1,279 件 ・ 関連度順
概要と使いどころ
Splits work across multiple agents or sessions running at once, keeping their surfaces disjoint so results merge cleanly. Use this when facing several independent tasks with no shared state, when a plan has parallelizable steps, when a broad search or audit would be faster fanned out, or when deciding whether work can safely be split at all.
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。
Fan out N parallel workers, drain them, and return one report. Use for /swarm, 'swarm this', or parallel coverage, races, gauntlets, and exploration.
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。
Optimize workload distribution across workers, processes, or nodes for efficient parallel execution. Use when asked to balance work distribution, improve parallel efficiency, reduce stragglers, implement load balancing, or optimize task scheduling. Covers static/dynamic partitioning, work stealing, and adaptive load balancing strategies.
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。
Parallel processing with joblib for grid search and batch computations. Use when speeding up computationally intensive tasks across multiple CPU cores.
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。
Run independent tasks concurrently across multiple files or targets using parallel sub-agents, with per-task model selection and LLM-as-a-judge verification. Use when tasks do not depend on each other and can run side by side.
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。
Fan out N parallel workers, drain them, and return one report. Use for /swarm, 'swarm this', or parallel coverage, races, gauntlets, and exploration.
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。
Multi-path parallel product analysis with cross-model test-time compute scaling. Spawns parallel agents (Claude Code agent teams + Codex CLI) to explore product from multiple perspectives, then synthesizes findings into actionable optimization plans. Can invoke competitors-analysis for competitive benchmarking. Use when "product audit", "self-review", "发布前审查", "产品分析", "analyze our product", "UX audit", or "信息架构审计".
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 facing 2+ independent tasks without a written plan, with no conflicting shared mutable state or sequential dependencies, where parallel delegation beats inline cost; otherwise inline. Planned tasks use subagent-driven-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。
Automated multi-agent orchestrator that spawns CLI subagents in parallel, coordinates via MCP Memory, and monitors progress. Use for orchestration, parallel execution, and automated multi-agent workflows.
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。
Analyze and, when the task authorizes source optimization, refactor a concrete long-running Python entrypoint containing repeated independent work. Use for explicit Python speedup or parallelization requests and evidence-backed long-running entrypoints; skip ordinary Python edits, test execution alone, short scripts, and projects that merely contain Python files.
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。
Dispatch independent tasks to parallel workers or subagents without write collisions. Use when: running or planning agents in parallel, even two; check scopes before any launch.
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。
Scaffold an isolated CLOUD dev box per agent (via crabbox + Daytona) for any codebase — the parallel-safe counterpart to dev-local-setup. Each agent gets its own full stack (own DB + dev server) and an in-box browser for e2e, so concurrent loops never collide on ports/state. Sets up the snapshot image, .crabbox.yaml, an idempotent setup.sh (also boots the stack locally), and a cbx.sh wrapper. Use when the user says "set up crabbox", "give each agent its own box", "add cloud testing", "make this repo testable in the cloud / on Daytona", "parallel-test this", or when setup-codebase-harness needs true per-agent isolation.
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。
Guide users or agents to the correct MATLAB tool for processing large tabular data in file-based formats (CSV, Parquet, delimited text, spreadsheets, MDF) that may not fit in memory. Use when a user or agent mentions large files, big data, out-of-memory errors, OOM, scaling up, tall arrays, datastores, or needs to process multiple tabular files. Covers the decision between datastore + tall, datastore + transform, and parallel execution. Also use when a user or agent has working in-memory code (readtable, parquetread) that runs out of memory and needs a migration path. Also covers building custom datastore classes for proprietary or non-standard formats — use when the task requires subclassing matlab.io.Datastore, implementing a custom reader, building an extensible datastore, or integrating a new file format with tall arrays or parallel computing. Do NOT use for MAT files (use matfile 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。
Multi-agent orchestration for complex tasks. Use when tasks require parallel work, multiple agents, or sophisticated coordination. Triggers include requests for features, reviews, refactoring, testing, documentation, or any work that benefits from decomposition into parallel subtasks. This skill defines how to orchestrate work using cc-mirror tasks for persistent dependency tracking and TodoWrite for real-time session visibility.
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。
Only to be triggered by explicit /parallel-task commands.
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。
Only to be triggered by explicit /parallel-task commands.
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。
Only to be triggered by explicit /parallel-task-spark commands.
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。
Parallel web scraping and data collection from multiple sources
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。
Manage parallel development with Git worktrees: creation with port allocation, environment sync, branch isolation, and cleanup. Use when working multiple branches at once, running parallel CI validations, or isolating agent workspaces.
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。
Multi-agent DAG orchestration for workflows where AI agents collaborate via dependency graphs, covering agent spawning, output merging, and quality evaluation. Use when a task needs multiple specialized agents or to parallelize AI work.
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 orchestrating Agent Teams for parallel development - team composition, task distribution, dependency management, and result aggregation. Triggers on /orchestrate, multi-agent coordination, parallel team work, task delegation to teammates.
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。
Parallel Agent Type Contracts
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。
Parallel Agent Orchestration
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。
Diagnose MSBuild build performance bottlenecks using binary log analysis. USE FOR: identifying why builds are slow by analyzing binlog performance summaries, detecting ResolveAssemblyReference (RAR) taking >5s, Roslyn analyzers consuming >30% of Csc time, single targets dominating >50% of build time, node utilization below 80%, excessive Copy tasks, NuGet restore running every build. Covers timeline analysis, Target/Task Performance Summary interpretation, and 7 common bottleneck categories. Use after build-perf-baseline has established measurements. DO NOT USE FOR: establishing initial baselines (use build-perf-baseline first), fixing incremental build issues (use incremental-build), parallelism tuning (use build-parallelism), non-MSBuild build systems.
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。