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Agent To Agent

Organization
OneWave-AI
agent-to-agent

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.

Overview

PublisherOneWave-AI
Repositoryclaude-skills
Skill nameagent-to-agent
Stars
293
Forks
49
Bundled files
7
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by OneWave-AI on GitHub. Read the source before you install it.

Installation

Install the Agent To Agent AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/OneWave-AI/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/agent-to-agent .claude/skills/agent-to-agent
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent To Agent in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agent To Agent on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agent To Agent is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Agent-to-Agent (A2A) Communication Protocol

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.

Contents

  • 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.

Workflow

  1. Understand the goal. Determine what the user wants to accomplish with multiple agents.
  2. Design the team. Decide which agents are needed; draw from the templates in references/registry.md or write custom specs.
  3. Choose the pattern. Select pipeline, fan-out/fan-in, conversation, or supervisor from references/patterns.md. Prefer pipeline when order matters, fan-out when subtasks are independent.
  4. Initialize. Locate the project root. Read .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.
  5. Execute. Dispatch agents via the Agent tool following the chosen pattern. Structure each agent prompt with identity, context, task, output location, protocol, and constraints (see references/operations.md). For parallelism, issue multiple Agent tool calls in a single response.
  6. Coordinate handoffs. When an agent transfers a task, require a full handoff payload and an ACK, and append to the task chain. Follow references/handoff.md.
  7. Monitor and recover. Read .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.
  8. Deliver. Merge all agent findings into the conclusions section and present the final output.

Core Rules

  • Treat .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.
  • Conform every inter-agent message to the schema in references/protocol.md.
  • Confine each agent to writing its own section plus shared conclusions. Restrict task assignment changes to the Coordinator.
  • Never write secrets to .a2a-context.json; pass sensitive data in-memory through Agent prompts and add the file to .gitignore.
  • Require explicit ERROR messages for all failures; never fail silently. Run context summarization when the file exceeds 50KB.

Minimal Example: 2-Agent Pipeline

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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Agent To Agent AI skill do?

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.

Why use Agent To Agent on TypingMind?

Because you install it once and use it with any model. Agent To Agent is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agent To Agent in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/OneWave-AI/claude-skills/tree/main/agent-to-agent. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Agent To Agent?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agent To Agent?

As many as you like. As long as a model supports skills, you can use Agent To Agent with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agent To Agent AI skill free?

Yes. It is published on GitHub by OneWave-AI under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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