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概要と使いどころ
Multi-agent orchestration using dmux (tmux pane manager for AI agents). Patterns for parallel agent workflows across Claude Code, Codex, OpenCode, and other harnesses. Use when running multiple agent sessions in parallel or coordinating multi-agent development 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。
Orchestrates multi-agent workflows by delegating ALL tasks to spawned subagents via /spawn command. Parallelizes independent work, supervises execution, tracks progress in UUID-based output directories, and generates summary reports. Never executes tasks directly. Triggers on keywords: orchestrate, manage agents, spawn agents, parallel tasks, coordinate agents, multi-agent, orc, delegate tasks
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 for cheap parallel web discovery (gpt-5.6-luna). Triggers: Luna search, parallel research, 루나검색, 루나 서치, 병렬 웹검색, 싸게 많이 찾아봐.
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。
Orchestrate multi-LLM parallel debate and synthesis. Dispatches prompts to available omega CLI wrappers in parallel, collects independent responses, runs anonymized peer review ranking, and synthesizes via a chairman model. Supports collaboration templates, idle watchdog, inter-agent messaging, worktree isolation, and multi-turn sessions. No server required.
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。
After /start-task: park the current clean, pushed feature branch in its own issue-numbered nested worktree (.worktrees/<issue#>) and return the primary checkout to current main, so the next parallel lane can start. Use when the user says to move the branch or task to a worktree or open a parallel lane.
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 map-reduce bulk ingest for large source sets. Map phase dispatches read-only knowledge-extractor agents (≤N-wide, default Haiku) emitting per-source JSON extracts; Python routes extracts by target library file (fuzzy-merging new topics, pre-allocating new files, flagging oversized topics); reduce phase dispatches one agent-knowledge-updater per file (parallel, one writer per file); single shelf-index rebuild + one log.md entry. Resumable. Supersedes kb-ingest-batch.
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。
This skill should be used when the user asks to "create a team", "spawn agents", "parallel execution", "delegate tasks to agents", "use a swarm", "team workflow", "split work across agents", "run tasks in parallel", or mentions wanting multiple agents to collaborate on a complex task.
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。
Meta-agent supervisor that manages a fleet of Claude Code agents running in tmux windows. Auto-discovers spare worktrees, spawns agents, monitors state, kicks idle agents, approves safe confirmations, and recycles worktrees when done. TRIGGER when user asks to supervise agents, run parallel tasks, manage worktrees, check agent status, or orchestrate parallel 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。
Parallel SDLC batch runner. Analyzes a list of independent task descriptions, groups them by heuristic file-conflict prediction, dispatches one worktree-isolated background agent per task (each running the full pipeline-orchestrator: BA → Dev → QA → Sec → Docs), and aggregates results into a single batch summary. Use when: - `/sdlc:batch` invokes this skill after parsing its arguments (Step 2 of commands/batch.md) - You have 2+ independent feature/fix descriptions to run through the SDLC pipeline in parallel Do NOT use for: - A single task (use pipeline-orchestrator directly via /sdlc:start) - Tasks that must share a single working tree (this skill isolates each task in its own git worktree)
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。
Next.js App Router routing primitives: file-based routes, dynamic and catch-all segments, route groups, parallel routes, intercepting routes, middleware, programmatic navigation, link patterns. Use this skill to: - Pick the correct dynamic segment syntax for the route shape. - Use route groups to organize without affecting URLs. - Implement parallel/intercepting routes for modal-style navigation. - Build effective middleware for auth, redirects, A/B tests. - Use Link and useRouter correctly. Do NOT use this skill for: - Data fetching per route (see nextjs-data-fetching). - General file conventions (see nextjs-conventions). - RSC vs Client (see server-component-patterns).
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。
On-brand visual marketing content using Paper MCP and parallel agent teams. Reads the project's brand/ directory to build a design system, then spawns designer agents in parallel — each creating a unique artboard with a different on-brand layout interpretation. Intelligently adapts agent count and approach based on user goals via AskUserQuestion interrogation. Produces Instagram carousels, TikTok slideshows, social posts, story slides, and visual assets. When used with content spec YAMLs from /slideshow-script, each agent gets a unique script AND unique design direction. Make sure to use this skill whenever the user wants visual marketing content designed in Paper — carousels, slideshows, social posts, story slides, or any visual asset. Even if they just say "design something" or "make slides" or "create visual content", this is the skill. Also triggers on "paper marketing", "instagram design", "TikTok design", "slideshow design", "create carousel", or "social graphics".
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 or investigations that can be worked on without shared state or sequential dependencies — fan out to specialized sub-agents with isolated context, then synthesize the results.
