Complete multi-agent systems toolkit: LangGraph workflows, agent orchestration patterns, supervisor hierarchies, human-in-the-loop, A2A protocols, memory systems, and production deployment.
npx claudepluginhub latestaiagents/agent-skills --plugin agent-pluginImplement checkpointing for agent recovery, debugging, and replay. Use this skill when building recoverable agents, implementing replay, debugging agent failures, or creating resumable workflows. Activate when: agent checkpoint, agent recovery, resume agent, agent restart, workflow replay, agent debugging, failure recovery, state snapshot.
Implement persistent agent state that survives failures and restarts. Use this skill when building stateful agents, implementing checkpointing, persisting agent memory across sessions, or recovering from failures. Activate when: durable state, agent persistence, checkpointing, agent recovery, stateful agents, state persistence, cross-session memory, agent restart.
Build agent workflows with LangGraph 1.0 state machines and graph patterns. Use this skill when creating agent graphs, implementing state machines, building multi-step agent workflows, or using LangGraph. Activate when: LangGraph, agent graph, state graph, agent workflow, graph nodes, conditional edges, agent state machine, ReAct agent.
Implement Agent-to-Agent (A2A) communication for cross-framework interoperability. Use this skill when building multi-agent communication, implementing agent protocols, connecting agents across frameworks, or standardizing agent interfaces. Activate when: agent to agent, A2A, agent communication, agent protocol, cross-framework agents, agent interoperability, MCP, agent discovery.
Use this skill when managing AI agent costs. Activate when the user needs to control token usage, implement cost limits for agents, optimize LLM spending, track agent costs, or prevent runaway API bills in agent systems.
Use this skill when implementing error handling for AI agents. Activate when the user needs agents to handle failures gracefully, implement retry strategies, design fault-tolerant agent systems, or build agents that can recover from errors without human intervention.
Use this skill when designing task handoffs between agents. Activate when the user needs to pass work between agents, transfer context between agents, implement agent-to-agent communication, or design protocols for agents to collaborate on sequential tasks.
Use this skill when implementing memory for AI agents. Activate when the user needs agents to remember past interactions, implement context persistence, build knowledge bases for agents, design agent state management, or create shared memory between multiple agents.
Use this skill when designing peer-to-peer multi-agent systems. Activate when the user needs agents that collaborate without central control, wants resilient agent networks, needs swarm-like agent behavior, or is building decentralized agent architectures.
Use this skill when designing supervisor-based multi-agent systems. Activate when the user needs to orchestrate multiple AI agents, coordinate agent workflows, implement a central controller for agents, design hub-and-spoke agent architecture, or build hierarchical agent systems.
Use this skill when implementing tool selection for AI agents. Activate when the user needs agents to choose the right tools, implement dynamic tool routing, integrate MCP servers, design tool selection logic, or build agents that can use external services effectively.
Build agents that pause for human approval, review, and intervention. Use this skill when implementing approval workflows, human oversight, agent interrupts, or review-before-execute patterns. Activate when: human in the loop, HITL, agent approval, human oversight, interrupt agent, pause agent, review workflow, agent supervision.
Use this skill when testing AI agent systems. Activate when the user needs to test agent behavior, write tests for multi-agent systems, implement agent evaluation frameworks, create test harnesses for autonomous agents, or validate agent outputs systematically.
This skill should be used when the model's ROLE_TYPE is orchestrator and needs to delegate tasks to specialist sub-agents. Provides scientific delegation framework ensuring world-building context (WHERE, WHAT, WHY) while preserving agent autonomy in implementation decisions (HOW). Use when planning task delegation, structuring sub-agent prompts, or coordinating multi-agent workflows.
Multi-agent orchestration with AI SDK v5 - handoffs, routing, and coordination for any AI provider (OpenAI, Anthropic, Google)
Multi-agent collaboration plugin for Claude Code. Spawn N parallel subagents that compete on code optimization, content drafts, research approaches, or any problem that benefits from diverse solutions. Evaluate by metric or LLM judge, merge the winner. 7 slash commands, agent templates, git DAG orchestration, message board coordination.
Multi-agent orchestrator — supervisor loop that launches agents to implement plans
Agent coordination and meta-programming - multi-agent orchestration, workflow automation. Works best with other voltagent plugins installed.
Requires secrets
Needs API keys or credentials to function
Share bugs, ideas, or general feedback.
Editorial "Agent Architect" bundle for Claude Code from Antigravity Awesome Skills.
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