「agent」の検索結果

Explain how email and Google Calendar work for OpenClaw agents, including the distinction between agent email and owner email, how gog and the Google Workspace skill are used, where related credentials/config live, and how to troubleshoot common setup or access issues. Use when someone asks how an OpenClaw agent accesses Gmail/Google Workspace/Calendar, where credentials are stored, how to use the agent inbox, how to give the agent write access to the owner's calendar, or how to guide another agent/user through the email or calendar setup model.

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。

Step-by-step setup and troubleshooting for connecting an owner's Google Calendar to an OpenClaw agent with read and write access. Use when: setting up calendar access for the first time, fixing read-only access, re-authenticating a broken Google connection, explaining the difference between the owner's Google account and the agent's Google account, how an OpenClaw agent accesses Gmail or Google Workspace, where credentials are stored, how to use the agent inbox, how to give the agent write access to the owner's calendar, or guiding another agent or user through the email or calendar setup model.

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 troubleshoot Ocana agents. Use when creating a new Ocana agent, refreshing/repairing management token auth, diagnosing `ocana_agent_create` failures, checking `gatewayReachable`, handling agents stuck in `provisioning`, debugging `226/NAMESPACE` or missing `/usr/bin/openclaw`, or testing lifecycle/reprovision flows for a newly created agent.

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。

Agent architect v2 — multi-agent, tool use, memory, planning, Hermes

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。

Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when: langgraph, langchain agent, stateful agent, agent graph, react agent.

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。

This skill should be used when the user asks to "create AGENTS.md", "update AGENTS.md", "maintain agent docs", "set up CLAUDE.md", or needs to keep agent instructions concise. Enforces research-backed best practices for minimal, high-signal agent documentation.

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 setting up a secure email inbox for any AI agent — configuring inbound email via Resend, webhooks, tunneling for local development, and implementing security measures to prevent prompt injection attacks. Also use when someone mentions 'agent email', 'bot inbox', 'receive emails for agent', 'agent webhook', 'email security for AI', 'prompt injection via email', 'inbound email for bot', or wants their AI agent to receive and respond to emails securely.

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 a new Hydra agent with full configuration. Use when user wants to add an agent, new agent, create agent, or set up another agent. Walks through persona, mounts, Telegram, model, and secrets.

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。

Read and drive Agentic Table (magic table / due-diligence) sheets through the unique-cli agentic-table command. Use when the user or task involves an Agentic Table: inspecting a sheet's state, a cell's value or lock state, a cell's edit history or its export artifacts; writing text into a specific cell; or running the full loop — creating a sheet, importing a questionnaire and sources, waiting for the agent to answer, and exporting the result. Access is enforced per sheet by the platform and varies from sheet to sheet; a denial is reported as `agentic-table: permission denied`.

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。

Orchestrates group discussions between installed BMAD agents, enabling natural multi-agent conversations where each agent is a real subagent with independent thinking. Use when user requests party mode, wants multiple agent perspectives, group discussion, roundtable, or multi-agent conversation about their project.

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。

Packages an agent for CARLA Leaderboard submission — builds the docker image with make_docker.sh (plain or ROS variant), checks the four required roots and the egg layout it assumes, sets the agent/track/routes environment inside the image, and verifies the image can import the stack and load the agent before submission. Use when the user asks to "build the leaderboard docker image", "submit to the leaderboard", "package my agent", or make_docker.sh fails.

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。

Drives a vehicle to a destination autonomously using CARLA's navigation agents (BasicAgent/BehaviorAgent/ConstantVelocityAgent), or plans a route between two points with GlobalRoutePlanner. The agent handles routing, obstacle avoidance and traffic-light obedience. Use when the user asks to "drive the ego to (x,y,z)", "navigate to the junction", "go to that location", "plan a route from A to B", or "make the car drive itself somewhere specific". Manual control is control-vehicle; autopilot roaming is control-traffic.

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。

スキルの新規作成・更新を支援するガイド。AIの能力を拡張するスキルパッケージの設計・実装・配布までをカバーする。SkillとSubAgentのどちらが適切かを判断し、SubAgentが適切な場合は subagent-creator へ誘導する。「スキルを作成して」「これをスキル化して」「新しいスキルを作りたい」「スキルを更新して」などのリクエスト時に使用。

Herdr（コーディングエージェント向けターミナルマルチプレクサ）の操作と、herdr CLI の構文・ID 規約・agent 状態モデル（idle / working / blocked / done）の真実源。pane・tab・workspace・terminal・command の確認や操作、別 pane での agent 起動、pane 上の agent との通信の時に使用。HERDR_ENV=1 必須。境界: pane へ委譲するかどうか・経路・モデル・指示書・結果回収の方針は context/delegation.md（本スキルは CLI のみ）。

Configure and orchestrate parallel agents using the standalone Antigravity 2.0 Agent Manager and Antigravity IDE.

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。

Give the agent its own dedicated email inbox via AgentMail. Send, receive, and manage email autonomously using agent-owned email addresses (e.g. hermes-agent@agentmail.to).

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 a self-improvement loop for a shipped product's AI agents — production traces feed evals, evals feed improvement proposals (prompts, playbooks, retrieval configs), and a human approval gate promotes changes with rollback. Technique-agnostic: chooses per project between ACE-style evolving playbooks, GEPA/MIPROv2 offline optimization, or simple eval-driven iteration via references/techniques.md. Load when the user asks to make my product's agents self-improving, learn from production traces, add a learning loop, evolve prompts or playbooks safely, promote agent improvements, or GEPA-style optimization. NOT harness-evolution (that improves the coding agent), NOT experimentation (product A/B tests), NOT agent-run-retro (dev-phase manual retros — this skill is the production-scale continuation).

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 a structured retrospective after development-phase runs of your product's agents — interview the owner in plain language about what went well and poorly, draft ranked improvement hypotheses, then design and run small n=1/n=2 experiments with pre-declared success criteria, guardrails, stop conditions, and a cost/ROI kill-switch. Load when the user says how did that run go, retro this run, the agent output was bad, what should we improve, draft hypotheses, run a small experiment, or after repeated dev runs of an agentic system produce uneven quality. Priority: output quality over performance over cost, each with diminishing-returns stops. NOT a product A/B test (experimentation), NOT coding-agent harness repair (harness-evolution), NOT production-scale learning (runtime-learning-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。

This skill should be used when the user asks to "create AGENTS.md", "update AGENTS.md", "maintain agent docs", "set up CLAUDE.md", or needs to keep agent instructions concise. Enforces research-backed best practices for minimal, high-signal agent documentation.

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。