「coordinator」の検索結果

The checkpoint coordinator for /feature. Routed to by /feature once a writing-plans plan exists. Uses authored HTML checkpoints or bounded legacy batches, delegates each scope to subagent-driven-development (fresh author agent per task, full review chain, fresh verification), then stops for a human checkpoint before the next batch. The checkpointed counterpart to /sprint's autonomous run.

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Northbridge coordination pattern for multi-agent orchestration. Creates convoys, dispatches work via sling, monitors progress, and handles escalations. Never executes work directly.

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```yaml name: cognitive-surrogate description: Layer 6 Barton Cognitive Surrogate System - build, train, and validate psychological models that predict and generate authentic responses matching a subject's cognitive patterns version: 1.0.0 trit: 0 # Ergodic/coordinator role in GF(3) triadic system ```

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Orchestrates translation of motion designer video specifications into working Remotion code by coordinating specialized agents. Acts as pipeline coordinator that delegates to remotion-scaffold, remotion-animation, remotion-composition, and remotion-component-gen. Use when you have a complete VIDEO_SPEC.md and need full Remotion implementation.

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Bridges asset requirements from motion design specs to production-ready assets. Parses specs for required assets, recommends free/paid sources, provides format conversion guidance, generates validated import code, and offers asset preparation checklists. Use when preparing assets for Remotion projects or when asked "where to get assets", "how to prepare assets", "asset formats for Remotion".

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用于执行包含独立任务的实施计划，将任务分派给新的子代理，并在任务之间进行协调与审查。

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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.

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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.

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Master coordinator for the founder. Filters noise, owns cross-functional processes, enforces consistency, routes decisions, and positions outputs for impact so the founder can think clearly. Doesn't own any function owns the space between all of them.

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Auto-activates when coordinating sequential validation specialists with workflow management, dependency tracking, and overall validation status monitoring.

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Choose responsibility for substantial work before deep execution: keep one responsibility's coupled parts in a single lane, and hand a bounded or independent lane to a worker when clean context, an unknown cause, independent review, or coordinator attention materially improves the outcome.

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Organize month-end or quarter-end close blockers, reconciliations, owners, and evidence gaps into a routine control pack. Use when the work is finance close coordination and issue tracking. Do not use for accounting judgment, policy interpretation, or final technical accounting conclusions.

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Package interview loops, schedules, feedback follow-ups, and candidate next steps for recruiting operations. Use when the work is routine hiring coordination and recap packaging. Do not use for final hiring decisions, compensation judgment, or sensitive people-policy advice.

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Bug修复循环协调者。测试完成后，读取技术问题追踪台，按P0→P1→P2顺序协调修复，每修复一个触发针对性回归测试，循环直至P0+P1全清，输出可上线结论。所有信息通过文档传递，不依赖对话历史。触发词：「全自动修复循环」「按Bug清单修复」「修复所有问题」「循环修复到上线」「开始bug修复循环」。

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跨项目并行开发协调框架创建 Skill。当启动涉及2个或以上子项目并行开发时激活：收集项目清单，生成标准协调框架文档（项目地图/API契约/并行轨道/Day 1清单）。触发词：「多项目并行」「建协调框架」「几个项目同时做」「分工要明确」「并行开发」「各自独立启动」「RULE-37」。与元项目导航的区别：元项目导航是轻量指针文档，本Skill生成可操作的协调框架（含API契约和并行轨道）。

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Diagnoses GKE JobSet interruptions, restarts, and preemptions for AI/ML training workloads autonomously. Use when troubleshooting JobSet restart loops, spot VM preemptions, node readiness failures, host VM issues, or coordinator worker crashes. Don't use for general GKE cluster creation, basic workload deployment, or non-JobSet application issues.

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Use this skill when creating, scaffolding, reviewing, or maintaining Python Home Assistant custom integrations, including work on custom_components, manifests, config flows, options flows, config entries, platforms/entities, DataUpdateCoordinator, integration automation triggers, diagnostics, repairs, discovery/networking, authentication, HACS-ready repositories, or Home Assistant integration quality scale compliance.

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Coordinates code quality: ln-511 metrics, ln-512 cleanup, inline agent review (Codex + Gemini), ln-513 regression, ln-514 log analysis. Returns quality_verdict + aggregated results.

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File-based workflow coordinator for Greek accounting. Defines processing pipelines, validation rules, and routine templates. No external APIs needed.

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Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.

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Workflow Phase 6 — TDD coordinator. Decides the scenario recipe and the implementation contract in main context, writes them to a state file, seeds per-worker tasks (scenario, implement, verify-tick, design-ui-tick) into the TaskList, and yields with harness_state continue so the harness invokes each worker as its own tick. No subagent delegation; no nested Skill calls.

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Meta-skill that orchestrates the discourse skill suite: routes between steel-man-argument, socratic-questioning, toulmin-argument-analysis, logical-fallacy-detector, bad-faith-rhetoric-detector, discourse-elision-analyzer, forensic-speech-structure, and productive-discourse-facilitator. Use when analyzing conversations, facilitating discourse, or operating as a dialogue copilot. Maintains mode (silent analysis, coaching overlay, active facilitator) and decides which skills to invoke based on conversation dynamics.

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Lightweight multi-agent research orchestration using one coordinator, three domain leads, and three lightweight research agents. Use for medium-size research, market scans, competitor comparisons, prior-art style exploration, report planning, and any task that benefits from parallel domain decomposition without running a full high-cost research lab.

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End-to-end vendor procurement workflow orchestrating RFQ creation, email sending, vendor response tracking, and client-facing estimate generation. Use this skill when managing the full quote-request-to-client-estimate pipeline, coordinating between vendor-rfq-creator and vendor-estimate-creator skills with email automation and status tracking.

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。