676건 · 관련도순
MCP Agent Mail - Mail-like coordination layer for multi-agent workflows. Identities, inbox/outbox, file reservations, contact policies, threaded messaging, pre-commit guard, Human Overseer, static exports, disaster recovery. Git+SQLite backed. Python/FastMCP.
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Multi-Agent Adversarial Analysis System for code security
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Debate multi-agente: 2-4 perspectivas (PM, Arquiteto, QA, Security) debatem decisao tecnica. Output: decisao + rationale + dissenting opinions → ADR. Inspirado no Party Mode do BMAD-METHOD.
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Multi-agent debate orchestration for Architecture Decision Records. Automatically triggers on ADR create/edit/delete. Coordinates architect, critic, independent-thinker, security, analyst, and high-level-advisor agents in structured debate rounds until consensus.
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5-level verification pyramid: static→unit→Playwright E2E (homepage-first, 6bp)→AI visual→post-deploy. 8-check quality gate. Multi-agent testing (functional/security/a11y/performance). Playwright v1.59+ AI agents (Planner/Generator/Healer). WCAG 2.2 AA via axe-core v4.11. Percy+Chromatic visual regression. ADA Title II 2027/2028 deadlines.
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Comprehensive guide for installing, configuring, operating, and troubleshooting OpenClaw — a self-hosted, multi-channel AI agent gateway. Use when the user asks about OpenClaw setup, configuration, channel management (WhatsApp/Telegram/Discord/Slack/iMessage/etc.), model provider setup, Gateway operations, multi-agent routing, security hardening, troubleshooting, or any maintenance task related to their local OpenClaw installation. Also use when encountering errors from `openclaw` CLI commands or the Gateway daemon.
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Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).
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High-performance reinforcement learning framework optimized for speed and scale. Use when you need fast parallel training, vectorized environments, multi-agent systems, or integration with game environments (Atari, Procgen, NetHack). Achieves 2-10x speedups over standard implementations. For quick prototyping or standard algorithm implementations with extensive documentation, use stable-baselines3 instead.
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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.
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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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OpenAI API (developers.openai.com) の Responses API リファレンス。 text generation, embeddings, gpt-6-astra / gpt-6.1-sol / gpt-6-luna, structured outputs, streaming, function calling, programmatic / async tool calling, conversation state, prompt caching, prompt cache diagnostics, reasoning (configuration_update), background mode, compaction, multi-agent, WebSocket mode (mid-turn steering, stream_id), webhooks, error codes, rate limits, Realtime server controls, 公式 SDK (Python / JS / .NET / Java / Go / Ruby), openai CLI, Chat Completions / Assistants 移行, deprecations。
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OpenAI API (developers.openai.com) の Agents SDK (Python / TypeScript)・Agents API (beta)。 Agent, Runner, handoffs, guardrails, tracing, SandboxAgent、 client.beta.agents の sessions / environments / vaults / plugins、 built-in tools（web search, file search, computer use, code interpreter, image generation）、 function tools, vector stores, MCP (Model Context Protocol) connectors, background mode, multi-agent orchestration。
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Bulk mode — maximum development speed by spending tokens aggressively on massive parallel sub-agents and multi-agent workflows, while the orchestrator context stays lean (pairs with /slim). Invoking /bulk is the user's explicit opt-in to Workflow/ultracode-style orchestration for this session. Use when the user types /bulk, asks for "爆速", "並列で大量に", "ultracode", "多エージェント", or hands over a big task where wall-clock speed matters more than token cost.
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Multi-agent council for code review and architecture decisions
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Configure and orchestrate parallel agents using the standalone Antigravity 2.0 Agent Manager and Antigravity IDE.
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Meta-skill que orquestra todos os agentes do ecossistema. Scan automatico de skills, match por capacidades, coordenacao de workflows multi-skill e registry management.
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Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration.
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AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.
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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.
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Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory
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SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) comprehensive development methodology with multi-agent orchestration
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Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
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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.
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Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
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