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概要と使いどころ
Review Agiflow Planning tasks for readiness, prioritize approved work, group related tasks into work units, and promote ready tasks to Todo. Use when grooming a backlog, organizing planned tasks, creating work units, or deciding what should be executed next.
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Pipeline orchestration: dispatch the highest-priority ready tasks/work units to agents, manage capacity, and coordinate the Todo to Done flow. Invoked as /agiflow:orchestrate. Uses list_tasks, list_active_tasks_by_org, list_members, update_task, get_work_unit_progress.
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。
Execute a work unit end-to-end: sequence tasks by dependency, implement, test between tasks, commit, and track progress. Use to deliver a complete feature in one session. Invoked as /agiflow:run-work <work-unit>. Uses get_work_unit, list_tasks, update_task, get_work_unit_progress.
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。
Plan and run usability tests on existing or prototype designs including test design, task scripts, moderation, observation, and findings synthesis. Use this skill whenever the user wants to test usability, run a moderated test, run an unmoderated test, validate a design, find usability issues, or improve task completion. Triggers on usability test, usability testing, moderated test, unmoderated test, task script, think aloud, prototype testing, user testing, design validation, task completion. Also triggers when the user has built something and wants to know if real users can use it before shipping.
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。
Initializes an AI development session by reading workflow guides, developer identity, git status, active tasks, and project guidelines from .trellis/. Classifies incoming tasks and routes to brainstorm, direct edit, or task workflow. Use when beginning a new coding session, resuming work, starting a new task, or re-establishing project 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。
Run fable-mode execution discipline on Claude Fable 5.1 — the top of the escalation ladder and the strongest staged run available. Routes the task to the @fable-orchestrator agent with its model overridden to Fable (`model: "fable"`), which stages the work, delegates artifact production to @fable-worker-sonnet / @fable-worker-haiku, and cold-checks deliverables with @fable-verifier. Trigger when the user explicitly asks for thorough/systematic/"deep work" handling on the strongest model ("fable on fable", "stage this on fable", "deep work mode, fable", "run this on 5.1"), or when a fable-opus run reports the task exceeds Opus's ceiling. Benchmarked 2026-09-02: zero correctness delta on Fable 5.1 at ~1.1× tokens, ~2× wall — a provenance runner, not an accuracy runner. Do NOT use for ordinary single-pass tasks — prefer fable-opus, fable-sonnet, or fable-haiku when the task doesn't need peak reasoning. Fable is plan-included; bulk work still routes down-tier.
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。
Modify a classroom task with language scaffolds that preserve cognitive demand for EAL learners. Use when adapting existing tasks for students at different English proficiency levels.
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。
Expert knowledge for Azure Durable Task development including best practices, decision making, architecture & design patterns, limits & quotas, configuration, integrations & coding patterns, and deployment. Use when choosing Durable storage backends, versioning orchestrations, using fan-out/fan-in, human approvals, or instance APIs, and other Azure Durable Task related development tasks. Not for Azure Functions (use azure-functions), Azure Logic Apps (use azure-logic-apps), Azure App Service (use azure-app-service), Azure Service Fabric (use azure-service-fabric).
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。
Delegate work to the TaskMarket agent-worker market - browse open tasks, create tasks with explicit authorization, and track/submit work, so an agent can outsource instead of burning inference on unreliable or low-confidence 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。
Review local code changes for bugs, regressions, missing tests, and pragmatic improvements. Use when the user asks to review the current changes from `git status`, or to review a specific backlog task by id such as `task-65` or `TASK-65`. Also trigger when the user says things like "review", "review current changes", "check my changes", "look over the diff", or "review task-42".
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。
Judge how a memory changes active-task state and whether it refers to an existing task. Use for mem-claw task-lifecycle model calls.
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。
Zero-config goal-to-tasks engine. Takes any goal (software, pentest, business, learning), runs adaptive discovery, generates a validated spec, parses into TaskMaster tasks, creates an implementation plan, and executes with built-in CDD verification. Use when user says "PRD", "product requirements", "I want to build", or any goal-driven phrase.
