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概要と使いどころ
Build agents on OpenAI's platform patterns — assistants-style threads, function calling, structured outputs, and multi-step tool workflows. Use when working with OpenAI models as the agent engine.
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。
Manage fleets of local AI agents: lifecycle, monitoring, task assignment, and resource control. Use when running multiple CLI agents via an orchestrator like AI Maestro.
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 agents at runtime — task routing, scheduling, parallel execution, result aggregation, and supervision dashboards. Use when running agents as a managed fleet rather than one-off 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。
Build LLM applications with chain-style orchestration frameworks — prompts, chains, tool-calling agents, memory, and retrieval pipelines. Use when structuring multi-step LLM programs, adding tools to a model, or composing RAG systems in code.
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。
Manage fleets of local AI agents — spawn, supervise, route tasks, monitor progress, and shut down workers cleanly. Use when coordinating multiple agents on parallel workstreams.
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。
Coordinate agents via messaging: send, receive, and manage inter-agent communication. Use when agents need to collaborate, hand off work, or notify 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。
Design and build LLM agents: ReAct loops, tool schemas, planning, memory, guardrails, evals, and human-in-the-loop. Use when building autonomous multi-step AI systems.
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。
Coordinate teams of role-based agents — role definitions, task delegation, sequential and hierarchical workflows, and shared memory. Use when a job naturally splits into specialist roles collaborating.
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 guidance — agent architecture, tool design, planning loops, guardrails, evaluation, and production 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。
Complete B2B SaaS marketing agency powered by 90 AI agents across 17 specialties. This is the entry point for ALL marketing requests. Routes to the right specialist team: content marketing, SEO, paid media, social media, email marketing, design, sales enablement (including technical presales — demos, proofs of concept, security questionnaires), product marketing, account-based marketing (ABM), partnerships and channel programs (co-marketing, marketplace GTM, channel conflict and deal registration, partner economics), growth, analytics, project management, or client operations. Use this skill when the request spans multiple disciplines or when unsure which specialist to invoke. Also triggers on: marketing help, marketing team, marketing agency, what can you do, marketing capabilities.
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。
Synapse A2A agent communication — sending messages, spawning agents, delegating tasks, sharing memory, and coordinating file edits. Use this skill when: running synapse send/reply/broadcast/interrupt, spawning agents with synapse spawn or synapse team start, managing the task board with synapse tasks, sharing knowledge with synapse memory, locking files with synapse file-safety, checking agent status with synapse list/status, or orchestrating any multi-agent workflow.
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。
Comprehensive code review using specialized agents — orchestrates code, comments, tests, errors, types, and simplify agents sequentially.
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。
Guides AI agents in using Tree Ring Memory for durable recall, project decisions, user preferences, warnings, future seeds, privacy-safe memory capture, and lifecycle-aware forgetting.
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。
Fully autonomous bug hunting pipeline — discover bugs in a scoped area using parallel subagents, independently triage each finding, fix confirmed issues with subagents, then audit all fixes against repo constraints and target platforms. Runs end-to-end without user interaction.
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。
Catalog-then-fix slop removal on an existing prose draft. Parallel catalog subagents inventory four slop categories (theatrics, overwrought register, corrective antithesis, dramatic short sentences), fix subagents repair only what a catalog names, mechanical lint + frequency analysis bracket the run, and a regression gate (lint delta, word count, cold meaning check) decides whether the result ships. Use when the user says "deslop this draft", "catalog deslop", "strip the AI patterns from this file", or as the final stage of the article-factory and mosaic-writing skills. For writing or redrafting prose from scratch, use the prose skill instead - this skill only repairs an existing draft in place.
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。
**WORKFLOW SKILL** — Create custom VS Code Copilot agents (.agent.md). USE FOR: new agents, tool restrictions, handoffs, orchestration patterns, discipline constraints. DO NOT USE FOR: general coding, VS Code extension development.
