“parallel”的搜索结果

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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Automated CLI-based parallel agent execution — spawn subagents via Gemini CLI, coordinate through MCP Memory, monitor progress, and run verification

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Coordinate multiple agents for a complex multi-domain project using PM planning, parallel agent spawning, and QA review

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Turborepo (高速モノレポビルドシステム) リファレンス。 turbo.json、turbo run、dependsOn、task tags、キャッシュ (local / remote)、 workspaces (pnpm / npm / yarn / bun / nub / aube)、Rust / Python / Go ワークスペース (実験的)、 --filter、turbo query affected、turbo prune、turbo gen、watch、parallel 実行、 Remote Cache (Vercel / 自前ホスト Remote Cache API)、devEngines.packageManager、 エラー診断 (recursive turbo invocations, missing root task, invalid env prefix)。

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Upstash サーバーレスデータプラットフォーム (マネージド SaaS の JS SDK)。 @upstash/redis (REST, pipelining, transactions, JSON, Redis Search, VECTOR/Array/Functions コマンド)、 @upstash/ratelimit (Fixed/Sliding Window, Token Bucket, blockUntilReady)、 QStash (publishJSON, schedules, queues, URL Groups, flow-control, DLQ)、 @upstash/vector (upsert, query, ANN, namespace, hybrid/sparse index, embedding models, reranking)、 @upstash/workflow (serve, durable execution, context.run/sleep/call/invoke, waitForEvent, parallel steps, agents)、 Upstash Search (@upstash/search, search, upsert, fetch, range, filtering, reranking, algorithm)、 @upstash/box (サンドボックス, filesystem, git, CDP, browser, network policy, snapshots, custom harness)、 upstash CLI。Rust 製 fandhe-db とは別。

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Next.js App Router 専用 API リファレンス（Pages Router / getStaticProps 等は非対応）。 Server Components, Server Actions, layout/page/loading/error ファイル規約, dynamic / parallel / intercepting routes, Metadata API (generateMetadata), Cache Components, use cache, Partial Prefetching, revalidatePath/revalidateTag/updateTag, catchError, next/root-params, ensureStatic, next/image・next/font・next/link, cookies/headers, useRouter, next.config.js, proxy（旧 middleware）。 プラットフォーム実行基盤・CDN・ISR インフラは vercel スキル参照。

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Bulk mode — maximum development speed by spending tokens aggressively on massive parallel sub-agents and multi-agent workflows, while the orchestrator context stays lean (pairs with /slim). Invoking /bulk is the user's explicit opt-in to Workflow/ultracode-style orchestration for this session. Use when the user types /bulk, asks for "爆速", "並列で大量に", "ultracode", "多エージェント", or hands over a big task where wall-clock speed matters more than token cost.

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Configure a Rails project to work with Conductor (parallel coding agents)

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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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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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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.

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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.

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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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Review a GitHub pull request from multiple perspectives in parallel, output only findings, then let the user pick which to fix in the same session. Use when the user asks to review a PR by number, mentions "PRレビュー" / "PR review" with a number, or invokes `/pr-review <number>`. Fans out subagents for absolute-rules, stop-slop, security, architecture, code-quality, testing, and ai-antipattern.

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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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Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2

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Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.

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Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.

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Use when: User wants to run Archon workflows, CREATE workflows or commands, set up Archon, or manage Archon configuration. Triggers (run): "use archon to", "run archon", "archon workflow", "use archon for", "have archon", "let archon", "ask archon to". Triggers (create): "create a workflow", "write a workflow", "make a command", "author a workflow", "new workflow", "new command", "archon workflow yaml". Triggers (setup): "set up archon", "install archon", "how to use archon", "configure archon", "archon setup", "get started with archon". Triggers (config): "change my archon config", "modify archon config", "archon config", "change archon settings", "update my config", "help me change my config", "edit archon config", "archon configuration". Triggers (init): "initialize archon", "set up .archon", "archon init", "add archon to repo". Capability: Runs AI workflows in isolated git worktrees for parallel development. Also: Creates and manages workflow YAML files, command files, and configuration. NOT for: Direct Claude Code work - only for delegating to Archon CLI.

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Comprehensive end-to-end validation of the Archon Web UI using browser automation and codebase review. Use when: User wants to validate, test, or audit the Archon web interface, find UI/UX bugs, test workflow management, verify parallel agent orchestration, or run comprehensive browser-based E2E tests. Triggers: "validate ui", "test the ui", "e2e test", "browser test", "validate archon", "test archon ui", "ui audit", "ux review", "comprehensive test", "validate everything". Capability: Starts Archon, runs exhaustive browser automation tests via agent-browser CLI, performs codebase review, and produces a detailed bug/UX report. NOT for: Unit tests (use `bun test`), CLI-only validation (use /validation:validate-simple).

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Deep agentic-flow@alpha integration implementing ADR-001. Eliminates 10,000+ duplicate lines by building claude-flow as specialized extension rather than parallel implementation.

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Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI systems.

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