「agents」の検索結果

Agents work in parallel on the same task; judges evaluate and select the winner.

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 two-agent proxy approval council for autonomous sprint gates. Use when a sprint needs delegated approval for human gates such as deploy, live mutation, push, cleanup, skip, or shortened watch decisions, while requiring two independent agents to reach consensus that the human principal would approve under written policy and preferences.

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。

Combine independent work from two or more selected agents into one decision-ready research report, design, or proposal. Preserve useful complementary findings, develop stronger combined options, and present material alternatives only when they exist. Use when multiple perspectives should improve the product without forcing consensus or staging disagreement.

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。

Method for adding agent comments and work to a shared file without overlapping other agents. Creates isolated workspaces per agent with UTC timestamps. Use when told to add comments, reviews, or work to any file.

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。

All agents have equal voice; decisions made by majority vote.

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。

Role-based collaboration pattern (Drafter, Reviewer, Breaker). Assigns roles to available agents via prompt parameters. Use when starting multi-agent dev 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。

Blind dual-implementation pattern where two agents independently create artifacts, then a third agent judges which is better and synthesizes learnings.

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。

Multiple agents draft a structured document through federated co-authorship and formal voting.

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 document handoffs, parent lane contracts, and parallel batch contracts between specialized AI agents (foreman, workers, reviewers). Use for multi-agent workflows, subagents, original goal preservation, native gates, claim ceilings, terminal states, baton passes, or guild-style agent coordination.

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 "share memory between agents", "KV cache compaction for multi-agent", "orchestrator worker context", "latent briefing", "reduce worker tokens", "cross-agent memory without summarization", or discusses Attention Matching compaction, recursive language models with workers, or token explosion in hierarchical 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。

This skill should be used when the user asks to "design multi-agent system", "implement supervisor pattern", "create swarm architecture", "coordinate multiple agents", or mentions multi-agent patterns, context isolation, agent handoffs, sub-agents, or parallel agent 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。

Turn one task into a tree of GitHub issues worked by agents. Creates (or reuses) an epic issue, then launches the recursive orchestrator → decomposer → worker pipeline. Use when asked to "decompose a task", "break this down into issues and work it", "spawn agents to build X", or run via /task-decompose.

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。

Claude Code 用に作った新規または既存 plugin を Codex からも install できるようにしたいとき、.codex-plugin/plugin.json と .agents/plugins/marketplace.json を同期・検査するときに使う。

Cloudflare Email Service（Email Sending + Email Routing）でトランザクションメールの送受信を実装するスキル。Workers bindingまたはREST APIによるメール送信、Email Routing、Agents SDKのemail handler、Workers・Node.js・Python・Go等への組み込みで使う。到達性、SPF/DKIM/DMARC、wrangler email設定、MCP email tools、coding agentからのメール送信も対象。「Workerにメール機能を追加」のような依頼でも、重要な設定要件があるため必ず使用する。

Cloudflare全体の製品選択と対象Accountの文脈を確定するルータースキル。Workers、Pages、KV、D1、R2、Workers AI、Vectorize、Agents SDK、Flagship、Tunnel、WAF、Terraform等のどの製品・参照・専用Skillを使うか判断する。Cloudflare要件が複数製品にまたがる、製品名が未確定、Accountの選択が必要な場合に使う。Wrangler操作、Durable Objects、Turnstile、Email Service、Workersコード、本番デプロイは責務表の専用Skillへ委譲し、本Skillで実行手順を重複定義しない。数値・API・設定は記憶よりCloudflare公式ドキュメントの取得を優先する。

Azure AI Projects SDK for .NET. High-level client for Azure AI Foundry projects including agents, connections, datasets, deployments, evaluations, and indexes. Use for AI Foundry project management, versioned agents, and orchestration. Triggers: "AI Projects", "AIProjectClient", "Foundry 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。

Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimi...

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。

You are a cognitive architect who understands that memory makes agents intelligent. You've built memory systems for agents handling millions of interactions. You know that the hard part isn't storing - it's retrieving the right memory at the right time.

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 phone agents with AgentPhone API. Use when the user wants to make phone calls, send/receive SMS, manage phone numbers, create voice agents, set up webhooks, or check usage — anything related to telephony, phone numbers, or voice AI.

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 LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.

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。

I build AI systems that can act autonomously while remaining controllable. I understand that agents fail in unexpected ways - I design for graceful degradation and clear failure modes. I balance autonomy with oversight, knowing when an agent should ask for help vs proceed independently.

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。

Route agent work through AccInt's MCP memory loop: retrieve prior outcomes, resolve frames, and close commitments with evidence.

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。

You're a quality engineer who has seen agents that aced benchmarks fail spectacularly in production. You've learned that evaluating LLM agents is fundamentally different from testing traditional software—the same input can produce different outputs, and "correct" often has no single answer.

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。