「parallel」の検索結果

Optimize ChatGPT Voice in the Codex desktop app into an ear-first control plane for free-form task coordination and opt-in workflows. Apply spoken synthesis, routing-only coordination, owning-task role contracts, project placement, explicit authority, safe speech, current-state verification, honest recovery, and the optional Slopware Dev Stack: MSL, MSW, CODER Loop, and Timebox. Load named workflows only when invoked: Freeway for parallel lanes, Decision Walkthrough for read-only authority convergence, and Tutorial for teaching-first onboarding. Use when the user invokes the voice optimizer, coordinates Codex work through voice, assigns the current voice task as coordinator, runs Freeway, starts a decision walkthrough, learns voice orchestration, routes development through CODER, authorizes Timebox, or asks CVO to install or use Slopware companions.

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, executing, or running Plans.md tasks — single task, parallel workers, or full team/breezing run. Accepts specific task numbers or ranges. Do NOT load for: planning, review, release, or setup.

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。

Executes plans.json tasks — solo, parallel, or breezing team mode. Use when implementing tasks or running the work loop.

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 running the full team/breezing flow end-to-end — all tasks with parallel workers. Do NOT load for: single-task implementation, planning, review, release, or setup.

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。

Before any multi-step work with a stated goal — a feature, a bugfix spanning multiple files, a QA sweep, a doc draft + iterations, a multi-day investigation — create an ATO Mission instead of doing it via bare `ato dispatch` calls. A Mission persists the goal + the verifiable success criteria, lets the coordinator tick drive the work across days, captures every event in a structured audit trail (SQLite + markdown narrative), and integrates parallel agents' work via merge strategies. Complement to `ato-warroom` (the cross-family decision before you start) and `ato-review` (the post-code-diff review). Missions is where multi-step work LIVES; war-rooms are where decisions ABOUT it get made; reviews are where the resulting commits get vetted. Fires when: the work has more than one decision point, a verifiable end state, or runs across more than one session. Use it for any ATO development that doesn't fit in a single dispatch.

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。

Launches 5 parallel AI agents to produce a comprehensive restaurant audit with composite Restaurant Health Score (0-100), grade, and prioritized action plan

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。

End-to-end automated model quantization and accuracy tuning workflow. Use when user asks for automated model quantization and accuracy tuning, e.g. "自动量化", "量化调优", "一键量化", "精度调优", etc. 当用户给出多卡（≥2 卡）卡号时，量化阶段通过 EP 并行适配（msmodelslim-ep-parallel-adaptation）保证多卡 EP 并行，调优主流程仍在本 Skill。

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。

Interactively gather parameters for `python -m cli.inference.text_generate`, generate a concrete text generation simulation command, explain assumptions, ask for execution confirmation, then run the command and summarize performance metrics. Use when the user wants to validate a specific model, hardware profile, batch/query size, prefill or decode scenario, TP/DP/EP/MOE parallel strategy, profiling database, chrome trace, empirical metrics export, or a best row produced by throughput_optimizer.

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。

Interactively gather parameters for `python -m cli.inference.throughput_optimizer`, generate a deployment simulation command, explain assumptions, ask for execution confirmation, then run the simulation and summarize the best parallel strategy. Use when the user wants to evaluate a model on one or more hardware profiles, compare hardware targets, compare aggregation versus disaggregation versus PD ratio optimization, choose TP/EP/MOE-DP search ranges, or obtain recommended concurrency, throughput, TTFT, and TPOT results from throughput modeling.

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。

Verify NowStack Mobile iOS/Expo changes in the Apple Simulator with `xcrun simctl`. Covers single or parallel Metro/device flows, deep-link navigation, preview routes, and OTP sign-in. Use for `ns ios verification`, "test on simulator", or changed `mobile-app/**`; never use computer-use.

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。

AI Trading Analyst for Hermes — full stock analysis with 5 parallel subagents, investment thesis, technical/fundamental/sentiment/risk analysis, options strategies, portfolio review, stock screener, and PDF reports. Say "trade analyze AAPL", "trade quick NVDA", "trade screen growth", etc.

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。

Full Stock Analysis Orchestrator — launches 5 parallel subagents via delegate_task for comprehensive multi-dimensional stock analysis with composite Trade Score (0-100). Triggered by "trade analyze <TICKER>" or "analyze <TICKER> stock".

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。

Reverse-engineer any person's writing voice from their content. Paste in posts, articles, tweets, or essays and this skill launches 10 parallel Sonnet subagents to analyze every dimension of the voice, then synthesizes into a voice file. Use when someone says 'match this voice,' 'analyze this writing,' 'extract their voice,' 'make me sound like this,' 'study this person's writing,' or pastes in content they want to learn from.

