「queues」の検索結果

Use this skill when creating or managing queues, configuring queue membership, setting up case or lead queues, creating public groups, or using groups in sharing rules and manual sharing. Trigger keywords: queue, public group, queue membership, queue email, group sharing, case queue, lead queue. NOT for the rule that decides which queue a record lands in — use admin/assignment-rules. NOT for Omni-Channel routing configuration — use admin/omni-channel-routing-setup.

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。

LaravelのWebアプリやAPIを、役割の分かれた構成で実装・レビューします。データ取得、入力検証、API応答、キューやキャッシュを整理し、保守しやすい設計を支援します。

• コントローラーをサービスとActionsに分けたいとき
• Eloquentの関連付けと過剰なクエリの点検
• API応答・ページ分割・入力検証の統一

Work with Amazon SQS for reliable message queuing. Create standard and FIFO queues, configure dead-letter queues for failed messages, send and receive messages in batches, and build decoupled event-driven architectures.

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 this skill when configuring or troubleshooting Lead or Case assignment rules in Salesforce: creating rule entries, setting filter criteria, assigning records to users or queues, understanding when rules run, and implementing round-robin patterns with Apex. Trigger keywords: lead assignment, case assignment, assignment rule, queue assignment, auto-assign. NOT for creating the queue or public group a rule routes into — use admin/queues-and-public-groups. NOT for Omni-Channel or skills-based routing — use admin/omni-channel-routing-setup. NOT for approval routing — use admin/approval-processes.

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。

Auto-activate for litestar_queues, QueuePlugin, QueueConfig, WorkerConfig, QueueService, @task, QueuedBackgroundTask, QueueEventsConfig, or SQLSpecBackendConfig. Not for litestar-saq, Celery, or Dramatiq.

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。

リアルタイム画面やデータ配信の遅延、情報の鮮度、処理待ちを個別に測り、イベント発生から画面表示までの経路を改善して、稼働環境で結果を確かめるスキル。

• リアルタイム画面の遅延調査
• 配信データの鮮度を確認したいとき
• キューの滞留を減らしたいとき

Python background job patterns including task queues, workers, and event-driven architecture. Use when implementing async task processing, job queues, long-running operations, or decoupling work from request/response cycles.

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。

Runs batch and HPC workloads on GKE, utilizing job queues and parallel processing. Use when running GKE batch jobs, configuring GKE HPC, or setting up GKE job queues. Don't use for standard web application deployments (use gke-app-onboarding instead).

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。

Helps DevOps engineers configure mirrord Operator's Temporal task queue splitting feature end-to-end. Generates the MirrordSplitConfig and MirrordPropertyList Kubernetes CRD YAMLs, the matching mirrord.json split_queues section with message_filter and jq_filter, and Helm value guidance. Use this skill whenever the user mentions Temporal splitting with mirrord, Temporal task queue splitting, splitting a Temporal worker, configuring mirrord with Temporal or Temporal Cloud, routing Temporal workflow or activity tasks to a local worker, or troubleshooting Temporal splitting sessions. Also trigger on split_queues with queue_type Temporal, or connecting mirrord to a Temporal frontend. This is an alpha Team/Enterprise feature of mirrord.

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。

Helps DevOps engineers configure mirrord Operator's Kafka queue splitting feature end-to-end. Generates the MirrordSplitConfig and MirrordPropertyList Kubernetes CRD YAMLs (the current resources; MirrordKafkaTopicsConsumer + MirrordKafkaClientConfig are deprecated but still supported), the matching mirrord.json split_queues section with message_filter and jq_filter, and Helm value guidance. Use this skill whenever the user mentions Kafka splitting with mirrord, MirrordSplitConfig, MirrordPropertyList, MirrordKafkaClientConfig, MirrordKafkaTopicsConsumer, Kafka queue/topic splitting, configuring mirrord with Kafka, Kafka Streams splitting, MSK IAM auth, or troubleshooting Kafka splitting sessions. Also trigger on split_queues with queue_type Kafka, or connecting mirrord to a Kafka cluster. This is a Team/Enterprise feature of mirrord.

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 this skill when the user is doing hands-on NVMe-over-Fabrics storage-target work on a BlueField DPU or ConnectX NIC with DOCA STA — standing up a doca_sta DOCA Core context that accelerates the target-side NVMe-oF data path over RDMA, defining doca_sta_subsystem targets (NQN + namespaces) backed by local NVMe-PCI backend disks (doca_sta_be), checking device support via doca_sta_cap_is_supported, sizing the per-connection I/O queues, or debugging DOCA_ERROR_* from a STA call. Trigger even when the user does not say "DOCA STA" — typical implicit phrasings include "my NVMe-oF Connect never completes", "Identify Controller times out over RoCE", "16 I/O queues at depth 1024 — does this BlueField support that", "offload the nvmf target onto the DPU", or "DOCA_ERROR_IO_FAILED on an NVMe read". Refuse and route elsewhere for DOCA install, raw RDMA data movement, raw packet I/O, flow-rule programming, or initiator-side / host NVMe stack work — those belong to other skills.

