「parallel」の検索結果

Azure Batch SDK for Java. Run large-scale parallel and HPC batch jobs with pools, jobs, tasks, and compute nodes.

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Configure and orchestrate parallel agents using the standalone Antigravity 2.0 Agent Manager and Antigravity IDE.

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You are an API mocking expert specializing in realistic mock services for development, testing, and demos. Design mocks that simulate real API behavior and enable parallel development.

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Create complete specs (requirements, design, tasks) for all features in roadmap.md using parallel sub-agent dispatch by dependency wave.

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Launch, inspect, or tear down an isolated fleet of ready SDD Composy tasks: one branch and Git worktree per member, a bound LOOP, cmux/tmux/headless presentation, no automatic merge. Use to run several ready tasks in parallel — 'rodar várias tarefas em paralelo', 'lançar o fleet', 'fleet status', 'tear down member X'. Do NOT use for a single task (sdd-execute, sdd-loop) or for merging without the explicit teardown token.

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Launch, inspect, and tear down autonomous phase fleets in isolated Git worktrees, on the Codex or the Claude runtime. Use when approved Flow phase slugs should run in parallel, when fleet status is requested, or when a fleet member must be stopped or explicitly merged.

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Use when approved phases should run in parallel — visually in a cmux panel or unattended — when fleet status is requested, or when a fleet member must be stopped or merged

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Build modern Next.js 13+ applications using App Router architecture with Server Components, Client Components, and advanced routing patterns. Use when implementing server-first rendering, creating nested layouts, building parallel routes, implementing intercepting routes, using React Server Components, optimizing data fetching with server-side async components, implementing streaming with Suspense, managing client-side interactivity boundaries, or leveraging Next.js 13+ app directory features for performant, SEO-friendly React applications.

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Daily per-section review cycle driving a section toward the smallest, clearest form that still does everything it does today. Selects its section live each run (scripted), inventories every file, derives the target shape before any scan, works parallel reading threads into one ranked list and strikes it on a 2-3h budget. One PR per run; each run's report and Needs-Peter queue live in its own docs/health/runs/ file; 'resume' applies Peter's answers later. Use for the morning section-improvement run, 'doctor <section>', or 'run section doctor'.

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天才会議の並列サブエージェント版。最重要の意思決定で、4人のペルソナを独立したサブエージェントとして並列実行し、互いの意見を見ずに独立思考させてから統合・レッドチーム検証する。「並列会議」「本気の会議」「独立に考えさせて」「エージェント会議」などのリクエストで使用。

Use only when the user explicitly types `/orchestrate goal` to decompose a large task, spawn a tree of parallel cloud-agent workers/subplanners/verifiers via the Cursor SDK, and collect structured handoffs; do not invoke autonomously.

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Guides Rust performance optimization. Use when profiling, benchmarking, reducing allocations, improving cache locality, choosing between rayon/async/threads, or applying SIMD/parallelism.

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Multi-agent exploration and task delegation for complex areas of interest. Use when you need to deeply explore a codebase area, gather information from multiple perspectives, or delegate parallel research tasks for comprehensive analysis.

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Idiomatic Go concurrency - sync primitives, channel semantics, the select statement, and the standard channel patterns (cancellation/done-channel, fan-out/fan-in, pipeline, or-done, context, worker-pool). Use when writing or reviewing Go that spawns goroutines, shares state, or coordinates over channels, even if the user never says "concurrency" - e.g. data races, deadlocks, goroutine leaks, sync/context/select usage, or "make this parallel". Distilled from luk4z7/go-concurrency-guide.

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Research a skill domain before building or improving a skill. Searches academic papers, practitioner blogs, and GitHub skill repos in parallel to find current best practices, domain gotchas, and existing skill patterns. Called by universal-skill-creator and improve-skills before writing any skill. Also load directly when the user asks to research a domain for a skill, find existing skills on a topic, discover best practices for a skill, check what research exists before building an agent skill, or says "what does current research say about", "find best practices for".

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Design state-of-the-art multi-agent systems, orchestration patterns, and wiring from scratch. Load when the user asks to build an agent system from scratch, design agent orchestration patterns, choose between sequential/parallel/hierarchical workflows, or define how multiple agents should collaborate. Also triggers on "agent architecture", "multi-agent wiring", "agent orchestration pattern", "how to connect these agents", "design the communication structure". Called by agent-builder for complex topologies (>4 agents or hierarchical). For process-driven agent design, use agent-builder instead.

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Structure git work with atomic commits, conventional messages, short-lived branches, and pre-commit hygiene. Load when committing, branching, resolving merge conflicts, organizing parallel work, or reviewing git history. Also triggers on "git workflow", "commit message", "conventional commits", "atomic commit", "branch strategy", "git worktree", "how should I commit this". Does not replace project-specific hook policies — complements them.

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Internal skill. Called by setup-evaluation after a PASS. Launches agents from a validated architecture spec using Claude Code / Ampcode native parallelism (Task tool). Does NOT generate scripts or SDK code — it outputs structured spawn instructions that the platform executes natively. Never invoked directly by the user. Never launches without a setup-evaluation PASS.

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Master Bevy's Entity Component System (ECS) in Rust, covering Systems, Queries, Resources, and parallel scheduling.

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Azure Batch SDK for Java. Run large-scale parallel and HPC batch jobs with pools, jobs, tasks, and compute nodes.

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 API mocking expert specializing in realistic mock services for development, testing, and demos. Design mocks that simulate real API behavior and enable parallel development.

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。

Download large datasets from NCBI efficiently using EPost, history server, batching, rate limiting, and retry logic. Use when bulk-fetching tens of thousands of sequences, pulling all results of a large ESearch, designing reproducible pipelines, comparing E-utilities to NCBI Datasets v2 CLI, or implementing checksum-validated downloads. Encodes WebEnv TTL (~8h), EPost 200-ID limit, retmax caps, parallelization design, and integrity verification.

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Download genome assemblies, gene records, and ortholog data from NCBI using the modern Datasets v2 CLI (replaces assembly_summary.txt scraping and many EFetch workflows). Use when bulk-pulling genome assemblies, gene metadata across species, ortholog sets, or BLAST databases; when E-utilities are too slow for genome-scale work; or when automatic checksum verification, parallel download, and clean accession-driven retrieval are required. Encodes the JSON-lines output format, dataformat conversion, --dehydrated for cloud workflows, and when Datasets is/isn't the right tool.

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Implements multiplicity control for confirmatory clinical trials using graphical procedures (Bretz-Maurer-Hommel), gatekeeping (parallel, serial, mixed), Hochberg/Hommel/Holm with PRDS, and the closed-testing principle (Marcus-Peritz-Gabriel; Goeman 2021 admissibility). Covers FDA Multiple Endpoints Final Guidance (October 2022), graphical procedures via R gMCP, primary + key-secondary + subgroup hierarchies, and FWER vs FDR distinction. Use when designing the multiplicity strategy for confirmatory trials with multiple primary or key secondary endpoints.

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。