「multi-agent」の検索結果

项目管理专家与产品经理在做项目决策、技术评审或商业策略制定时，当需要多视角碰撞以打破思维局限，请用此技能模拟多智能体真实会议辩论，一键生成包含决策结论、核心论据与风险提示的完整会议记录，让复杂决策高效落地！

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Use when designing multi-agent topologies that run ON OpenRig — authoring RigSpec and AgentSpec files for new rigs, creating agent startup content (guidance / skills / culture), or diagnosing why a launched rig's agents aren't behaving as intended. NOT for changing OpenRig itself (use developing-openrig); NOT for ordinary CLI operation of an existing rig (use openrig-user). Covers the full authoring lifecycle from user intent to validated, launchable rig.

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 multi-agent AI workflows with clear boundaries, handoffs, and monitoring. Use when a complex PM task should run as parallel specialized agents instead of one linear process.

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 question, decision, plan, tradeoff, or claim needs a rigorous verdict and one perspective is not enough. Spawns a panel of 3 to 5 subagent jurors that form independent blind opinions, deliberate anonymously under an anti-anchoring and anti-sycophancy protocol, and return one committed verdict with confidence, preserved dissent, and a concrete next action. Domain-agnostic across engineering, architecture, data, product, hiring, strategy, vendor choice, build-vs-buy, and research design. Trigger phrases include "convene a jury", "have agents debate and decide", "get a panel to decide", "multi-agent decision", "stress-test this and decide", "monte um juri", "tribunal de agentes", "painel para decidir". Do NOT use to only critique without deciding (use the-fool for that), to build a plan or write the solution itself, or for simple factual lookups.

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 challenging ideas, plans, decisions, or proposals. Invoke to play devil's advocate, run a pre-mortem, red team, stress test assumptions, audit evidence quality, or find blind spots before committing. Do NOT use for building plans, making decisions, or generating solutions — this skill only challenges and critiques. For a multi-agent panel that challenges and then commits to a verdict, use the-jury.

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 and deploy autonomous AI agents using the OpenServ SDK (@openserv-labs/sdk). IMPORTANT - Always read the companion skill openserv-client alongside this skill, as both packages are required to build and run agents. openserv-client covers the full Platform API for multi-agent workflows and ERC-8004 on-chain identity. Read reference.md for the full API reference.

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 only when a developer explicitly asks to clean, prune, tidy, or organize DeepChat SDD documentation after implementation and validation. Scans docs/features, docs/issues, and docs/architecture; prefers multi-agent review when available; removes completed issue docs when a linked GitHub issue is closed or implementation and validation evidence proves the bug no longer exists, drops stale plans and legacy task files from completed feature or architecture goals, and deletes obsolete feature or architecture docs.

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。

CCG Skills - Quality gates, documentation generator, and multi-agent orchestration. Auto-installed by CCG workflow 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。

Use this skill when working inside the ClawTeam repository itself: local development, debugging, reviewing, testing, validating multi-agent flows, or checking whether a code change actually works end-to-end. Use the repository bootstrap scripts to standardize the local `clawteam` command and to wire project-local `.agents` and `.claude` skills back to this repository. This skill is repository-development oriented, not a general end-user usage guide.

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Multi-agent, browser-driven QA pass over a branch or PR — scope it, plan it, fan out one subagent per testing area in its own isolated sandbox, compile a severity-ranked markdown report. Use for an "E2E test", "QA pass", "manual test", or "bug bash" request — broader than driving the UI to check one fix (see `playwright`) or reviewing a diff (see `code-review`).

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 multi-agent econometric review on estimation code, identification arguments, and research artifacts

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This skill should be used when the user asks to "/do-agent", asks for "Execute complex tasks using multi-agent architecture with context protection", or needs the workflow previously provided by the /do-agent slash command.

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。

Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination

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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search 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。

Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed 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。

Author or run durable BB workflows when the user requests workflow execution or multi-agent orchestration.

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 whether a multi-agent setup earns its coordination cost — use before adding an agent, or when a workflow feels slow or agents agree without adding signal

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。

Parallel read-only multi-agent root-cause investigation for bugs, regressions, crashes, flaky behavior, or unexplained failures. Use when the user asks to investigate a bug, find the root cause, trace a regression, understand why something broke, or wants a ranked diagnosis with the fastest proof path without making code edits.

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Plan and execute large refactor or rewrite efforts efficiently with parallel multi-agent analysis and implementation. Use when a user asks to refactor many files, split workstreams, analyze a target code area, and coordinate sub-agents with clear ownership and dependency-aware 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。

Parallel read-only multi-agent review of a current git diff or explicit file scope to find behavioral regressions, security or privacy risks, performance or reliability issues, and contract or test coverage gaps. Use when the user asks for a review swarm, parallel review, diff review, regression review, security review, or wants high-signal issues plus a prioritized fix path without editing files.

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。

Spawn Agentica multi-agent patterns

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Reference guide for Agentica multi-agent infrastructure APIs

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Orchestrates multi-agent forensic investigations on public GitHub repositories, coordinating parallel evidence collection, hypothesis formation, verification, and report generation.

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Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib 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。