“queue”的搜索结果

调查OpenClaw CI的排队和运行环境不足问题，根据测量结果调整运行器注册预算、并行数量、测试分组、运行器规格及任务分配位置。

• 调查OpenClaw CI排队原因
• 估算高峰期运行器注册数量
• 调整并行数量和测试拆分

将大型功能的RFC设计提案拆分为带依赖关系和验收测试的工作单元，组织多个智能体完成调查、实现、测试与审查，再依次合并变更并验证整个系统。

• 将大型设计提案拆成可验证的任务
• 为多个智能体分配带依赖关系的工作
• 审查并依次合并变更分支

分别衡量实时界面与数据推送的延迟、数据新鲜度和待处理任务，梳理事件发生到屏幕显示的完整路径，逐段改进，并在运行环境中验证结果。

• 调查实时界面的延迟
• 检查推送数据的新鲜度
• 减少队列积压

Manage ComfyUI image generation from the command line. Validate workflows, which define generation steps, submit them for execution, monitor queued jobs, inspect available models, and download images from completed runs.

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• Validating and submitting image generation workflows
• Checking or interrupting queued generation jobs
• Listing available models and node types

Move slow Python operations into background jobs so requests can return promptly. Use task queues and workers, the processes that execute queued work, with retry rules, duplicate handling, time limits, and persistent job status.

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• Send emails and notifications in the background
• Generate reports and data exports asynchronously
• Process uploads and media conversions

Size support and operations teams using demand ranges and queueing calculations, which estimate how work waits for service. Assess workload imbalance and service target risks, then build a quarterly hiring plan that accounts for training time and attrition.

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• Estimate staffing for support and operations queues
• Check workload imbalance and service target risks
• Build quarterly hiring plans with ramp time and attrition

Use the Archon CLI to launch AI workflows on projects, inspect their results, and approve, reject, cancel, or resume runs. The skill also guides setup, configuration changes, workflow creation, and improvements to workflow prompts.

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• Running an AI workflow to address an issue
• Asking Archon to review a pull request
• Inspecting, approving, or resuming a run

Run batch processing, scientific simulations, and machine learning training on Google Kubernetes Engine. Configure parallel jobs, queues that manage resource access, and distributed computation, with guidance on scaling idle capacity down and recovering from interruptions.

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• Run data processing jobs in parallel
• Share computing resources through job queues
• Configure distributed simulations with MPI

Create and manage Volcano jobs and scheduling queues in KubeSphere. Use YAML templates for PyTorch, TensorFlow, MPI, and batch workloads, configure resource requests and execution policies, and investigate jobs that remain pending or fail to create pods.

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• Prepare distributed training jobs for PyTorch or TensorFlow
• Create MPI and batch processing jobs
• Configure queue capacity and scheduling weight

Use this skill for hands-on DOCA Ethernet packet-queue work on a BlueField DPU or ConnectX NIC — bringing up a `doca_eth_rxq` or `doca_eth_txq` on a port / representor / SF, picking among the four `enum doca_eth_rxq_type` values (`_REGULAR` / `_CYCLIC` / `_MANAGED_MEMPOOL` / `_SHARED_MEMPOOL`), sizing burst or scatter-gather length against the `_cap_*` queries, submitting `doca_eth_txq_task_send` / `_lso_send` (carrying packet `doca_buf`s — no `doca_eth_frame` struct exists), or debugging DOCA_ERROR_* from an Ethernet call. `doca_eth_rxq` and `doca_eth_txq` are two Core contexts; one `doca_ctx_start()` does not cover both. Trigger on implicit phrasings: "my RX queue is up but no packets arrive", "send-task returns AGAIN at line rate", "which queue type for fixed-MTU ingress", "device open fails without sudo", or "is L3 checksum offload available here". Refuse and route elsewhere for installing DOCA, flow-rule / steering programming, host↔DPU control messaging, or RDMA data movement.