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。
**WORKFLOW SKILL — MANDATORY for ANY hook work.** Create, configure, edit, debug, or review lifecycle hooks for VS Code Copilot (default) and Claude Code. USE FOR: criar/configurar/editar hook, hook do Copilot/Claude/agente, lifecycle hook, PreToolUse, PostToolUse, SessionStart, SubagentStop, Stop hook, UserPromptSubmit, PreCompact, bloquear comando, validar tool call, injetar contexto, guard, .agent.md frontmatter hooks, hooks.json, .github/hooks, .claude/settings.json, hook Node.js, cross-platform, hook não dispara/silencioso/ignorado, permissionDecision, hookSpecificOutput, additionalContext. **DEFAULT = VS Code GitHub Copilot.** Switch to Claude Code only when user explicitly says "Claude" OR workspace has `.claude/`. NEVER assume Claude. DO NOT USE FOR: general coding, creating agents (use agent-creator), creating skills (use skill-creator).
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。
Audit and fix agent readiness: the Lighthouse Agentic Browsing fraction, accessibility tree for agents, robots.txt and Content-Signal for AI agents, WAF treatment of agent traffic, llms.txt, Markdown delivery, ai-catalog.json, /.well-known discovery files, and WebMCP tools. Exclude AI citability and brand signals (seo-geo) and commerce protocol depth (seo-ecommerce).
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 you need Codex to coordinate multiple agents through Agent Relay for peer-to-peer messaging, lead/worker handoffs, or shared status tracking across sub-agents and terminals.
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。
Designs, builds, and deploys AI agents or multi-agent systems on Google Cloud. Provides an interactive workflow to gather requirements, recommend a tailored architecture, and generate deployment instructions. Use when designing or implementing agentic systems on Google Cloud. Don't use for general Google Cloud solution architecture (use google-cloud-solution-architecture instead) or for narrow tasks targeting a single product without agent 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。
Discovers requirements and designs a borderless open data lakehouse using Lakehouse for Apache Iceberg and BigQuery data agents. Use when architecting multi-cloud storage infrastructure (Cloud Storage, AWS S3, Azure Blob), establishing ingestion and AI serving subsystems, configuring Cross-Cloud Interconnect, or deploying Gemini Enterprise Agent Platform and BigQuery data agents. Don't use for single-cloud data warehouses, or when the focus is on Knowledge Catalog metadata governance and Spark-driven IDE analytics workflows (use google-cloud-solution-agentic-analytics-spark-knowledge-catalog instead).
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。
Onboarding entrypoint for agents-cli in Agent Platform. It should be used when the user wants to "create a new agent", "develop an agent", "build an agent using ADK", "run the agent locally", "debug agent code", "test an agent", "evaluate an agent", "deploy an agent", "publish an agent", "monitor an agent", or needs the ADK (Agent Development Kit) development lifecycle.
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。
Configures best-practice alerting policies for AI agents using OpenTelemetry (OTel) metrics, generating output as Terraform (.tf) configuration files. Use when analyzing, writing, or deploying alerting policies to monitor agent latency, error rates, token usage, and quality metrics. Don't use for standard infrastructure monitoring unrelated to AI agents, or when the agent is not instrumented with OpenTelemetry (for Reliability, Cost, Safety, Security alerts). NOTE: Reliability, Cost, Safety, and Security alerts use generic OTel metrics and work across runtimes (such as Cloud Run, Vertex AI). Quality alerts rely on Vertex AI Online Monitors and are strictly bound to Vertex AI deployments.
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。
Authoring and managing Agentforce agent definitions using the declarative Agent Script DSL (.agent files) and associated metadata types, including the Agent Script language for the new Agentforce Builder (hybrid-reasoning -> logic vs | prompt instructions, @actions/@subagent/@variables references, subagents). Use when creating agents in source control, writing or debugging Agent Script, debugging agent metadata, or understanding the metadata lifecycle of GenAiPlugin/GenAiPlanner/BotVersion types. Triggers: 'how do I deploy an Agentforce agent using source control', 'convert an agent topic to a subagent', 'what metadata types make up an Agentforce agent', 'agent test run command failing in CI pipeline', 'GenAiPlugin vs GenAiPlanner metadata relationship', 'Agent Script logic vs prompt instructions'. NOT for Apex-based agent actions — use agentforce/custom-agent-actions-apex. NOT for clicking the agent together in the Builder UI — use agentforce/agentforce-agent-creation.
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。