918 件 ・ 関連度順
概要と使いどころ
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.
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 Argocd — argoCD GitOps continuous delivery management for Kubernetes. Covers application sync status, deployment health, rollback operations, repository management, cluster registration, RBAC analysis, and application diff review. Use when checking deployment status, investigating sync failures, managing GitOps applications, or auditing ArgoCD 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 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 Allure Testops — allure TestOps test management and analytics platform monitoring. Covers project dashboards, launch tracking, test case management, defect clustering, environment matrix analysis, and CI/CD integration review. Use when managing test cases in Allure TestOps, reviewing launch results, analyzing flaky tests, or tracking quality trends.
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 Gcp Gke — google Kubernetes Engine cluster operations, node pool management, workload analysis, autopilot configuration, and upgrade planning 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 Cassandra — apache Cassandra keyspace analysis, compaction strategies, repair status, nodetool operations, and cluster health monitoring.
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。
Use when working with Neo4J — neo4j graph database analysis, index management, Cypher query optimization, APOC utilities, and cluster health monitoring.
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 Redshift — amazon Redshift query performance, WLM configuration, table design analysis, vacuum/analyze status, and cluster 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 Clickhouse — clickHouse table analysis, MergeTree optimization, query performance tuning, parts management, and cluster 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 Cockroachdb — cockroachDB cluster health, range distribution, SQL statistics, schema change monitoring, and query optimization.
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 Couchbase — couchbase bucket analysis, index advisor, N1QL query performance, XDCR status, and cluster health monitoring.
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 Ecs — aWS ECS cluster health, service status, task analysis, capacity provider management, and deployment tracking. Covers service stability, task failure investigation, container insights, resource utilization, and scaling 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。
Use when working with Singlestore — singleStore (MemSQL) workspace management, pipeline status, query tuning, memory analysis, and cluster 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.
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 Mongodb — mongoDB database analysis, performance tuning, query optimization, and health monitoring. Covers collection analysis, index recommendations, aggregation pipelines, replica set health, Atlas cluster management, and slow query investigation.
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 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 Alloydb — google AlloyDB instance analysis, query insights, columnar engine optimization, maintenance windows, and cluster 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 Kubescape — kubescape Kubernetes security posture analysis. Covers NSA/CISA framework assessment, MITRE ATT&CK mapping, CIS Kubernetes benchmarks, workload scanning, RBAC analysis, and network policy evaluation. Use when assessing Kubernetes cluster security, evaluating compliance frameworks, or analyzing workload 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 Tidb — tiDB cluster topology, slow query analysis, hot region analysis, dashboard monitoring, and SQL optimization.
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。
scikit-learn compatible Python toolkit for time series ML: classify, cluster, regress, segment, transform with 30+ algorithms (ROCKET, InceptionTime, KNN-DTW, HIVE-COTE, WEASEL). Handles panel, multivariate, and unequal-length series. Maintained successor to sktime. Alternatives: sktime (larger ecosystem), tslearn (fewer algorithms), catch22 (features only).
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。
Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker, pair scoring for two-stage retrieval / pair classification), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.
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。