「replication」の検索結果

Manages GKE storage, including PVCs, PersistentVolumes, Filestore, and GCS FUSE. Use when configuring GKE storage, creating PVCs, or setting up GCS FUSE on GKE. For diagnosing storage failures (volume attach/mount errors, disk performance/node storage pressure, or Cloud Storage FUSE OOM), use the gke-storage-troubleshooting skill instead. Don't use for database administration or replication strategies outside volume provisioning context.

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Use when configuring or troubleshooting a Salesforce B2C Commerce (SFCC) storefront — including Business Manager site creation, SFRA cartridge path setup, customer groups, search index rebuilding, and key quota limits. Trigger keywords: SFCC, Commerce Cloud, Business Manager, storefront, cartridge, SFRA, site preferences, replication. NOT for B2B Commerce on Lightning (WebStore, BuyerGroup) — use admin/b2b-commerce-store-setup. NOT for choosing SFCC vs B2B Commerce — use architect/b2b-vs-b2c-architecture.

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Ground GEODE design and evaluation work in Stanford CS329A Parts 2 and 5. Use for test-time compute, planning, multi-step reasoning, deep research, LATS, independent subplans, best-of-N, repair depth, measurement replication, verifier authority, and GEODE/Eco²/SIL/Crucible comparisons.

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Align, run, audit, and publish GEODE benchmark comparisons against Agent-World arXiv 2604.18292v1 across MCP-Mark, BFCL V4, and tau2-bench. Use for Agent-World or AgentWorld comparison requests, cross-suite agent benchmark design, Tau2 comparator/profile diagnosis, paired thin-runtime-versus-GEODE experiments, mean-accuracy@8 replication, or benchmark comparability and artifact reviews.

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Use when designing UI components, reviewing visual design, building design systems, or checking accessibility. Auto-activates on frontend design tasks and UI reviews.

日本語の概要は準備中です。原文の説明を表示しています。

Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.

日本語の概要は準備中です。原文の説明を表示しています。

Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experiments using ssizeRNA, PROPER, powsimR for scRNA-seq, and pilot-data dispersion estimation from DESeq2/edgeR. Covers the biological-versus-technical replication distinction (technical replicates do not add degrees of freedom for biological inference), replicate-number-versus-sequencing-depth budgeting, scRNA-seq sample-versus-cell allocation under a pseudobulk model, and the critique that "n=3" is a publication convention rather than a power calculation. Use when budgeting a sequencing experiment, writing the sample-size justification in a grant, estimating replicates from pilot data, allocating a fixed budget between samples and depth, or planning scRNA-seq cohort size. For clinical-trial sample size see clinical-biostatistics/power-and-sample-size; for the power-given-n direction see experimental-design/power-analysis.

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Extracts, filters, annotates, and exports differential expression results from DESeq2 or edgeR with proper handling of padj=NA (independent filtering, Cook's outliers, all-zero), multiple-testing correction choice (BH vs Storey q-value vs IHW vs lfsr), TREAT vs post-hoc fold-change filtering, p-value histogram diagnostics, gene annotation via org.db/biomaRt/mygene, GSEA preranked input, ORA background construction, replication reality (Schurch 2016 small-n result), and SABV/sex-stratified reporting. Use when extracting and interpreting DE results, troubleshooting padj=NA, choosing FDR method, preparing ranked lists for pathway analysis, annotating gene IDs, or comparing DESeq2 vs edgeR outputs.

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Normalize read-depth copy-ratio profiles and segment them into copy-number regions using circular binary segmentation (CBS, DNAcopy), hidden Markov models, HaarSeg, and fused-lasso methods. Covers GC-content, mappability, and replication-timing (wave-artifact) bias correction, panel-of-normals/PCA denoising, diploid-baseline centering, and algorithm selection by sequencing depth and event size. Use when choosing a segmentation algorithm, correcting depth bias, diagnosing oversegmentation or a mis-centered baseline, tuning CBS or HMM parameters, or understanding why a downstream CNV caller produced fragmented or shifted segments.

