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Team Communication Protocols

CommunityPopular
wshobson
team-communication-protocols

Structured messaging protocols for agent team communication including message type selection, plan approval, shutdown procedures, and anti-patterns to avoid. Use this skill when establishing communication norms for a newly spawned team, when deciding whether to send a direct message or a broadcast, when a team-lead needs to review and approve an implementer's plan before work begins, when orchestrating a graceful team shutdown after all tasks are complete, or when debugging why teammates are not coordinating correctly at integration points.

Overview

Publisherwshobson
Repositoryagents
Skill nameteam-communication-protocols
Stars
39.8K
Forks
4.2K
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by wshobson on GitHub. Read the source before you install it.

Installation

Install the Team Communication Protocols 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/wshobson/agents.git /tmp/agents
mkdir -p .claude/skills
cp -r /tmp/agents/plugins/agent-teams/skills/team-communication-protocols .claude/skills/team-communication-protocols
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Team Communication Protocols 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 Team Communication Protocols 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 Team Communication Protocols 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.

Team Communication Protocols

Protocols for effective communication between agent teammates, including message type selection, plan approval workflows, shutdown procedures, and common anti-patterns to avoid.

When to Use This Skill

  • Establishing communication norms for a new team
  • Choosing between message types (message, broadcast, shutdown_request)
  • Handling plan approval workflows
  • Managing graceful team shutdown
  • Discovering teammate identities and capabilities

Message Type Selection

message (Direct Message) — Default Choice

Send to a single specific teammate:

json
{
  "type": "message",
  "recipient": "implementer-1",
  "content": "Your API endpoint is ready. You can now build the frontend form.",
  "summary": "API endpoint ready for frontend"
}

Use for: Task updates, coordination, questions, integration notifications.

broadcast — Use Sparingly

Send to ALL teammates simultaneously:

json
{
  "type": "broadcast",
  "content": "Critical: shared types file has been updated. Pull latest before continuing.",
  "summary": "Shared types updated"
}

Use ONLY for: Critical blockers affecting everyone, major changes to shared resources.

Why sparingly?: Each broadcast sends N separate messages (one per teammate), consuming API resources proportional to team size.

shutdown_request — Graceful Termination

Request a teammate to shut down:

json
{
  "type": "shutdown_request",
  "recipient": "reviewer-1",
  "content": "Review complete, shutting down team."
}

The teammate responds with shutdown_response (approve or reject with reason).

Communication Anti-Patterns

Anti-PatternProblemBetter Approach
Broadcasting routine updatesWastes resources, noiseDirect message to affected teammate
Sending JSON status messagesNot designed for structured dataUse TaskUpdate to update task status
Not communicating at integration pointsTeammates build against stale interfacesMessage when your interface is ready
Micromanaging via messagesOverwhelms teammates, slows workCheck in at milestones, not every step
Using UUIDs instead of namesHard to read, error-proneAlways use teammate names
Ignoring idle teammatesWasted capacityAssign new work or shut down

Plan Approval Workflow

When a teammate is spawned with plan_mode_required:

  1. Teammate creates a plan using read-only exploration tools
  2. Teammate calls ExitPlanMode which sends a plan_approval_request to the lead
  3. Lead reviews the plan
  4. Lead responds with plan_approval_response:

Approve:

json
{
  "type": "plan_approval_response",
  "request_id": "abc-123",
  "recipient": "implementer-1",
  "approve": true
}

Reject with feedback:

json
{
  "type": "plan_approval_response",
  "request_id": "abc-123",
  "recipient": "implementer-1",
  "approve": false,
  "content": "Please add error handling for the API calls"
}

Shutdown Protocol

Graceful Shutdown Sequence

  1. Lead sends shutdown_request to each teammate
  2. Teammate receives request as a JSON message with type: "shutdown_request"
  3. Teammate responds with shutdown_response:
    • approve: true — Teammate saves state and exits
    • approve: false + reason — Teammate continues working
  4. Lead handles rejections — Wait for teammate to finish, then retry
  5. After all teammates shut down — Call TeamDelete to remove team resources

Handling Rejections

If a teammate rejects shutdown:

  • Check their reason (usually "still working on task")
  • Wait for their current task to complete
  • Retry shutdown request
  • If urgent, user can force shutdown

Teammate Discovery

Find team members by reading the config file:

Location: ~/.claude/teams/{team-name}/config.json

Structure:

json
{
  "members": [
    {
      "name": "security-reviewer",
      "agentId": "uuid-here",
      "agentType": "team-reviewer"
    },
    {
      "name": "perf-reviewer",
      "agentId": "uuid-here",
      "agentType": "team-reviewer"
    }
  ]
}

Always use name for messaging and task assignment. Never use agentId, role names, or unsuffixed aliases directly. If a teammate was spawned as team-lead-2, send to team-lead-2, not team-lead.

Troubleshooting

A teammate is not responding to messages. Check the teammate's task status. If it is idle, it may have completed its task and is waiting to be assigned new work or shut down. If it is still active, it may be mid-execution and will process messages once the current operation finishes.

A teammate says it cannot see SendMessage. Check the teammate agent's tools: frontmatter. Agent Teams communication tools such as SendMessage, TaskList, TaskGet, and TaskUpdate must be listed explicitly when an agent uses a restricted tool allowlist.

The lead is sending broadcasts for every status update. This is a common anti-pattern. Broadcasts are expensive — each one sends N messages. Use direct messages (type: "message") for point-to-point updates. Reserve broadcasts for critical shared-resource changes like an updated interface contract.

A teammate rejected a shutdown request unexpectedly. The teammate is still working. Check the rejection reason in the shutdown_response content field, wait for the work to finish, then retry. Never force-terminate a teammate that has unsaved work.

A plan_approval_request arrived but the request_id is missing. The teammate called ExitPlanMode without the required request context. Have the teammate re-enter plan mode, complete exploration, and call ExitPlanMode again. The request_id is generated automatically by the plan mode system.

Two teammates are waiting on each other and neither is making progress. This is a deadlock: both are blocked waiting for the other to finish first. The lead should send a direct message to one teammate with a stub or partial result so it can unblock and proceed.

Related Skills

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 Team Communication Protocols AI skill do?

Structured messaging protocols for agent team communication including message type selection, plan approval, shutdown procedures, and anti-patterns to avoid. Use this skill when establishing communication norms for a newly spawned team, when deciding whether to send a direct message or a broadcast, when a team-lead needs to review and approve an implementer's plan before work begins, when orchestrating a graceful team shutdown after all tasks are complete, or when debugging why teammates are not coordinating correctly at integration points.

Why use Team Communication Protocols on TypingMind?

Because you install it once and use it with any model. Team Communication Protocols 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 Team Communication Protocols in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wshobson/agents/tree/main/plugins/agent-teams/skills/team-communication-protocols. 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 Team Communication Protocols?

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 Team Communication Protocols?

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

Is the Team Communication Protocols AI skill free?

Yes. It is published on GitHub by wshobson 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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