“parallel”的搜索结果

Concurrent investigation of independent failures. Use when multiple unrelated issues need parallel resolution.

中文简介正在准备中。当前显示原文说明。

Shows you how to use Cowork's parallel workers to run multiple tasks at the same time instead of one after another — dramatically cutting the time on batch work. For processing large batches of files or research tasks, when running multiple independent analyses simultaneously, or during a performance optimization session. Use when the user says "speed up", "parallel workers", "batch task", "process multiple files", "why is cowork so slow", "faster", "do several things at once", or "can cowork multitask".

中文简介正在准备中。当前显示原文说明。

Use ONLY when the user explicitly asks to orchestrate parallel external agents (Codex or Claude Code) in tmux sessions via tp (tmux-pilot). Or when a user tells you that you're the boss or orchestrator. You can also use this if you are an operclaw agent. NEVER auto-invoke for a generic "do this in parallel" request. For in-process subagents, use superpowers:dispatching-parallel-agents instead.

中文简介正在准备中。当前显示原文说明。

Coordinate structured thinking and multi-agent parallel execution for complex tasks. Use when tackling multi-step projects, planning parallel work, breaking down complex problems, coordinating specialist tasks, facing architectural decisions, or when user mentions "workflow", "orchestration", "multi-step", "coordinate", "parallel execution", "structured thinking", "break this down", "plan this out", "how should I approach", or needs help planning complex implementations.

中文简介正在准备中。当前显示原文说明。

Breaks work into ordered, parallel-dispatchable tasks with an execution DAG. Output format is consumable by CI matrices and parallel agent runners — each task has a stable ID, declared file writes, conflict edges, and branch suffix. Use when you have a spec, brief, or shaper output and need to decompose it into implementable units. Use when a task feels too large, when scope spans multi-repo or multi-week work, or when parallel execution across multiple agents is on the table.

中文简介正在准备中。当前显示原文说明。

The intelligence layer of WinDAGs. Decomposes natural language tasks into Hierarchical Task DAGs (HTDAGs), matches subtasks to skills, executes waves in parallel, and dynamically expands nodes when complexity exceeds executor capability. Use for 'orchestrate', 'execute DAG', 'parallel agents', 'decompose task', 'coordinate skills'. NOT for single-skill tasks or simple linear workflows.

中文简介正在准备中。当前显示原文说明。

分布式并行策略选型与实测（USP / CP 通信掩盖 / CFG / TP/RSP/PP 概览；含拓扑相关选型 （按实测拓扑分域条件化：域内 bulk vs 跨域 head-parallel 翻转）与 AlltoAllV 缺陷绕过）。在 model-auto-optimization 中承担 S3：优先 USP、结合拓扑带宽差异选 CP，少量 step + 多 rank 验证特性开启与掩盖（产物：并行方案 + 多 rank 证据）。当用户需要多卡并行 策略选择、**序列并行形态抉择**（纯 Ulysses vs 复合 AllGather-KV×Ulysses：按 GQA / 跨域带宽 / 形态 plumbing 条件化定胜负）、**并行 × 稀疏叠加**（seam 契约：先汇聚后稀疏、 窗口偏移、块对齐、per-head 掩码；含「稀疏看似生效实则未生效」判定）、通信掩盖调优 （含**掩盖率上限**：1-1/n 何时成立、c/f 决定的真实上限、没生效的排查）、**并行方案 差异归因**（阶段 Δ 分解 / 集合通信按 communicator 归属 / 4→8 卡线性度），或排查多卡 跑不动 / 通信暴露大 / 换卡组 / 端口 bind / HCCL 带宽验证问题时使用；**并收编原并行作用域诊断**： 改了 SP/CP/Ulysses/AllGather-KV 后**不报错但结果没变/性能没变**、或小规模能跑大规模崩 （如 Ascend EE1003 coreDim 超限）时，用本技能证明"改动到底有没有生效"（判别量逐层收窄 + 两侧对照 + 日志≠生效，见 `references/scope-effectiveness-check.md`）； 即使用户只说 "多卡跑不动""通信暴露大""为什么没达到 6/7 的掩盖率""CP 和 USP 该选哪个""CP 叠稀疏 怎么不生效"而未说并行，也应触发。特性档位/接口事实见 `docs/zh/features/parallelism.md` /`usp.md`（仓内真源），框架侧开启见 framework-integration； 本技能承载选型决策、monkey-patch 掩盖与多卡诊断实测。 由 dev-workflow 多卡场景触发，亦由 model-auto-optimization 的 S3 阶段触发。

中文简介正在准备中。当前显示原文说明。

Task claiming and ownership tracking for parallel agent execution. Agents claim tasks from a shared queue with file ownership enforcement. Use during parallel execution phases when multiple agents work simultaneously.

中文简介正在准备中。当前显示原文说明。

Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.

中文简介正在准备中。当前显示原文说明。

Fornece treinamento distribuído de LLM nativo do PyTorch usando torchtitan com paralelismo 4D (FSDP2, TP, PP, CP). Use para fazer pré-treinamento de Llama 3.1, DeepSeek V3 ou modelos customizados em escala de 8 a 512+ GPUs com Float8, torch.compile e checkpoint distribuído.