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Runs cis-pQTL Mendelian randomization for drug-target validation using UKB-PPP (Olink), deCODE (SomaScan), Fenland, INTERVAL, ARIC, and FinnGen-PPP proteomes plus colocalization triangulation, phenome-wide on-target adverse-effect scans, cross-platform Olink/SomaScan replication, and PAV (protein-altering variant) sensitivity. Use when nominating or de-risking a drug target from plasma-proteome GWAS, mimicking pharmacological inhibition via cis-pQTL instruments, separating shared-causal from LD-confounded signal under the Schmidt 2020 cis-MR framework, screening on-target adverse phenotypes pheWAS-style, or producing publication-grade STROBE-MR plus PP.H4 evidence for a target gene.

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Expert-level biochemistry knowledge. Use when working with proteins, enzymes, metabolism, DNA, RNA, carbohydrates, lipids, cell signaling, or bioenergetics. Also use when the user mentions 'enzyme kinetics', 'metabolism', 'glycolysis', 'Krebs cycle', 'DNA replication', 'protein folding', 'amino acids', 'ATP', 'oxidative phosphorylation', 'gene expression', 'lipid bilayer', or 'signal transduction'.

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Create a new PostgreSQL CDC capture from scratch using flowctl. Covers all Postgres variants (vanilla, Aurora, RDS, Cloud SQL, Supabase, Neon). Use when user says "capture Postgres", "stream from Postgres", "Postgres CDC", "set up replication slot", or "connect Postgres to Estuary".

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Use when working with Aws Efs — aWS EFS file system management and performance analysis. Covers file system inventory, mount targets, access points, throughput modes, lifecycle policies, replication configurations, and storage metrics. Use when inspecting EFS file systems, debugging mount issues, reviewing performance settings, or optimizing storage costs.

日本語の概要は準備中です。原文の説明を表示しています。

Use when working with Aerospike — aerospike cluster management, namespace utilization, set analysis, and secondary index monitoring. Covers node health, storage engine statistics, migration tracking, UDF management, and XDR cross-datacenter replication status. Read this skill before any Aerospike operations.

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Use when working with Apache Kudu — apache Kudu cluster management, tablet server health monitoring, table partition analysis, and replication diagnostics. Covers master health, tablet distribution, column encoding efficiency, compaction metrics, and maintenance window scheduling. Read this skill before any Kudu operations.

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Use when working with Azure Sql — azure SQL Database DTU/vCore analysis, query performance insights, elastic pool management, geo-replication status, and database health monitoring via Azure CLI.

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Use when working with Azure Blob Storage — azure Blob Storage container analysis, access tier management, lifecycle policies, replication configuration, and storage account health via Azure CLI.

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Use when working with Mariadb — mariaDB Galera cluster status, MaxScale routing, ColumnStore engine analysis, performance schema tuning, and replication health.

日本語の概要は準備中です。原文の説明を表示しています。

Use when working with Aws Elasticache Deep — aWS ElastiCache deep analysis for Redis and Memcached clusters, replication health, failover analysis, and performance metrics. Covers node-level metrics, memory utilization, cache hit rates, eviction tracking, connection analysis, and engine-specific diagnostics.

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Use when working with Mysql — mySQL and MariaDB database analysis, performance tuning, query optimization, and health monitoring. Covers slow query investigation, index analysis, table statistics, replication health, InnoDB status, connection pool analysis, and schema inspection.

日本語の概要は準備中です。原文の説明を表示しています。

Use when working with Vitess — vitess tablet health, VSchema management, resharding status, VReplication workflows, and cluster topology.

日本語の概要は準備中です。原文の説明を表示しています。

Expert en systèmes distribués (CAP theorem, consensus, partitions, vector clocks)

日本語の概要は準備中です。原文の説明を表示しています。

Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experiments using ssizeRNA, PROPER, powsimR for scRNA-seq, and pilot-data dispersion estimation from DESeq2/edgeR. Covers the biological-versus-technical replication distinction (technical replicates do not add degrees of freedom for biological inference), replicate-number-versus-sequencing-depth budgeting, scRNA-seq sample-versus-cell allocation under a pseudobulk model, and the critique that "n=3" is a publication convention rather than a power calculation. Use when budgeting a sequencing experiment, writing the sample-size justification in a grant, estimating replicates from pilot data, allocating a fixed budget between samples and depth, or planning scRNA-seq cohort size. For clinical-trial sample size see clinical-biostatistics/power-and-sample-size; for the power-given-n direction see experimental-design/power-analysis.

日本語の概要は準備中です。原文の説明を表示しています。

Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.

日本語の概要は準備中です。原文の説明を表示しています。