392 件 ・ 関連度順
概要と使いどころ
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.
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 Apache Geode — apache Geode distributed cache management, region inspection, member health monitoring, and WAN gateway analysis. Covers locator status, server groups, partition distribution, persistent disk store health, and continuous query diagnostics. Read this skill before any Geode operations.
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 Apache Hbase — apache HBase cluster management, region server health monitoring, table analysis, and compaction management. Covers RegionServer load balancing, HDFS integration health, WAL status, namespace inspection, and coprocessor diagnostics. Read this skill before any HBase operations.
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 Airflow — apache Airflow workflow orchestration management. Covers DAG management, task instance monitoring, executor health, pool status, variable management, connection audit, and scheduler diagnostics. Use when checking DAG run status, investigating task failures, managing Airflow resources, or auditing configurations.
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 Gcp Cloud Deploy — google Cloud Deploy delivery pipeline management, release tracking, rollout status monitoring, approval workflows, and deployment diagnostics via gcloud CLI.
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 Gcp Cloud Run — google Cloud Run service management, revision traffic splitting, scaling configuration, concurrency tuning, and container diagnostics via gcloud CLI.
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 Gcp Cloud Functions — google Cloud Functions management, execution analysis, scaling configuration, event trigger inspection, and runtime diagnostics via gcloud CLI.
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 Gcp Firestore Deep — google Cloud Firestore document and collection analysis, index management, security rules review, usage monitoring, and performance diagnostics via gcloud CLI.
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 Azure Logic Apps — azure Logic Apps workflow run analysis, trigger history, connector management, error diagnostics, and workflow definition inspection via Azure CLI.
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 Gcp Alloydb — google AlloyDB cluster management, instance analysis, query insights, maintenance window configuration, and performance diagnostics via gcloud CLI.
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 Rds Deep — aWS RDS deep analysis covering Performance Insights, event subscriptions, proxy management, and global database status. Covers PI counter metrics, wait event analysis, top SQL queries, read replica lag, storage autoscaling, and Aurora-specific diagnostics.
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 Gcp Cloud Trace — google Cloud Trace latency analysis, trace exploration, sampling configuration, and span diagnostics via gcloud CLI.
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 Azure Aks — azure Kubernetes Service (AKS) cluster management, node pool status, addon management, upgrade planning, and health diagnostics via Azure CLI.
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 Gcp Bigquery Deep — google BigQuery job analysis, slot utilization, storage optimization, materialized views, BI Engine capacity, and query performance diagnostics via bq CLI and gcloud.
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 Gcp Cloud Build — google Cloud Build trigger management, build history analysis, worker pool configuration, artifact management, and CI/CD pipeline diagnostics via gcloud CLI.
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 Gcp Cloud Spanner — google Cloud Spanner instance management, query statistics analysis, hot spot detection, schema analysis, and performance diagnostics via gcloud CLI.
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.
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。
Artificial intelligence-driven transformative applications in disease ... — In cancer diagnostics, AI systems have achieved breakthrough performances
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。
Manages MongoDB Atlas Stream Processing (ASP) workflows. Handles workspace provisioning, data source/sink connections, processor lifecycle operations, debugging diagnostics, and tier sizing. Supports Kafka, Atlas clusters, S3, HTTPS, and Lambda integrations for streaming data workloads and event processing. NOT for general MongoDB queries or Atlas cluster management. Requires MongoDB MCP Server with Atlas API credentials.
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。
Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.
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。
You are an expert error analysis specialist with deep expertise in debugging distributed systems, analyzing production incidents, and implementing comprehensive observability solutions.
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。
You are an error tracking and observability expert specializing in implementing comprehensive error monitoring solutions. Set up error tracking systems, configure alerts, implement structured logging,
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。
Guide for optimizing MSBuild build parallelism and multi-project scheduling. Only activate in MSBuild/.NET build context. USE FOR: builds not utilizing all CPU cores, speeding up multi-project solutions, evaluating graph build mode (/graph), build time not improving with -m flag, understanding project dependency topology. Note: /maxcpucount default is 1 (sequential) — always use -m for parallel builds. Covers /maxcpucount, graph build for better scheduling and isolation, BuildInParallel on MSBuild task, reducing unnecessary ProjectReferences, solution filters (.slnf) for building subsets. DO NOT USE FOR: single-project builds, incremental build issues (use incremental-build), compilation slowness within a project (use build-perf-diagnostics), non-MSBuild build systems. INVOKES: binlog MCP server tools (expensive_projects, expensive_targets, project_target_times); falls back to dotnet build -m, dotnet build /graph, binlog replay + grep.
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。
Diagnose MSBuild build performance bottlenecks using binary log analysis. Only activate in MSBuild/.NET build context. USE FOR: identifying why builds are slow by analyzing binlog performance summaries, detecting ResolveAssemblyReference (RAR) taking >5s, Roslyn analyzers consuming >30% of Csc time, single targets dominating >50% of build time, node utilization below 80%, excessive Copy tasks, NuGet restore running every build. Covers timeline analysis, Target/Task Performance Summary interpretation, and 7 common bottleneck categories. Use after build-perf-baseline has established measurements. DO NOT USE FOR: establishing initial baselines (use build-perf-baseline first), fixing incremental build issues (use incremental-build), parallelism tuning (use build-parallelism), non-MSBuild build systems. INVOKES: binlog MCP server tools (overview, errors, search, items, properties); falls back to dotnet msbuild binlog replay + grep/cat when the MCP is unavailable.
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。