中文简介正在准备中。当前显示原文说明。

Treina modelos de linguagem grandes (2B-462B de parâmetros) usando NVIDIA Megatron-Core com estratégias avançadas de paralelismo. Use ao treinar modelos >1B de parâmetros, precisar de máxima eficiência de GPU (47% MFU em H100), ou necessitar de paralelismo tensor/pipeline/sequência/contexto/expert. Framework pronto para produção usado em Nemotron, LLaMA, DeepSeek.

中文简介正在准备中。当前显示原文说明。

6つの専門レビュアーロールを並列実行し、consensusLevel（複数ロールの合意度）と Tech Lead レポート（top3指摘・blindSpots・consensusSummary）で結果を統合する マルチエージェントレビュー entry skill。 Parallel multi-role review with consensus scoring (consensusLevel) and Tech Lead report. Use when a major release needs exhaustive multi-angle review, or when a single-perspective review is not enough and you want confidence that no reviewer angle was missed（重要リリース前の網羅レビュー・多視点の確証が 欲しいとき）。

中文简介正在准备中。当前显示原文说明。

Coordinate parallel autonomous operations. Use when running parallel features, managing concurrent work, coordinating multiple agents, or optimizing throughput.

中文简介正在准备中。当前显示原文说明。

Complete guide for TUnit new-generation testing framework. Use when creating test projects with TUnit or migrating from xUnit to TUnit. Covers Source Generator driven test discovery, AOT compilation support, fluent async assertions. Includes project creation, [Test] attribute, lifecycle management, parallel control, and xUnit syntax comparison. Keywords: TUnit, tunit testing, source generator testing, AOT testing, new generation testing framework, [Test], [Arguments], TUnit.Assertions, Assert.That, Before(Test), After(Test), NotInParallel, TUnit.Templates, Microsoft.Testing.Platform, TUnit vs xUnit, parallel execution

中文简介正在准备中。当前显示原文说明。

为多个 AI 智能体分配负责范围和完成条件，用看板共享进度与阻碍。通过分支或独立工作目录分开编辑，并在测试、差异审查和风险检查后整合成果。

• 向多个 AI 智能体分配并行任务
• 跨会话共享进度与阻碍
• 通过分支或 worktree 分开编辑

使用 Kotlin Coroutines 和 Flow 实现 Android 与 Kotlin Multiplatform 的异步任务和数据更新，涵盖并行加载、界面状态、取消、错误处理及测试。

• 并行加载多种数据
• 使用 StateFlow 管理界面状态
• 对搜索输入防抖并重试网络请求

帮助在Django中用Celery将邮件发送、PDF生成等工作转为后台任务，并设计定期运行、失败重试、任务串联、并行执行、监控与测试。

• 在后台发送邮件和生成PDF
• 定期更新库存和发送通知
• 设计重试并防止重复更新

帮助设计在 Claude Code 中反复推进开发工作的自主循环，涵盖顺序执行与并行任务，并梳理运行间的进度衔接、质量检查、变更合并和停止条件。

• 设计顺序执行的开发流程
• 在多次运行之间衔接进度
• 协调并行任务与变更合并

通过 Claude DevFleet 将开发工作分配给多个 Claude Code 智能体，在独立工作目录中并行执行，并管理任务依赖、进度以及变更、测试和错误报告。

• 将开发计划拆分为带依赖关系的任务
• 在独立工作目录中并行开发
• 检查多个开发任务的进度

将大型功能开发或迁移目标拆成可分多次执行的计划，整理每个步骤的背景、依赖关系和验证方法，支持将工作交接给其他代理。

• 将大型功能拆分为多个PR
• 规划跨多次会话的迁移
• 找出可并行推进的步骤

使用云端GPU运行机器学习训练、推理和数据处理，并将模型发布为API。通过Python定义运行环境，还可配置自动扩缩容、存储、并行处理和定时任务。

• 无需管理GPU服务器即可训练模型
• 将模型发布为自动扩缩容的API
• 并行处理大量数据

通过OpenAI Codex CLI完成现有项目的功能开发、代码重构和拉取请求审查。也涵盖长时间任务的进度检查，以及使用独立Git工作目录并行修复多个问题的步骤。

• 为现有项目添加功能
• 重构认证模块
• 审查拉取请求

Coordinate 15 agents implementing claude-flow v3 across security, core systems, integration, testing, performance, and release work. Provides role assignments, task dependencies, GitHub tracking examples, and a four-phase schedule spanning 14 weeks.

中文简介正在准备中。当前显示英语简介。

• Assign claude-flow v3 work across 15 agents
• Run tasks in parallel while respecting dependencies
• Track milestones and progress through GitHub issues

Create workflows that pause for approval and resume later, or scripts that distribute work across multiple agents. Choose a saved template for ongoing processes or a JavaScript script to collect structured results from parallel tasks.

中文简介正在准备中。当前显示英语简介。

• Build a workflow with approval checkpoints
• Split review work across multiple agents
• Collect structured results from research tasks