81 skills · sorted by relevance
Agent-to-Agent 轻量通信协议。仿 Google A2A 设计，但适配 Clawdbot/OpenClaw 生态。用于：(1) Agent 能力发现 (2) 跨 agent 任务协作 (3) 结构化的 agent 间消息交换。触发词：a2a、agent协议、agent通信、agent card、能力发现。
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A2A agent card JSON templates with schema validation and examples for different agent types. Use when creating agent cards, implementing A2A protocol discovery, setting up agent metadata, configuring authentication schemes, defining agent capabilities, or when user mentions agent card, agent discovery, A2A metadata, service endpoint configuration, or agent authentication setup.
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SDK installation and setup patterns for Agent-to-Agent Protocol across Python, TypeScript, Java, C#, and Go. Use when implementing A2A protocol, setting up SDKs, configuring authentication, or when user mentions SDK installation, language-specific setup, or A2A integration.
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Integration patterns for combining Agent-to-Agent (A2A) Protocol with Model Context Protocol (MCP) for hybrid agent communication. Use when building systems that need both agent-to-agent communication and agent-to-tool integration, implementing composite architectures, or when user mentions A2A+MCP integration, hybrid protocols, or multi-agent tool access.
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SDK installation and setup patterns for Agent-to-Agent Protocol across Python, TypeScript, Java, C#, and Go. Use when implementing A2A protocol, setting up SDKs, configuring authentication, or when user mentions SDK installation, language-specific setup, or A2A integration.
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AI Agent skill marketplace integration for A2A Market. Enables agents to buy skills, sell skills, and earn money autonomously. Use when: (1) User asks to find/search/buy a skill or capability, (2) User wants to sell/list/monetize their agent's skills, (3) User asks about marketplace earnings or transactions, (4) Agent detects a capability gap and needs to acquire new skills, (5) User says "marketplace", "buy skill", "sell skill", "a2a market", or mentions earning money with their agent, (6) User asks about credits, daily rewards, referral, or registration. Supports x402 USDC payments on Base L2 and Credits payment system.
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A2A Market — AI Agent 商业交易网络。想买东西？说一句话，平台帮你全网找货、比价、议价。 想卖东西？上架商品或声明你能供什么，有人需要时平台主动找你。 一个身份，买卖自由。支持 Webhook 秒级推送和轮询拉取双通知模式。 配置 API Key 即可使用，无需安装任何软件包。 Triggers: 采购, 寻源, 发布商品, 上架, 卖, 供给, A2A Market, agent注册, 算力余额, buy, sell, procurement, sourcing, supply, notifications, compute balance.
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A2AP — AI Agent互联互通的第一开放协议。6个LIVE能力（humanize_text/memory_evolve/skill_audit/news_briefing/agent_peering/custom_agent_dev）全部通过DeepSeek V4 Pro真机实现。公网已打通 frp-can.com:51831，支持HTTP Bridge & MCP桥接（87K⭐生态直接调用）。Hermes↔OpenClaw双向通知桥。TCP原始协议（非HTTP），默认端口9800。CC BY-SA 4.0。
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Manage agent identities and delegated work on OKX AI, from finding service providers and publishing tasks to reviewing deliveries. It also covers task monitoring, AI service subscriptions, and communication setup between agents.
Guide development of a LangChain or LangGraph project created with agents-cli. It explains tool additions, local runs, evaluation, and serving while identifying the required graph structure and commands that differ from ADK projects.
Use to extend an existing agent project with memory, app integration, VPC, multi-agent, migration, model, browser, code interpreter, payments, or resource removal. Triggers: "add memory", "remember across sessions", "call agent from app", "invoke agent from code", "agent auth", "streaming", "VPC", "VPC connectivity", "can't reach from VPC", "multi-agent", "A2A", "A2A auth", "orchestrator not delegating", "specialist not called", "migrate Bedrock Agent", "migration issue", "change model", "browser tool", "code interpreter", "delete agent", "tear down", "agentcore remove", "cross-account memory", "add payments capability to my agent", "wire payments plugin", "integrate x402 payments with the agent I'm building", "add MPP payments", "Machine Payments Protocol". External APIs via Gateway: use agents-connect. New project: use agents-get-started. CLI/dev-server errors: use agents-debug. Runtime x402/MPP payments: use agents-pay. Migration-specific Strands vs LangGraph routes here.
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Sub-skill de agent-ready-cloudflare: Implement A2A Agent Card
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Provides best practices for AI agent orchestration including MCP servers, A2A protocol, multi-agent coordination, and swarm architectures. Use when designing agent systems, configuring MCP servers, setting up agent teams, or when user mentions 'MCP', 'A2A', 'agent orchestration', 'multi-agent', 'swarm', 'agent team', 'LangGraph', 'CrewAI', 'AutoGen'.
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Comprehensive research corpus covering 40+ agentic coordination protocols and ecosystems (MCP, A2A, ANP, UCP, ACP, AGNTCY, NANDA, FIPA, WoT, etc.) with emphasis on identity architectures, governance bodies, and interoperability convergence patterns.
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Synapse A2A agent communication — sending messages, spawning agents, delegating tasks, sharing memory, and coordinating file edits. Use this skill when: running synapse send/reply/broadcast/interrupt, spawning agents with synapse spawn or synapse team start, managing the task board with synapse tasks, sharing knowledge with synapse memory, locking files with synapse file-safety, checking agent status with synapse list/status, or orchestrating any multi-agent workflow.
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Re-inject Synapse A2A initial instructions after context has been cleared. Use this skill when the agent has lost its identity, A2A communication protocol, or configuration after /clear or context reset, when synapse send/reply commands stop working, or when the agent no longer recognizes its SYNAPSE_AGENT_ID. Triggered by /synapse-reinst command.
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Agent profiles, public channels, and direct messaging between AI agents via the a2achat.top API.
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Agent2Agent (A2A) Protocol implementation - communicate with other AI agents
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Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Deep comparison of A2A protocol, MCP, OpenAI function calling, and LangChain message types. Teaches when to use rigid schemas vs free-form with validation, typed vs untyped, streaming vs batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).
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Auto-activate for litestar_mcp, LitestarMCP, MCP, MCPConfig, MCPSkillsConfig, LitestarA2A, A2AConfig, @mcp.tool/resource/prompt, mcp_tool=, or stdio MCP. Not for non-Litestar MCP SDK servers.
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Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Compares the version-pinned A2A, MCP, and JSON-RPC role boundaries covered by this bundle; OpenAI and LangChain-specific APIs require their own current sources. Teaches when to use rigid schemas versus free-form content with validation, typed versus untyped boundaries, and streaming versus batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).
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Find ITINAI agents, post service agents, and make reviewed A2A service requests.
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Guided setup and troubleshooting for installing, enabling, configuring, verifying, and updating @aramisfa/openclaw-a2a-outbound in OpenClaw.
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Agent2Agent (A2A) Protocol implementation - communicate with other AI agents
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