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Found 518 Skills
A curated collection of research papers and resources on agentic reasoning for Large Language Models, organized by planning, tool use, search, self-evolution, and multi-agent systems.
Create, review, and update Prompt and agents and workflows. Covers 5 workflow patterns, agent delegation, Handoffs, Context Engineering. Use for any .agent.md file work or multi-agent system design. Triggers on 'agent workflow', 'create agent', 'ワークフロー設計'.
OpenAI Agents SDK (Python) development. Use when building AI agents, multi-agent workflows, tool integrations, or streaming applications with the openai-agents package.
Multi-agent code review with specialized perspectives (security, performance, patterns, simplification, tests)
Build single-agent and multi-agent systems using Google's Agent Development Kit (ADK) in Python, Java, Go, or TypeScript. Use when creating AI agents with ADK, designing multi-agent architectures, implementing agent tools, configuring agent callbacks, managing agent state, orchestrating sequential/parallel/loop agent workflows, or when the user mentions ADK, google-adk, google agent development kit, agentic AI with Gemini, or agent orchestration with Google tools. Also use when setting up ADK projects, writing agent tests, deploying agents, or integrating MCP tools with ADK.
Multi-agent distributed context preservation protocol using cryptographic sharding, gossip propagation, and Byzantine fault tolerance to maintain coherent shared memory across dynamic agent networks.
Design new APIs or review existing ones using debate-driven multi-agent workshop. Agents propose designs and challenge each other on consumer UX, domain modeling, security, performance, and standards compliance. Use when the user wants to design a new API, review an existing API, decide between REST/GraphQL, or improve API architecture. Keywords: api design, api review, rest api, graphql, openapi, api architecture, api specification, endpoint design, api standards.
Multi-agent routing, channel bindings, and tool policies
Multi-agent review of implementation plans. Use after creating a plan but before implementing, especially for complex or risky changes.
Multi-agent quality improvement review with constructive feedback. Provides suggestions for best practices, code quality, alternatives, and performance optimization.
Designs multi-agent system architectures with orchestration patterns, tool schemas, and performance evaluation. Use when building AI agent systems, designing agent workflows, creating tool schemas, or evaluating agent performance.
Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory