'caching' 검색 결과

Design patterns and architectural guidance for .NET applications. Navigation skill covering vertical slices, domain modeling, messaging patterns, resilience, caching, and system design. For building scalable, maintainable applications. Keywords: architecture, patterns, vertical slices, domain modeling, messaging, resilience, caching, design patterns

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Implements Redis patterns for caching, sessions, rate limiting, pub/sub, and distributed locks with best practices. Use when users request "Redis caching", "session storage", "rate limiter", "pub/sub messaging", or "distributed locks".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

OpenClaw의 시작, 채팅, 화면 표시가 느린 원인을 작업 횟수와 실측값으로 조사합니다. 불필요한 처리를 줄이고 성능 개선 PR 작성과 검토를 지원합니다.

• 시작이나 채팅 응답 지연 조사
• 중복 SQL 실행과 파일 읽기 줄이기
• 실측값으로 성능 개선 PR 검토

REA의 분석 처리와 캐시 설계를 검토합니다. 결과의 정확성과 근거 접근성을 유지하면서 처리 시간, CPU, 메모리, 에이전트가 다루는 정보량을 개선합니다.

• 분석 처리가 느린 원인 조사
• 캐시 설계 검토
• CPU와 메모리 부하 절감

여러 EVM 체인에서 토큰 잔액과 가격의 자릿수 오류를 방지합니다. 실행 중 소수 자릿수를 조회하고 체인별로 캐시하며, 정밀 계산과 단위 통일을 지원합니다.

• 토큰 잔액의 자릿수 오류를 조사합니다
• 여러 EVM 체인의 토큰 수량을 비교합니다
• 브리지 이후 소수 자릿수를 확인합니다

Docker 컨테이너, 이미지, 저장 볼륨과 네트워크를 관리합니다. 여러 서비스 실행, 로그를 통한 장애 조사, 디스크 정리와 Dockerfile 개선에도 사용합니다.

• 컨테이너를 시작하고 중지합니다
• Docker Compose로 여러 서비스를 실행합니다
• 로그로 컨테이너 종료 원인을 조사합니다

Manage cached LLM responses, which are saved answers available for reuse. Inspect statistics and stored entries, clear cached data, and configure expiration policies and similarity thresholds that govern when answers can be reused.

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• Inspecting response cache statistics
• Clearing saved responses
• Adjusting cache expiration policies

Analyze application and system performance to find bottlenecks, the points that slow processing. Use resource usage and performance data to plan improvements to resource allocation, load balancing, caching, and algorithms, then compare results after changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating slow application processing
• Reviewing CPU and memory allocation
• Improving workload distribution across nodes

Analyze slow development workflows and agent coordination using execution times, resource use, dependencies, and communication patterns. Identify bottlenecks, prioritize causes, and build an improvement plan covering parallel work, caching, or resource allocation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate slow test execution
• Find unnecessary waits in task sequences
• Reduce communication delays between agents

Create and improve GitHub Actions workflows for automated testing and deployment. Coordinate multiple AI agents to review pull requests, investigate failed runs, manage releases, and analyze workflow performance and operating costs.

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• Creating workflows suited to a project's languages
• Improving test parallelization and caching
• Investigating failures and proposing fixes

Measure performance in the agentic-flow worker system, including trigger detection, agent selection, caching, and concurrent operations. Run a full suite or individual benchmarks to compare response times and resource usage against configured targets.

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• Checking overall worker system performance
• Measuring trigger detection and agent selection speed
• Examining cache behavior and memory usage

Create a custom offline page and configure a service worker, a browser background script, to cache it in advance. Show it when page navigation fails, and review resource dependencies and fallback conditions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add an offline page to a PWA
• Guide users during connection loss
• Review offline page caching

Implement caching and data conversion for syncable entities in Twenty's server code. Build lookup maps, convert stored records and inputs into flat representations, and resolve references to universal identifiers for later validation and processing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a cache service for a new syncable entity
• Convert stored entities into flat data
• Transform creation and update inputs

Measure Python execution time and memory use to locate slow or resource-heavy code. Use profiling results to guide improvements in data structures, caching, input and output operations, algorithms, and parallel processing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding slow functions in Python applications
• Reducing memory use and investigating leaks
• Improving data processing and database queries

Choose and implement ways to manage data in React apps, from values held by individual components to shared data and cached server responses. Covers Redux Toolkit, Zustand, Jotai, and React Query, including migration from legacy Redux.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing a library for shared application data
• Managing fetched data and caching
• Migrating legacy Redux code to Redux Toolkit

Choose embedding models, which represent text as numerical vectors, for meaning-based search and RAG applications. Compare options for code, prose, and multilingual content, and plan document splitting, preprocessing, caching, and dimension reduction to improve retrieval quality.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing an embedding model for RAG
• Reviewing document chunk size and boundaries
• Planning code or specialist document search

Improve Bazel builds in large repositories containing multiple projects. Get guidance on build configuration, shared remote caches, remote execution, dependency management, custom build rules, and diagnosing build problems.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Set up Bazel for a monorepo
• Configure remote caching and execution
• Review slow build configurations

Create multi-stage Dockerfiles that separate building an application from running it. Guides image selection, caching, file inclusion, permissions, and health checks to produce smaller container images with only the dependencies needed at runtime.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Separating build and runtime stages
• Reducing files and packages in the final image
• Reordering layers to reuse the build cache

Audit GitHub Actions workflows and run history to identify wasted automated checks and runner time. Get up to three evidence-based improvements aimed at reducing runtime or cost while preserving required validation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate long GitHub Actions runs
• Review CI costs and unnecessary workflow runs
• Improve caching and cancellation of overlapping runs

Design or review distributed systems, where multiple computers work together, using 42 cloud design patterns. Organizes options for reliability, performance, messaging, security, and deployment, emphasizing why to choose each pattern and the trade-offs involved.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review a distributed system architecture
• Choose patterns for failures and recovery
• Compare caching, scaling, and load management approaches

Choose SGLang Diffusion settings for a specific model, GPU, and memory budget. Compare generation speed, memory use, and output quality to select command-line options, including CPU offloading, multiple GPUs, caching, and reduced precision.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing generation settings for a specific model and GPU
• Reducing GPU memory use to fit a memory budget
• Comparing performance settings across multiple GPUs

Design a database layer from business requirements, access patterns, and expected growth. Compare database technologies, define tables and relationships, and plan indexes, caching, scaling, migrations, backups, and recovery with clear reasons and trade-offs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing a database for a new service
• Designing tables, relationships, and indexes
• Planning database migrations and rollback procedures

Investigate how transformer language models produce predictions with TransformerLens. This skill guides inspection of intermediate values and attention patterns, plus experiments that replace internal values to test their effects and identify components involved in model behavior.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inspecting attention patterns and intermediate values
• Testing output changes by replacing internal activations
• Analyzing attention head contributions to predictions