claude

agent-architect

Multi-agent system design: supervisor and mesh patterns, handoffs, memory, tool routing, error recovery, cost budgets, testing, LangGraph, durable state, human-in-the-loop, checkpointing, A2A. 13 skills. Same skills as agent-plugin, without its commands.

6 on GitHubby latestaiagentsv2.1.0MITupdated 9 days agoAI Architecture
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Plugin agent-architect from marketplace latestaiagents in latestaiagents/agent-skills. Commands read from the repo's manifest — 8 days ago.

What it installs

Skills (13)

  • a2a-protocols

    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, ag

  • agent-checkpointing

    Implement 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 deb

  • agent-cost-budgeting

    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.

  • agent-error-recovery

    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.

  • agent-handoff-protocols

    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.

  • agent-memory-systems

    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.

  • agent-mesh-architecture

    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.

  • agent-supervisor-pattern

    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.

  • agent-testing-harness

    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.

  • agent-tool-routing

    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.

  • durable-state-patterns

    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, statef

  • human-in-loop-agents

    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, revi

  • langgraph-workflows

    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 ed

View source on GitHub latestaiagents.com

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