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 implementing delays that must survive process restarts (e.g., 24-hour cart abandonment, scheduled follow-ups), waiting for human approval or external events with timeouts (review gates, webhook callbacks, async API completion), polling external services without losing state on crashes, calling other functions and awaiting their results, memoizing expensive operations so they don't re-run on retry, or running async work in parallel inside a workflow. Covers Inngest step methods: step.run, step.sleep, step.waitForEvent, step.waitForSignal, step.sendEvent, step.invoke, step.ai, plus patterns for loops and parallel execution.
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。
Searching internet for technical documentation using llms.txt standard, GitHub repositories via Repomix, and parallel exploration. Use when user needs: (1) Latest documentation for libraries/frameworks, (2) Documentation in llms.txt format, (3) GitHub repository analysis, (4) Documentation without direct llms.txt support, (5) Multiple documentation sources in parallel
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。
Split independent work across parallel agents with explicit boundaries and a parent integration review. Use only when user allows subagents and tasks do not share mutable 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。
Use when legacy prompts or older framework flows reference `dispatching-parallel-agents` and you need the modern runtime to resolve that capability cleanly.
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。
Runs synapse-docs, /simplify, sync-plugin-skills, and github-pages-sync in parallel for synapse-a2a development workflows. Use when you need doc updates, code simplification, plugin skill sync, and site-docs sync at the same time.
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。
Analysis of Competing Hypotheses. Operationalizes Richards Heuer's CIA-tradition technique for systematically narrowing among multiple hypotheses against evidence. Builds an explicit hypothesis-vs-evidence matrix and focuses on disconfirmation rather than confirmation — the central insight: hypotheses cannot be proven, only disconfirmed; the surviving hypothesis is the one with the least disconfirming evidence. Spawns parallel hypothesizers (in isolation, across hypothesis-generation angles) and parallel evidence-gatherers (across evidence classes), then synthesizes into a matrix, diagnosticity analysis, sensitivity analysis, and falsification milestones. Produces feedback only — no code, no tickets, no artifacts.
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 using dmux (tmux pane manager for AI agents). Patterns for parallel agent workflows across Claude Code, Codex, OpenCode, and other harnesses. Use when running multiple agent sessions in parallel or coordinating multi-agent development 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。
parallel-overlay-receipt-parity
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 this skill whenever a task involves running parallel work on a git repository without conflicts — e.g. "fix this bug while I keep working on my feature", "run Claude on two branches at once", "work on this task in isolation", "create a throwaway branch for this experiment", or anything where two agents or sessions might edit the same files. Also trigger when the user asks to use worktrees directly, or when an agent needs its own isolated checkout so it can work without disturbing the main branch. Even if the user just says "do this in parallel" or "don't mess up my current work", consider worktrees.
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。
Grind through ready beads in priority order by running several issues at once, each in its own isolated git worktree managed by worktrunk (`wt`), then merging the finished branches back one at a time. Use when asked to work the beads queue in parallel, to run N issues concurrently, or when a serial grind is too slow and the ready issues are independent.
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。
优化域入口（L2）：拿到**已明确的瓶颈点 / 瓶颈标签**后，按标签把任务**分发**到四个优化模块 （DiT 计算 `dit-perf-opt` / DiT 通信 `dit-parallel-opt` / VAE `vae-opt` / host `host-opt`），并给出域内**最小前置集**与**域级验收口径**（性能入库口径引 `perf-gate`、精度判据引 `accuracy-gate`）。 **本技能不再承载选档与实施内容**（Step 2–4 闭环、特性档位选择、组合试验已全部归 `dit-perf-opt`）。 入口信号（任一即可触发）：① 用户或编排层已给出**瓶颈点/瓶颈标签**（"瓶颈已经明确，按这个点优化"）； ② **框架侧特性没落地**（该开的开关/特性没开，需要先判走哪个模块）。 near-miss（看似相关但不属本技能）： - **瓶颈未明**（"这个模型怎么加速""怎么跑通""采个 profile"，还要先定位）→ 先走 `model-auto-optimization`（唯一有分析权的一方），本入口**不接受未定位的任务**。 - 问"要不要开量化、开哪一档""量化/稀疏/Cache 怎么选怎么开" → `dit-perf-opt`（选档与实施在模块层）。 - 多卡并行形态 / 通信掩盖 / TP·offload → `dit-parallel-opt`；VAE·TAE 解码段 → `vae-opt`；交付搬运 / 装载预热等固定开销 → `host-opt`。 - 需要改本仓代码（pattern / 算子 / 测试 / 文档）→ `dev-workflow`。 由 model-auto-optimization 的阶段路由与用户直接声明瓶颈两条路径触发。
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。