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。

Azure Queue Storage JavaScript/TypeScript SDK (@azure/storage-queue) for message queue operations. Use for sending, receiving, peeking, and deleting messages in queues. Supports visibility timeout, message encoding, and batch operations. Triggers: "queue storage", "@azure/storage-queue", "QueueServiceClient", "QueueClient", "send message", "receive message", "dequeue", "visibility timeout".

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。

Designs and runs the support function — channels, queues, routing, staffing, service levels, quality, and the metrics that show whether it is working. Use this to set up or fix support operations, choose channels, size a team, set or renegotiate service levels, reduce cost per contact, diagnose long queues or poor quality, or decide what to automate.

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。

AWS SQS message queue service for decoupled architectures. Use when creating queues, configuring dead-letter queues, managing visibility timeouts, implementing FIFO ordering, or integrating with Lambda.

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。

Async communication patterns using message brokers and task queues. Use when building event-driven systems, background job processing, or service decoupling. Covers Kafka (event streaming), RabbitMQ (complex routing), NATS (cloud-native), Redis Streams, Celery (Python), BullMQ (TypeScript), Temporal (workflows), and event sourcing patterns.

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。

Provides AWS messaging patterns using AWS SDK for Java 2.x for SQS queues and SNS topics. Handles sending/receiving messages, FIFO queues, DLQ, subscriptions, and pub/sub patterns. Use when implementing messaging with SQS or SNS.

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。

This skill should be used when the user asks to "set up Cloudflare Queues", "create a message queue", "implement queue consumer", "process background jobs", "configure queue retry logic", "publish messages to queue", "implement dead letter queue", or encountering "queue timeout", "message retry", "throughput exceeded", "queue backlog" errors.

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 in amqplib, the Node.js client for RabbitMQ and AMQP 0-9-1 protocol. You help developers implement reliable message queuing with work queues, pub/sub fanout, topic routing, RPC patterns, dead letter queues, and message acknowledgment — building decoupled microservices that communicate asynchronously through RabbitMQ.

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。

Unified expert guide for async job queues & durable workflows: BullMQ v5 (Redis queues), Trigger.dev v3 (serverless tasks), Inngest, and Temporal.io (distributed sagas) / Panduan ahli terpadu untuk antrean job asinkron & workflow tahan-gagal: BullMQ v5, Trigger.dev v3, Inngest, dan Temporal.io.

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。

Cloudflare platform management via Wrangler CLI, Agents SDK (v0.13+), and Browser Rendering API. Deploy Pages, Workers, KV, R2, D1, Queues, Vectorize, Workflows, Hyperdrive. Build stateful AI agents with the full SDK stack — Agent, AIChatAgent, Think, Voice, sub-agents, fibers, HITL, scheduling, observability, Code Mode (Dynamic Worker sandboxes). Budget web scraping via cf_browser.py. Use when deploying to Cloudflare, managing CF infrastructure, configuring wrangler.toml/wrangler.jsonc, building AI agents or chat agents on Workers, adding voice, orchestrating sub-agents, or for cheap web scraping. Triggers: Cloudflare, wrangler, Pages, KV, R2, D1, Workers, Vectorize, Queues, Workflows, Hyperdrive, cf_browser, Browser Rendering, Agents SDK, Code Mode, codemode, MCP, Think, AIChatAgent, Voice agent, sub-agents, fibers, HITL, Dynamic Workers.

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。

Runs batch and HPC workloads on GKE, utilizing job queues and parallel processing. Use when running GKE batch jobs, configuring GKE HPC, or setting up GKE job queues. Don't use for standard web application deployments (use gke-app-onboarding instead).

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。

Configuring Salesforce lead management and conversion in Setup: lead settings, web-to-lead, conversion field mapping, lead queues, auto-response rules, lead processes. Trigger keywords: web-to-lead, lead conversion, lead field mapping, lead settings, lead process, lead queue, lead auto-response. NOT for Apex that controls what conversion creates - use apex/lead-conversion-customization. NOT for lead assignment rule logic - use admin/assignment-rules. NOT for duplicate rule configuration - use admin/duplicate-management. Also covers: LeadConvertSettings objectMapping XML, LeadConfigSettings, LeadStatus StandardValueSet converted flag, lead process BusinessProcess, Database.convertLead bulk conversion limits.

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。

Auto-activate for litestar_saq, SAQPlugin, SAQConfig, QueueConfig, TaskQueues, CronJob, litestar workers run, background jobs, schedules, or SAQ UI. Not for litestar-queues, Celery, RQ, or Dramatiq.

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 designing a background-job system, choosing between BullMQ / Sidekiq / RQ / Temporal / SQS, deciding queue-vs-workflow, sizing concurrency vs rate limits, building dead-letter queues, or making handlers idempotent. Triggers: jobs running twice on retry, lost jobs after worker crash, DLQ filling up, Redis OOM from job backlog, exactly-once requested, "do we need Temporal?", visibility timeout / lockDuration confusion, exponential backoff vs jitter, fan-out fan-in workflows. NOT for outbound webhook publishing (different concerns), receiver-side webhook handling (different concerns), event-streaming/Kafka topology, or in-process async (event loop 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。