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Graphile Worker expert for high-performance PostgreSQL job queues with trigger-based job creation and millisecond job pickup via LISTEN/NOTIFY. Use when "graphile worker, postgres trigger job, listen notify queue, postgraphile worker, database trigger queue, transactional job, graphile-worker, postgresql, triggers, listen-notify, job-queue, postgraphile, high-performance, supabase" mentioned.

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Use when an "enable observability" card asks for queues, or a worker's queue.backlog / consumer lag signals are unbound - which exporter or direct broker data source reads Kafka, RabbitMQ, NATS and Redis queues, the metric names the recipes expect, and the consumer-side metrics the developer adds

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Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for queues, async workers, cron jobs, delayed tasks, retry behavior, worker-only config drift, and payload-to-side-effect chains. Use when the user asks to trace a queue payload, inspect async job execution, explain worker-only behavior, follow retries or dead-letter handling, or connect an enqueued item to a later file, cache, email, or privilege-bearing side effect. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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Work with the @upstash/qstash TypeScript/JavaScript SDK, an HTTP-based message queue, task scheduler, and background job system for serverless and edge runtimes (Next.js, Vercel, Cloudflare Workers, Deno, Node.js). Use when publishing messages to HTTP endpoints or URL groups, running background jobs without a long-running worker process, scheduling with cron expressions, delaying messages, building FIFO queues with parallelism and flow control, configuring retries and callbacks, handling a dead letter queue (DLQ), deduplicating messages, fanning out to multiple endpoints, verifying QStash webhook signatures (Next.js App Router, Pages Router, and Edge Runtime), running a local QStash dev server, or migrating regions. Also use when the user asks for a serverless cron job, async task queue, job scheduler, delayed delivery, webhook delivery with retries, or event-driven messaging between services.

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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).

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BullMQ expert for Redis-backed job queues, background processing, and reliable async execution in Node.js/TypeScript applications. Use when: bullmq, bull queue, redis queue, background job, job queue.

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BullMQ expert for Redis-backed job queues, background processing, and reliable async execution in Node.js/TypeScript applications. Use when: bullmq, bull queue, redis queue, background job, job queue.

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BullMQ expert for Redis-backed job queues, background processing, and reliable async execution in Node.js/TypeScript applications. Use when: bullmq, bull queue, redis queue, background job, job queue.

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Windows アプリ開発 (WinUI 3 / Windows App SDK) のテスト・性能・アクセシビリティ・スレッド。 MSTest, xUnit, NUnit, WinAppDriver, Appium, Playwright, WebView2 テスト, CI, WACK, MSIX サイドローディング, version adaptive。LoggingChannel / ETW, WPT, WPA, プロファイリング, GC, 起動性能, XAML 最適化, ListView/GridView 仮想化。UI Automation, AutomationProperties, automation peers, キーボード操作性, ハイコントラスト, Narrator。DispatcherQueue, DispatcherQueueTimer, WinRT 非同期パターン, async/await, ConfigureAwait, スレッドプール, STA/MTA, デッドロック回避, バックグラウンドタスク。

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BullMQ (Node.js ジョブキュー、Redis / PostgreSQL バックエンド対応) リファレンス。 Queue, Worker, Job, FlowProducer, QueueEvents、PostgreSQL backend、 createPostgresBackend, withBackend、レート制限、cron、delayed / repeatable / priority ジョブ、 再試行、デッドレター、フロー、バッチ処理、メトリクス。Python / Elixir / PHP / Rust 対応。

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BullMQ expert for Redis-backed job queues, background processing, and reliable async execution in Node.js/TypeScript applications. Use when: bullmq, bull queue, redis queue, background job, job queue.

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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).

中文简介正在准备中。当前显示原文说明。

BullMQ expert for Redis-backed job queues, background processing, and reliable async execution in Node.js/TypeScript applications. Use when: bullmq, bull queue, redis queue, background job, job queue.

中文简介正在准备中。当前显示原文说明。

用于构建和审查职责清晰的Laravel网站应用与API，梳理数据获取、输入验证、接口响应、队列和缓存，帮助形成便于维护的应用结构。

• 将控制器逻辑拆分为服务和Actions
• 检查Eloquent关联和过多查询
• 统一接口响应、分页与输入验证