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概要と使いどころ
Dynamic agent composition and management system. USE WHEN user says create custom agents, spin up custom agents, specialized agents, OR asks for agent personalities, available traits, agent voices. Handles custom agent creation, personality assignment, voice mapping, and parallel agent orchestration.
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AgentHero AI - Hierarchical multi-agent orchestration system with PM coordination, file-based state management, and interactive menu interface. Use when managing complex multi-agent workflows, coordinating parallel sub-agents, or organizing large project tasks with multiple specialists. All created agents use aghero- prefix.
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。
Comprehensive guide for building AI workflows, agents, and workforce systems with AgenticFlow. Use when designing workflows with various node types, configuring single agents, or orchestrating workforce collaboration patterns.
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Assess agentic layer maturity using the 12-grade classification system (Class 1-3). Use when evaluating codebase readiness, identifying next upgrade steps, or tracking progress toward the Codebase Singularity.
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。
Patterns for multi-agent coordination, task decomposition, handoffs, and workflow orchestration. Best practices for building and managing agent 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。
Design and operate multi-agent orchestration patterns (ReAct loops, evaluator-optimizer, orchestrator-workers, tool routing) for LLM systems. Use when building or debugging agent workflows, tool-use loops, or multi-step task delegation; triggers: agentic, multi-agent, orchestration, ReAct, evaluator-optimizer, tool-use, handoff.
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 manage AI agent skills following best practices. Use when creating new skills, optimizing context, designing multi-agent systems, or implementing progressive disclosure patterns.
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。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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。
Programmatic agent definitions for the Claude Agent SDK in TypeScript and Python. Use when creating agents for SDK-based applications rather than filesystem-based Claude Code.
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。
Optimize ElevenLabs conversational AI agents for real estate applications. Use when creating new agents, improving conversation quality, selecting voices, engineering system prompts, configuring agent parameters, or analyzing agent performance metrics. Includes voice selection, model tuning, prompt engineering, and A/B testing strategies.
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。
Economic identity and reputation system for autonomous agents on Base.
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。
Designs multi-agent systems with coordinated agent swarms, task distribution, inter-agent communication, and emergent collective behavior.
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。
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
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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。
Expert in designing, orchestrating, and managing multi-agent systems (MAS). Specializes in agent collaboration patterns, hierarchical structures, and swarm intelligence. Use when building agent teams, designing agent communication, or orchestrating autonomous workflows.
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Track and optimize agent specialization during methodology development. Use when agent specialization emerges (generic agents show >5x performance gap), multi-experiment comparison needed, or methodology transferability analysis required. Captures agent set evolution (Aₙ tracking), meta-agent evolution (Mₙ tracking), specialization decisions (when/why to create specialized agents), and reusability assessment (universal vs domain-specific vs task-specific). Enables systematic cross-experiment learning and optimized M₀ evolution. 2-3 hours overhead per experiment.
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。
Write effective system prompts for TD AI agents. Covers role definition, constraint specification, output formatting, tool usage instructions, and prompt structure patterns.
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Create well-structured prompts for AI agents using proven architecture patterns. Use when users ask to write agent prompts, system prompts, or agent instructions, or want to improve existing prompts that aren't working.
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。
Systematically deconstruct written content into verifiable claims, validate each using search/documentation, and facilitate informed discussion through structured interviewing.
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Expert prompt engineer specializing in designing, optimizing, and managing prompts for large language models. Masters prompt architecture, evaluation frameworks, and production prompt systems with focus on reliability, efficiency, and measurable outcomes.
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Automatically applies when designing multi-agent systems. Ensures proper tool schema design with Pydantic, agent state management, error handling for tool execution, and orchestration patterns.
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Comprehensive guide for the agent permissions and approval system. Use when an agent needs to request user approval for sensitive operations, when implementing new approval policies, when integrating platform-specific approval UIs (Slack buttons, WhatsApp replies, Dashboard modals), or when troubleshooting approval flows. Covers policy definition, platform adapters, and the complete approval lifecycle.
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Designs multi-step agent workflows with tool usage, retry logic, state management, and budget controls. Provides orchestration diagrams, tool execution order, fallback strategies, and cost limits. Use for "AI agents", "agentic workflows", "multi-step AI", or "autonomous 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。
Expert context manager specializing in information storage, retrieval, and synchronization across multi-agent systems. Masters state management, version control, and data lifecycle with focus on ensuring consistency, accessibility, and performance at scale.
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。