'replication' 검색 결과

Best practices for building and maintaining applications with PowerSync: Cloud and self-hosted setup, sync configuration, client SDK usage, backend integration (Supabase, custom Postgres, MongoDB, Azure DocumentDB, MySQL, MSSQL), schema changes, watch and reactive queries (useQuery), attachments, the upload queue, and debugging. Use this skill whenever the user mentions PowerSync, a @powersync/* package (@powersync/web, @powersync/react-native, @powersync/node), the powersync Flutter/Dart package, the powersync Rust crate, wa-sqlite, offline-first sync, local-first architecture, sync rules, sync streams, uploadData, fetchCredentials, disconnectAndClear, logout or user switching in a synced app, real-time data replication, Electric Cloud migration, Electric Shapes, an app stuck on Syncing, data not syncing, or wants to add offline-capable sync to a mobile or web app, even if they do not explicitly name PowerSync.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyzes time-series and longitudinal RNA-seq for differential expression and trajectory structure. Covers DESeq2 LRT with reduced models, time as factor vs continuous vs natural splines, maSigPro (Nueda 2014 for RNA-seq), ImpulseDE2 with explicit impulse-model failure modes, DREAM for repeated measures via linear mixed models, pseudoreplication avoidance, conditional vs marginal modeling, and trajectory clustering with DPGP, Mfuzz (with Schwämmle 2010 fuzzifier estimation), and splines+k-means. Use when modeling time-course or longitudinal expression, choosing factor vs spline, handling repeated measures from the same subject, avoiding pseudoreplication, clustering temporal trajectories, or selecting between dedicated time-course tools and pairwise+LRT.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert-level distributed systems theory covering consistency models, consensus algorithms, fault tolerance, replication, distributed transactions, and the CAP theorem.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert-level molecular biology knowledge. Use when working with DNA replication, transcription, translation, gene regulation, cloning, PCR, sequencing, CRISPR, epigenetics, or molecular techniques. Also use when the user mentions 'DNA replication', 'transcription', 'translation', 'gene expression', 'PCR', 'cloning', 'restriction enzyme', 'gel electrophoresis', 'Western blot', 'CRISPR', 'epigenetics', 'promoter', 'enhancer', or 'RNA splicing'.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Detect DCSync attacks where adversaries abuse Active Directory replication privileges to extract password hashes by monitoring for non-domain-controller accounts requesting directory replication via DsGetNCChanges.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create a MySQL CDC capture using flowctl with binlog replication. Use when setting up streaming from MySQL, Amazon RDS MySQL, or Aurora MySQL. Use when user says "capture MySQL", "stream from MySQL", "MySQL CDC", "binlog replication", or "connect MySQL to Estuary".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert en bases de données (PostgreSQL, MySQL, MongoDB, Redis, query optimization, replication)

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Detect DCSync attacks where adversaries abuse Active Directory replication privileges to extract password hashes by monitoring for non-domain-controller accounts requesting directory replication via DsGetNCChanges.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Compare cell-type composition and spatial features across conditions in IMC/MIBI cohorts with the patient as the experimental unit, covering pseudoreplication, per-patient aggregation, mixed models, compositional (Dirichlet/scCODA) differential abundance, diffcyt, per-image-to-patient spatial differential testing (SpaceANOVA), batch covariates, and FDR. Use when testing whether a cell type or spatial niche differs between groups, avoiding cell-level pseudoreplication, choosing a differential-abundance method, or correctly powering an IMC cohort comparison.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyzes time-series and longitudinal RNA-seq for differential expression and trajectory structure. Covers DESeq2 LRT with reduced models, time as factor vs continuous vs natural splines, maSigPro (Nueda 2014 for RNA-seq), ImpulseDE2 with explicit impulse-model failure modes, DREAM for repeated measures via linear mixed models, pseudoreplication avoidance, conditional vs marginal modeling, and trajectory clustering with DPGP, Mfuzz (with Schwämmle 2010 fuzzifier estimation), and splines+k-means. Use when modeling time-course or longitudinal expression, choosing factor vs spline, handling repeated measures from the same subject, avoiding pseudoreplication, clustering temporal trajectories, or selecting between dedicated time-course tools and pairwise+LRT.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when publishing, unpublishing, replicating, or activating/deactivating content in AEM 6.5 LTS. Covers Quick Publish, Manage Publication, tree activation, bulk content activation, package replication, workflow-based approval, scheduled activation/deactivation (on/off time), dispatcher flush, and the programmatic Replication Java API for AEM publish instances.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Diagnose and fix common AEM 6.5 LTS replication issues including blocked queues, connectivity failures, and content distribution problems

