Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Act as the A2A Coordinator: a protocol layer that lets multiple Claude Code agents communicate, collaborate, and delegate work through structured message passing, shared context, and formal handoffs. Orchestrate every interaction through the shared context file .a2a-context.json and the Agent tool.
references/protocol.md — message format, message types, lifecycle, shared context schema, atomic read-modify-write, context size management.references/registry.md — agent registration, capability discovery, built-in agent templates.references/patterns.md — request/response, pipeline, fan-out/fan-in, conversation, supervisor.references/handoff.md — structured handoff, acceptance, rejection, chain tracking.references/error-handling.md — timeouts, rejections, deadlock detection, degradation, escalation matrix.references/workflows.md — worked examples (research+writer, code+review, sales+technical).references/operations.md — coordination commands, best practices, monitoring, security, init detail.references/registry.md or write custom specs.references/patterns.md. Prefer pipeline when order matters, fan-out when subtasks are independent..a2a-context.json if it exists and report current state; otherwise create it from the template in references/operations.md. Register every agent into the agents section per references/registry.md.references/operations.md). For parallelism, issue multiple Agent tool calls in a single response.chain. Follow references/handoff.md..a2a-context.json to track progress. On timeout, rejection, deadlock, or failure, apply the procedures and escalation matrix in references/error-handling.md. Cap retries at 3 before escalating to the user.conclusions section and present the final output..a2a-context.json as the single source of truth. Read it before acting; write the complete file back after modifying. Follow the atomic read-modify-write procedure in references/protocol.md.references/protocol.md.conclusions. Restrict task assignment changes to the Coordinator..a2a-context.json; pass sensitive data in-memory through Agent prompts and add the file to .gitignore.User: "Research the top 5 AI frameworks and write a comparison article."
Coordinator:
1. Create .a2a-context.json
2. Register: researcher, writer
3. Dispatch researcher: "Search for top 5 AI frameworks, compare features, performance, ecosystem"
4. Read researcher's findings from shared context
5. Dispatch writer: "Using the research findings, write a 1200-word comparison article"
6. Read writer's draft from shared context
7. Present the final article to the user
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Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. A top-tier commander (Fable or Opus) orchestrates the swarms; the user picks a power level (Max Power / Heavy / Balanced / Economy) that sets the Opus/Sonnet/Haiku model mix per layer. Layer 1 is 3-50+ specialist agents, each with its own full context window; Layer 2 is 2+ sub-agents per member. Includes git safety, tiered sizing, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up.
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Use to test whether an AI agent, prompt, or skill actually works before or after it ships — build a small eval set, score runs, and catch regressions when the prompt or model changes. Trigger when the user asks whether a prompt is good, wants to compare models or versions, sees inconsistent agent output, or is about to put an agent in front of customers.
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Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.
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Designs and deploys custom agent teams for specific business workflows. Interactive discovery of business processes, then generates complete team configurations with specialized agent roles, tool access, communication protocols, and handoff rules.
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Use when an AI coding agent is writing or refactoring production code and its known failure modes need to be held in check — over-engineering, silent scope creep, invented APIs, claiming work is done without checking, deleting tests to make them pass. Trigger when the user asks for careful, production-grade, or reviewed code, or when a previous agent run produced confident output that turned out wrong.
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