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Distributed Mesh

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bradygaster
distributed-mesh

How to coordinate with squads on different machines using git as transport

Overview

Publisherbradygaster
Repositorysquad
Skill namedistributed-mesh
Stars
3.3K
Forks
506
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Distributed Mesh 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.
    https://github.com/bradygaster/squad/tree/dev/packages/squad-cli/templates/skills/distributed-mesh
  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/bradygaster/squad.git /tmp/squad
mkdir -p .claude/skills
cp -r /tmp/squad/packages/squad-cli/templates/skills/distributed-mesh .claude/skills/distributed-mesh
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Distributed Mesh 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 Distributed Mesh 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 Distributed Mesh 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.

SCOPE

✅ THIS SKILL PRODUCES (exactly these, nothing more):

  1. mesh.json — Generated from user answers about zones and squads (which squads participate, what zone each is in, paths/URLs for each), using mesh.json.example in this skill's directory as the schema template
  2. sync-mesh.sh and sync-mesh.ps1 — Copied from this skill's directory into the project root (these are bundled resources, NOT generated code)
  3. Zone 2 state repo initialization (if applicable) — If the user specified a Zone 2 shared state repo, run sync-mesh.sh --init to scaffold the state repo structure
  4. A decision entry in .squad/decisions/inbox/ documenting the mesh configuration for team awareness

❌ THIS SKILL DOES NOT PRODUCE:

  • No application code — No validators, libraries, or modules of any kind
  • No test files — No test suites, test cases, or test scaffolding
  • No GENERATING sync scripts — They are bundled with this skill as pre-built resources. COPY them, don't generate them.
  • No daemons or services — No background processes, servers, or persistent runtimes
  • No modifications to existing squad files beyond the decision entry (no changes to team.md, routing.md, agent charters, etc.)

Your role: Configure the mesh topology and install the bundled sync scripts. Nothing more.

Context

When squads are on different machines (developer laptops, CI runners, cloud VMs, partner orgs), the local file-reading convention still works — but remote files need to arrive on your disk first. This skill teaches the pattern for distributed squad communication.

When this applies:

  • Squads span multiple machines, VMs, or CI runners
  • Squads span organizations or companies
  • An agent needs context from a squad whose files aren't on the local filesystem

When this does NOT apply:

  • All squads are on the same machine (just read the files directly)

Patterns

The Core Principle

"The filesystem is the mesh, and git is how the mesh crosses machine boundaries."

The agent interface never changes. Agents always read local files. The distributed layer's only job is to make remote files appear locally before the agent reads them.

Three Zones of Communication

Zone 1 — Local: Same filesystem. Read files directly. Zero transport.

Zone 2 — Remote-Trusted: Different host, same org, shared git auth. Transport: git pull from a shared repo. This collapses Zone 2 into Zone 1 — files materialize on disk, agent reads them normally.

Zone 3 — Remote-Opaque: Different org, no shared auth. Transport: curl to fetch published contracts (SUMMARY.md). One-way visibility — you see only what they publish.

Agent Lifecycle (Distributed)

1. SYNC:    git pull (Zone 2) + curl (Zone 3) — materialize remote state
2. READ:    cat .mesh/**/state.md — all files are local now
3. WORK:    do their assigned work (the agent's normal task, NOT mesh-building)
4. WRITE:   update own billboard, log, drops
5. PUBLISH: git add + commit + push — share state with remote peers

Steps 2–4 are identical to local-only. Steps 1 and 5 are the entire distributed extension. Note: "WORK" means the agent performs its normal squad duties — it does NOT mean "build mesh infrastructure."

The mesh.json Config

json
{
  "squads": {
    "auth-squad": { "zone": "local", "path": "../auth-squad/.mesh" },
    "ci-squad": {
      "zone": "remote-trusted",
      "source": "git@github.com:our-org/ci-squad.git",
      "ref": "main",
      "sync_to": ".mesh/remotes/ci-squad"
    },
    "partner-fraud": {
      "zone": "remote-opaque",
      "source": "https://partner.dev/squad-contracts/fraud/SUMMARY.md",
      "sync_to": ".mesh/remotes/partner-fraud",
      "auth": "bearer"
    }
  }
}

Three zone types, one file. Local squads need only a path. Remote-trusted need a git URL. Remote-opaque need an HTTP URL.