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Orchestrates end-to-end replication workflows spanning multiple concerns: new environment setup, production incident response, and performance optimization for AEM 6.5 LTS.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

AEM as a Cloud Service content distribution and replication. Covers programmatic publishing using the Replication API and distribution event monitoring using Sling Distribution events.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Compare cell-type composition and spatial features across conditions in IMC/MIBI cohorts with the patient as the experimental unit, covering pseudoreplication, per-patient aggregation, mixed models, compositional (Dirichlet/scCODA) differential abundance, diffcyt, per-image-to-patient spatial differential testing (SpaceANOVA), batch covariates, and FDR. Use when testing whether a cell type or spatial niche differs between groups, avoiding cell-level pseudoreplication, choosing a differential-abundance method, or correctly powering an IMC cohort comparison.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyzes time-series and longitudinal RNA-seq for differential expression and trajectory structure. Covers DESeq2 LRT with reduced models, time as factor vs continuous vs natural splines, maSigPro (Nueda 2014 for RNA-seq), ImpulseDE2 with explicit impulse-model failure modes, DREAM for repeated measures via linear mixed models, pseudoreplication avoidance, conditional vs marginal modeling, and trajectory clustering with DPGP, Mfuzz (with Schwämmle 2010 fuzzifier estimation), and splines+k-means. Use when modeling time-course or longitudinal expression, choosing factor vs spline, handling repeated measures from the same subject, avoiding pseudoreplication, clustering temporal trajectories, or selecting between dedicated time-course tools and pairwise+LRT.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Detect DCSync attacks where adversaries abuse Active Directory replication privileges to extract password hashes by monitoring for non-domain-controller accounts requesting directory replication via DsGetNCChanges.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Diagnose and fix common AEM 6.5 LTS replication issues including blocked queues, connectivity failures, and content distribution problems

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Orchestrates end-to-end replication workflows spanning multiple concerns: new environment setup, production incident response, and performance optimization for AEM 6.5 LTS.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when publishing, unpublishing, replicating, or activating/deactivating content in AEM 6.5 LTS. Covers Quick Publish, Manage Publication, tree activation, bulk content activation, package replication, workflow-based approval, scheduled activation/deactivation (on/off time), dispatcher flush, and the programmatic Replication Java API for AEM publish instances.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Migrates legacy AEM (6.x, AMS, on-prem) to AEM as a Cloud Service using BPA CSV or cache, CAM/MCP target discovery, and a one-pattern-per-session workflow. Use for BPA/CAM findings, Cloud Service blockers, or fixes for scheduler, ResourceChangeListener, replication, EventListener, OSGi EventHandler, DAM AssetManager, HTL data-sly-test lint. OSGi configs → Cloud Manager — scan ui.config, .cfg.json, secrets, $[secret:]/$[env:] — agent follows references/osgi-cfg-json-cloud-manager.md when prompted. After BPA/CAM discovery, migration hands off each (pattern, file) pair to the code-assessment skill — code-assessment owns the five pattern guides (scheduler/, resource-change-listener/, replication/, event-migration/, asset-manager/) and the shared references for SCR→DS, ResourceResolver/SLF4J, HTL lint, and prerequisites.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

AEM as a Cloud Service content distribution and replication. Covers programmatic publishing using the Replication API and distribution event monitoring using Sling Distribution events.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

MySQL과 MariaDB의 테이블 설계와 느린 SQL을 검토합니다. 인덱스, 갱신 처리, 연결 관리, 복제 지연을 점검하고 구체적인 수정안과 검증 절차를 정리합니다.

• 테이블과 인덱스 설계
• 느린 SQL과 교착 상태 조사
• 대규모 운영 테이블 변경 검토

Implement and manage Raft, a method for keeping distributed servers in agreement. Covers leader election, log replication, node membership changes, and recovery after network disruptions to maintain consistent state across the cluster.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Implementing leader election across distributed nodes
• Keeping replicated logs consistent
• Adding or removing cluster nodes