis, or Upstash Redis. Also use when building AI agents on Redis with Upstash AgentKit (agent memory, chat history, RAG tools, tool caching, chat persistence, resumable streams, distributed locks) for the Vercel AI SDK, TanStack AI, or Vercel Eve. Supports automatic serialization/deserialization of JavaScript types.

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Work with any Upstash product, SDK, or tool, including serverless Redis (caching, sessions, leaderboards, key-value storage), Ratelimit (rate limiting and throttling), QStash (message queue, cron schedules, background jobs), Workflow (durable long-running functions), Vector (vector database for embeddings, semantic search, and RAG), Search (full-text and semantic search), Box (sandboxed cloud containers for AI agents, TypeScript/JavaScript and Python), Blob (serverless S3-compatible file storage with direct browser uploads), the Upstash CLI, and no-signup scratch Redis for agents. Use when the user mentions Upstash or needs any of these capabilities in a serverless, edge, or Node.js app.

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