Write Partitioning

Each squad writes only to its own directory (boards/{self}.md, squads/{self}/*, drops/{date}-{self}-*.md). No two squads write to the same file. Git push/pull never conflicts. If push fails ("branch is behind"), the fix is always git pull --rebase && git push.

Trust Boundaries

Trust maps to git permissions:

  • Same repo access = full mesh visibility
  • Read-only access = can observe, can't write
  • No access = invisible (correct behavior)

For selective visibility, use separate repos per audience (internal, partner, public). Git permissions ARE the trust negotiation.

Phased Rollout

  • Phase 0: Convention only — document zones, agree on mesh.json fields, manually run git pull/git push. Zero new code.
  • Phase 1: Sync script (~30 lines bash or PowerShell) when manual sync gets tedious.
  • Phase 2: Published contracts + curl fetch when a Zone 3 partner appears.
  • Phase 3: Never. No MCP federation, A2A, service discovery, message queues.

Important: Phases are NOT auto-advanced. These are project-level decisions — you start at Phase 0 (manual sync) and only move forward when the team decides complexity is justified.

Mesh State Repo

The shared mesh state repo is a plain git repository — NOT a Squad project. It holds:

  • One directory per participating squad
  • Each directory contains at minimum a SUMMARY.md with the squad's current state
  • A root README explaining what the repo is and who participates

No .squad/ folder, no agents, no automation. Write partitioning means each squad only pushes to its own directory. The repo is a rendezvous point, not an intelligent system.

If you want a squad that observes mesh health, that's a separate Squad project that lists the state repo as a Zone 2 remote in its mesh.json — it does NOT live inside the state repo.

Examples

Developer Laptop + CI Squad (Zone 2)

Auth-squad agent wakes up. git pull brings ci-squad's latest results. Agent reads: "3 test failures in auth module." Adjusts work. Pushes results when done. Overhead: one git pull, one git push.

Two Orgs Collaborating (Zone 3)

Payment-squad fetches partner's published SUMMARY.md via curl. Reads: "Risk scoring v3 API deprecated April 15. New field device_fingerprint required." The consuming agent (in payment-squad's team) reads this information and uses it to inform its work — for example, updating payment integration code to include the new field. Partner can't see payment-squad's internals.

Same Org, Shared Mesh Repo (Zone 2)

Three squads on different machines. One shared git repo holds the mesh. Each squad: git pull before work, git push after. Write partitioning ensures zero merge conflicts.

AGENT WORKFLOW (Deterministic Setup)

When a user invokes this skill to set up a distributed mesh, follow these steps exactly, in order:

Step 1: ASK the user for mesh topology

Ask these questions (adapt phrasing naturally, but get these answers):

  1. Which squads are participating? (List of squad names)
  2. For each squad, which zone is it in?
    • local — same filesystem (just need a path)
    • remote-trusted — different machine, same org, shared git access (need git URL + ref)
    • remote-opaque — different org, no shared auth (need HTTPS URL to published contract)
  3. For each squad, what's the connection info?
    • Local: relative or absolute path to their .mesh/ directory
    • Remote-trusted: git URL (SSH or HTTPS), ref (branch/tag), and where to sync it to locally
    • Remote-opaque: HTTPS URL to their SUMMARY.md, where to sync it, and auth type (none/bearer)
  4. Where should the shared state live? (For Zone 2 squads: git repo URL for the mesh state, or confirm each squad syncs independently)

Step 2: GENERATE mesh.json

Using the answers from Step 1, create a mesh.json file at the project root. Use mesh.json.example from THIS skill's directory (.squad/skills/distributed-mesh/mesh.json.example) as the schema template.

Structure:

json
{
  "squads": {
    "<squad-name>": { "zone": "local", "path": "<relative-or-absolute-path>" },
    "<squad-name>": {
      "zone": "remote-trusted",
      "source": "<git-url>",
      "ref": "<branch-or-tag>",
      "sync_to": ".mesh/remotes/<squad-name>"
    },
    "<squad-name>": {
      "zone": "remote-opaque",
      "source": "<https-url-to-summary>",
      "sync_to": ".mesh/remotes/<squad-name>",
      "auth": "<none|bearer>"
    }
  }
}

Write this file to the project root. Do NOT write any other code.