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。

Enhance an existing marketing plan with parallel research. Use when the agent has a draft plan (brand strategy, campaign brief, content calendar, launch plan) that needs strengthening with real data. Triggers on 'deepen this plan', 'strengthen this strategy', 'research gaps in my plan', 'make this plan better', 'this plan is too surface level', 'add research to this', 'validate this plan', 'back this up with data', or when a plan exists but lacks audience data, competitive positioning, or keyword strategy. Make sure to use this whenever someone has an EXISTING plan that feels thin or unresearched — even if they just say 'is this plan good enough?' or 'what's missing here?', they likely need deepening. This skill ENHANCES existing plans — it does not create new ones. If no plan exists, route to /brainstorm or /launch-strategy first.

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。

Ongoing link-building campaigns that raise domain authority and expand a site's KD ceiling beyond what on-page work alone can earn. Use when the user mentions 'off-page SEO', 'link building', 'directory link building', 'backlink campaign', 'listicle placements', 'guest posts', 'outreach targets', 'get listed in listicles', or asks 'how do I get more domain authority'. Generates a prioritized, researched target list across directories, listicles, and guest-post candidates — does NOT submit anything on the user's behalf. Pairs with `mktg-backlink-prospector` agent for parallel research. For a launch-day push (submit to directories, Product Hunt, BetaList), see /startup-launcher. For measured backlink data — referring domains, backlink counts, rank tracking — see /openseo. Distinct from `seo-machine` (on-page programmatic) and `seo-audit` (technical diagnostic). The off-page half of the SEO operation.

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。

First-run conversational setup wizard for marketing-cli. Use when /cmo detects no populated brand files and no cmo-preferences.md, when the user just installed marketing-cli for the first time, or when they say things like "set up marketing", "start marketing", "marketing wizard", "I just installed marketing-cli", "onboard me", "first time", "initial setup". Asks 3 sharp questions (identity, posture, distribution preferences), records the answers to brand/cmo-preferences.md, then hands off to /cmo's foundation flow which spawns 3 research agents in parallel. The user never has to type /mktg-setup directly — /cmo auto-routes here on first run.

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。

Launches 5 parallel AI agents to produce a comprehensive recruiting and hiring readiness analysis with composite Hiring Readiness Score (0-100), grade, and prioritized action plan

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 research that keeps bringing gifts back — like cats! Dispatch multiple agents to research a topic in parallel, compile findings, and iterate on new discoveries.

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。

Lightweight refactor: audit → plan → approve → implement in a single Opus context, no sub-agent fan-out. Behavior preservation is the goal — verifies tests still pass, optionally commits, and offers a separate /code-review pass at the end. Use by default; reach for /refactor only when the cleanup needs parallel fan-out or exceeds one context.

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。

Lightweight build: explore → plan → approve → implement in a single Opus context, no sub-agent fan-out. Verifies build + tests, optionally commits, and offers a separate /code-review pass at the end. Use by default for everyday features; reach for /claude-setup:build only when the work needs parallel fan-out or exceeds one context.

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。

Lightweight build: explore → plan → approve → implement in a single Opus context, no sub-agent fan-out. Verifies build + tests, optionally commits, and offers a separate /code-review pass at the end. Use by default for everyday features; reach for /build only when the work needs parallel fan-out or exceeds one context.

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。

Lightweight refactor: audit → plan → approve → implement in a single Opus context, no sub-agent fan-out. Behavior preservation is the goal — verifies tests still pass, optionally commits, and offers a separate /code-review pass at the end. Use by default; reach for /claude-setup:refactor only when the cleanup needs parallel fan-out or exceeds one context.

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。

Refactoring pipeline: analyzes a target file or directory for code quality issues, plans safe incremental improvements, optionally writes tests first as a safety net, implements the changes, and reviews the result. Orchestrates refactor-planner → (test-writer) → parallel-task-orchestrator → code-reviewer.

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。

Work with the @upstash/box TypeScript/JavaScript SDK for sandboxed cloud containers with AI agents, shell, filesystem, git, cron schedules, snapshots, and a headless browser. Use when building with Upstash Box, creating a sandbox or isolated environment to run untrusted or agent-generated code, running AI coding agents in containers, giving an agent a cloud dev environment with a shell and repository, browser automation from a box, scheduling recurring jobs inside a box, saving and restoring snapshots, or orchestrating parallel boxes.

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。