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。
Customise the prd-taskmaster plugin workflow via curated brainstorm questions. The AI asks, the user answers in plain English, and the skill writes their preferences to .atlas-ai/config/atlas.json. Future runs of prd-taskmaster read that file and apply user preferences to phase gates, validation strictness, default provider, preferred execution mode, and template choice. For deeper tweaks beyond the curated questions, users can hand-edit files in .atlas-ai/customizations/. Use when the user says "customise workflow", "customize workflow", "adjust my PRD settings", "tune the skill", or wants to change how prd-taskmaster behaves.
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。
Phase 3 of the prd-taskmaster pipeline: smart mode selection and user handoff. Detects installed capabilities (superpowers, ralph-loop, task-master-ai, playwright, research providers), recommends ONE execution mode (A/B/C) with reasoned justification, appends the task-execution workflow to CLAUDE.md, surfaces a structured AskUserQuestion multi-option picker for user agency, and dispatches the chosen mode. Mode D (Atlas Fleet) is selectable only when detect_capabilities returns tier=premium (licensed atlas-launcher detected); otherwise it is a locked Atlas Pro teaser. Plan Mode is NOT used (spec section 13.5): AskUserQuestion is the sole user-agency mechanism. Declares HANDOFF complete so EXECUTE can follow.
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 the user says I want to add X, implement Y, create a task for Z, or plan this feature. Writes a task file plus a PRD under .agent/, links them, and waits for approval before any implementation starts.
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。
Volcengine Cloud Detect (云拨测) API integration for creating detection tasks, retrieving results, generating reports, and managing detection nodes. Use when the user wants to (1) create network detection/dialing tasks (拨测任务), (2) check task results or generate detection reports, (3) list or search detection nodes (拨测节点), (4) manage existing tasks (list/stop/restart/delete). Triggers include natural language requests about website monitoring, network latency testing, HTTP detection, ping tests, DNS tests, node queries, or report generation for Volcengine cloud detect service.
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。
Create and check long-running video material evaluation tasks. Use this skill when the user wants to submit videos for evaluation, check an existing video evaluation task list, or fetch the result of a previously created video evaluation task. This skill is not a general-purpose video upload skill because upload is allowed only as an internal step of task creation. Authentication uses an API key passed as an Authorization bearer token.
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 reviewing whether an OpenSpec change's execution goal, acceptance criteria, and implementation or verification tasks are aligned. This should trigger for requests such as Review this OpenSpec change with ATDD; Check acceptance criteria against tasks; Find acceptance criteria without task coverage; Detect tasks that diverge from the execution goal; Explain what is missing, vague, ambiguous, partial, absent, or divergent in this OpenSpec change. Part of Plinth Toolkit
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。
Extracts executable task definitions from Markdown files and runs them as shell pipelines. Parses fenced code blocks with task metadata annotations and manages dependencies between 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。
Breaks work into ordered tasks. Use when you have a spec or clear requirements and need to break work into implementable tasks. Use when a task feels too large to start, when you need to estimate scope, or when parallel work is possible.
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。
Repo-local workflow skill for large coding tasks. Initializes .agent/tasks/TASK_ID artifacts, installs project-scoped Codex and Claude subagents, updates AGENTS.md plus the repo's Claude guide file with the workflow, and runs a spec-freeze → build → evidence → verify → fix loop with fresh-session verification.
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 approved, bounded work through a same-directory Codex App fork: create and name the execution task, ask it to run spec-executor, route decisions through Codex Task Messenger, validate the returned receipt, and archive a completed child. The source may come from any planning workflow. Requires Codex App task tools and codex-task-messenger.
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 background tasks in Python with Celery. Use when a user asks to process tasks asynchronously, schedule periodic jobs, run background workers, build task queues in Python, or offload heavy processing from web requests.
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。