Step 3: COPY sync scripts

Copy the bundled sync scripts from THIS skill's directory into the project root:

  • Source: .squad/skills/distributed-mesh/sync-mesh.sh

  • Destination: sync-mesh.sh (project root)

  • Source: .squad/skills/distributed-mesh/sync-mesh.ps1

  • Destination: sync-mesh.ps1 (project root)

These are bundled resources. Do NOT generate them — COPY them directly.

Step 4: RUN --init (if Zone 2 state repo exists)

If the user specified a Zone 2 shared state repo in Step 1, run the initialization:

On Unix/Linux/macOS:

bash
bash sync-mesh.sh --init

On Windows:

powershell
.\sync-mesh.ps1 -Init

This scaffolds the state repo structure (squad directories, placeholder SUMMARY.md files, root README).

Skip this step if:

  • No Zone 2 squads are configured (local/opaque only)
  • The state repo already exists and is initialized

Step 5: WRITE a decision entry

Create a decision file at .squad/decisions/inbox/<your-agent-name>-mesh-setup.md with this content:

markdown
### <YYYY-MM-DD>: Mesh configuration

**By:** <your-agent-name> (via distributed-mesh skill)

**What:** Configured distributed mesh with <N> squads across zones <list-zones-used>

**Squads:**
- `<squad-name>` — Zone <X> — <brief-connection-info>
- `<squad-name>` — Zone <X> — <brief-connection-info>
- ...

**State repo:** <git-url-if-zone-2-used, or "N/A (local/opaque only)">

**Why:** <user's stated reason for setting up the mesh, or "Enable cross-machine squad coordination">

Write this file. The Scribe will merge it into the main decisions file later.

Step 6: STOP

You are done. Do not:

  • Generate sync scripts (they're bundled with this skill — COPY them)
  • Write validator code
  • Write test files
  • Create any other modules, libraries, or application code
  • Modify existing squad files (team.md, routing.md, charters)
  • Auto-advance to Phase 2 or Phase 3

Output a simple completion message:

✅ Mesh configured. Created:
- mesh.json (<N> squads)
- sync-mesh.sh and sync-mesh.ps1 (copied from skill bundle)
- Decision entry: .squad/decisions/inbox/<filename>

Run `bash sync-mesh.sh` (or `.\sync-mesh.ps1` on Windows) before agents start to materialize remote state.

Anti-Patterns

❌ Code generation anti-patterns:

  • Writing mesh-config-validator.js or any validator module
  • Writing test files for mesh configuration
  • Generating sync scripts instead of copying the bundled ones from this skill's directory
  • Creating library modules or utilities
  • Building any code that "runs the mesh" — the mesh is read by agents, not executed

❌ Architectural anti-patterns:

  • Building a federation protocol — Git push/pull IS federation
  • Running a sync daemon or server — Agents are not persistent. Sync at startup, publish at shutdown
  • Real-time notifications — Agents don't need real-time. They need "recent enough." git pull is recent enough
  • Schema validation for markdown — The LLM reads markdown. If the format changes, it adapts
  • Service discovery protocol — mesh.json is a file with 10 entries. Not a "discovery problem"
  • Auth framework — Git SSH keys and HTTPS tokens. Not a framework. Already configured
  • Message queues / event buses — Agents wake, read, work, write, sleep. Nobody's home to receive events
  • Any component requiring a running process — That's the line. Don't cross it

❌ Scope creep anti-patterns:

  • Auto-advancing phases without user decision
  • Modifying agent charters or routing rules
  • Setting up CI/CD pipelines for mesh sync
  • Creating dashboards or monitoring tools

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 Distributed Mesh AI skill do?

How to coordinate with squads on different machines using git as transport

Why use Distributed Mesh on TypingMind?

Because you install it once and use it with any model. Distributed Mesh 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 Distributed Mesh in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bradygaster/squad/tree/dev/packages/squad-cli/templates/skills/distributed-mesh. 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 Distributed Mesh?

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 Distributed Mesh?

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

Is the Distributed Mesh AI skill free?

Yes. It is published on GitHub by bradygaster 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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