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概要と使いどころ
Delegate routine work to subagents — boilerplate generation, data transformation, file analysis, documentation drafting. Use when splitting tasks into independent subtasks for parallel subagent 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。
Discover, validate, and invoke .agent.md custom agents. Use when finding agent files in the local Claude or VS Code Insiders directories, checking frontmatter, verifying disable-model-invocation, or determining agentName for runSubagent 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。
Map tasks to specialist agents. Use when choosing which agent for a job, comparing agent capabilities, or routing to React/Next.js/Playwright/docs/code-quality experts. Keywords: which agent, best agent for this, delegate to expert, agent capability mapping.
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 en workflow Claude Code (commands, skills, subagents, CLAUDE.md, best practices)
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 en orchestration d'agents autonomes (CrewAI, AutoGen, LangGraph, swarm patterns, planning)
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 en tests d'agents IA (tests unitaires, intégration, régression, guardrails, red teaming agents)
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。
Agent Orchestrator v2 IA — Expert en orchestration multi-agents (tool routing, mémoire, planning, ReAct, orchestration de workflows IA)
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 en déploiement d'agents IA (containerisation, scaling, monitoring, versioning, A/B testing agents)
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 maintain AGENTS.md files for project onboarding and grounding. Use when initialized in a new project, when project structure changes significantly, or when creating subfolder-level AGENTS.md files for complex monorepos.
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。
Decomposes a large implementation plan into small-model-sized tasks executed by supervised worker agents in dependency-ordered parallel waves, with a plan-first approval gate inside every task, file-based state for context compaction, and a hard escalation ladder. Use when a master plan or multi-workstream feature is too large for one session and should be executed cheaply by many small agents under frontier-model supervision.
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 executing implementation plans with independent tasks in the current session — trigger phrases "execute the plan", "independent tasks", "parallel tasks", "subagents for the plan","split the plan into subagents". Dispatches a fresh subagent per task with two-stage review after each: spec compliance first, then code quality.
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 asked to compare, cross-review, merge, judge, choose, or arbitrate competing plans from multiple agents such as Codex and Claude Code; when given two or more proposed plans, session IDs, transcripts, plan documents, PR descriptions, or pasted strategies; or when the user wants one recommended execution plan after agents review each other's proposals.
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 — "in parallel", "independent tasks", "multiple unrelated failures", "different test files or subsystems" — that can be worked on without shared state or sequential dependencies. Dispatch one agent per independent slice, each with only the context it needs, even when the user did not ask for subagents explicitly.
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 a task needs a small, throwaway multi-agent fan-out (a couple of parallel workers plus a review pass) in ANY project — trigger phrases "spawn a few parallel agents", "fan out", "quick worker team" — without the full /startcycle-graph contract, no .agents/graph.md bootstrap, no state.json schema, no persistent files. Roles run on the session model unless `.aos/pipeline.json` overrides one (an external CLI can take the worker step) and portable across machines that may not have Antigravity/OpenCode/Codex installed.
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 agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.
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 autonomous multi-agent build pipeline after /bdbrainstorm or /grill-me, when the user types /startcycle-graph or "run a startcycle", or when a task needs durable state, a review repair loop and an automated quality gate. Routes to the startcycle-dispatch Dynamic Workflow, which reads production_artifacts/state.json and invokes Architect, TechLead, UI/UX, Engineering, Media/EventTech, Reviewer, and Shipping in turn per .agents/graph.md's edge table — the agents never invoke each other.
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 /syncskills, sync agent skills, update .agents skills from .codex, .cursor, or .claude, or asks to make .agents/skills include skills from other agent folders.
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。
Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it's achieving natural conversation flow with sub-800ms latency while handling interruptions, background noise, and emotional nuance. This skill covers two architectures: speech-to-speech (OpenAI Realtime API, lowest latency, most natural) and pipeline (STT→LLM→TTS, more control, easier to debug). Key insight: latency is the constraint. Hu
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 in building voice AI applications - from real-time voice agents to voice-enabled apps. Covers OpenAI Realtime API, Vapi for voice agents, Deepgram for transcription, ElevenLabs for synthesis, LiveKit for real-time infrastructure, and WebRTC fundamentals. Knows how to build low-latency, production-ready voice experiences. Use when: voice ai, voice agent, speech to text, text to speech, realtime voice.
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。
Build AI agents that interact with computers like humans do - viewing screens, moving cursors, clicking buttons, and typing text. Covers Anthropic's Computer Use, OpenAI's Operator/CUA, and open-source alternatives. Critical focus on sandboxing, security, and handling the unique challenges of vision-based control. Use when: computer use, desktop automation agent, screen control AI, vision-based agent, GUI automation.
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 in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies. Covers agent design with roles and goals, task definition, crew orchestration, process types (sequential, hierarchical, parallel), memory systems, and flows for complex workflows. Essential for building collaborative AI agent teams. Use when: crewai, multi-agent team, agent roles, crew of agents, role-based agents.
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 in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build agent, AI agent, autonomous agent, tool use, function calling.
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。
Design patterns for building autonomous coding agents. Covers tool integration, permission systems, browser automation, and human-in-the-loop workflows. Use when building AI agents, designing tool APIs, implementing permission systems, or creating autonomous coding assistants.
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 agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.
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。