「autonomous agents」の検索結果

Design multi-step AI agent architectures with tools, memory, and orchestration. TRIGGERS - Use when user wants to design AI agents, multi-agent systems, or autonomous AI workflows.

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Use when building autonomous LLM agents operating in live environments. Protects against prompt injection, tool abuse, data exfiltration, and runaway loops. Keywords: guardrails, prompt injection, IPI, safety, sandboxing, NeMo, LLM Guard.

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Use when building autonomous code editing, bug fixing, or software engineering agents. Keywords: SWE-agent, code agent, bug localization, patch generation, AST, diff, TDD loop, repository indexing.

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。

Check and process messages from autonomous AILANG agents. Use when starting a session, after agent handoffs, or when checking for completion notifications.

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。

Agentic workflow patterns for autonomous LLM reasoning. Use when building ReAct agents, implementing reasoning loops, or creating LLMs that plan and execute multi-step tasks.

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This skill should be used when the user asks to "create an agent", "write a subagent", "generate agent definition", "add agent to plugin", "write agent frontmatter", "create autonomous agent", "build subagent", needs agent structure guidance, YAML frontmatter configuration, invocation criteria with examples, or wants to add specialized subagents to Claude Code plugins with proper capabilities lists and tool access definitions.

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 autonomous task-driven agents with the BabyAGI loop — task creation, prioritization, and execution from a single objective.

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Architect autonomous agent systems — perception/action loops, tool design, planning strategies, memory, and multi-agent orchestration patterns. Use when designing an agent from scratch or scaling a prototype to production.

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Smallest personal narrated-explainer-video pipeline (spoken narration over visuals, not an audio podcast), fully tool-agnostic and autonomous by default — topic → research ∥ asset collection → script → TTS → video → 4K render ∥ publish info + cover. The skill defines the pipeline logic and self-verified checkpoints; any TTS backend and any video tool (Remotion, HyperFrames, CapCut, ...) work, and how much human oversight to apply is set by the working project's AGENTS.md/CLAUDE.md, not here. Use when the user wants a quick personal narrated video with minimal steps, whether or not they name the tool stack. Do NOT trigger for audio-only podcasts, written episodic content, or heavy multi-format production.

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 the current session as a senior developer (Opus) leading a configurable junior fleet. Decomposes requests into scoped tasks, dispatches junior-developer subagents in parallel, optionally runs an architect plan-review gate before dispatch, autonomously resolves escalations, optionally dispatches a doc-writer second wave after juniors complete, then integrates and gates with /test + /governance. Use when the user says "/dev-team", "run the dev team", "delegate this to the juniors", "act as the senior dev", or wants a task built by a senior-led fleet.

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Build a HyperFrames video composition from a brief/script autonomously. Uses separated builder and critic subagents — builders never review their own work. Quality decisions are grounded in project context (identity, audience, goals) and established visual/motion design principles. Measurable criteria (text size, contrast, overflow) are verified with tooling, not visual judgment. Use when the user says "make this video", "implement this brief", "build this composition", or provides a video script/brief to implement.

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 unattended resolution of open GitHub issues - triage by complexity, dispatch parallel worker agents into isolated worktrees, review their diffs, open PRs, and monitor CI and CodeRabbit until green. Use when asked to work through open issues autonomously or put Claude on auto-pilot.

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Generate a production-ready operations runbook for deploying AI agents. Covers pre-deployment checklists, shadow mode → supervised → autonomous rollout stages, monitoring dashboards, rollback proce...

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。

Autonomous advertising operations framework for AI agents managing campaigns across Google Ads, Meta, LinkedIn, TikTok, and programmatic.

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Design, build, deploy, and operate production AI agent systems — single agents, multi-agent teams, and autonomous swarms. Complete methodology from agent architecture through orchestration, memory systems, safety guardrails, and operational excellence.

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AI Agent skill marketplace integration for A2A Market. Enables agents to buy skills, sell skills, and earn money autonomously. Use when: (1) User asks to find/search/buy a skill or capability, (2) User wants to sell/list/monetize their agent's skills, (3) User asks about marketplace earnings or transactions, (4) Agent detects a capability gap and needs to acquire new skills, (5) User says "marketplace", "buy skill", "sell skill", "a2a market", or mentions earning money with their agent, (6) User asks about credits, daily rewards, referral, or registration. Supports x402 USDC payments on Base L2 and Credits payment system.

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。

Skill for discovering and researching autonomous AI agents, tools, and ecosystems using the AgentFolio directory.

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Framework de orquestração multi-agent para colaboração autônoma de IA. Use ao construir equipes de agentes especializados trabalhando juntos em tarefas complexas, quando você precisa de colaboração de agentes baseada em papéis com memória, ou para workflows em produção exigindo execução sequencial/hierárquica. Construído sem dependências de LangChain para execução enxuta e rápida.

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Agentes autônomos são sistemas de IA que conseguem decompor objetivos, planejar ações, executar ferramentas e se auto-corrigir sem orientação humana constante. O desafio não é torná-los capazes - é torná-los confiáveis. Cada decisão extra multiplica a probabilidade de falha. Esta habilidade cobre loops de agentes (ReAct, Plan-Execute), decomposição de objetivos, padrões de reflexão e confiabilidade em produção. Insight-chave: taxas de erro compostas matam agentes autônomos. Uma taxa de sucesso de 95% por passo cai para 60% b

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 production-ready Tavily integrations with best practices baked in. Reference documentation for developers using coding assistants (Claude Code, Cursor, etc.) to implement web search, content extraction, crawling, and research in agentic workflows, RAG systems, or autonomous 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。

Skill for discovering and researching autonomous AI agents, tools, and ecosystems using the AgentFolio directory.

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 multi-step tasks autonomously using a 3-tier agent hierarchy (Director/Manager/Worker). Use when user says "run this end-to-end", "handle everything", "execute all tasks", or needs parallel task execution with queuing, course correction, and session resume. Provides FIFO task queue, escalation policy, git worktree isolation, and context persistence across agent sessions.

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。

Skill for discovering and researching autonomous AI agents, tools, and ecosystems using the AgentFolio directory.

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。

Skill for discovering and researching autonomous AI agents, tools, and ecosystems using the AgentFolio directory.

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。