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Coordinator Init Mode

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bradygaster
coordinator-init-mode

The complete two-phase Init Mode protocol the Squad coordinator runs when no team exists yet in the current repo. Phase 1 = propose the team (no files created, wait for user confirm). Phase 2 = create .squad/ scaffolding, casting state, .gitattributes for merge drivers, and the always-on built-ins (Scribe, Ralph, Rai, Fact Checker). Loaded on demand when the coordinator detects no .squad/team.md exists.

Overview

Publisherbradygaster
Repositorysquad
Skill namecoordinator-init-mode
Stars
3.3K
Forks
506
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Coordinator Init Mode 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/coordinator-init-mode
  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/coordinator-init-mode .claude/skills/coordinator-init-mode
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Coordinator Init Mode 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 Coordinator Init Mode 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 Coordinator Init Mode 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.

Load this skill when: you detect that no .squad/team.md exists in the resolved team root — that means this is a fresh repo or a repo that has never been squadified, and Init Mode is the right path. The short stub in squad.agent.md tells you to load this skill; the full two-phase protocol lives here.

⚠️ Eager-execution exception: Init Mode is the ONE exception to the eager-execution / parallel-fan-out doctrine. Phase 1 MUST end with a user confirmation before any file is created. Do not bypass.

Phase 1: Propose the Team

No team exists yet. Propose one — but DO NOT create any files until the user confirms.

  1. Identify the user. Run git config user.name to learn who you're working with. Use their name in conversation (e.g., "Hey {user}, what are you building?"). Store their name (NOT email) in team.md under Project Context. Never read or store git config user.email — email addresses are PII and must not be written to committed files.
  2. Ask: "What are you building? (language, stack, what it does)"
  3. Cast the team. Before proposing names, run the Casting & Persistent Naming algorithm (see the canonical Casting reference at .squad/templates/casting-reference.md):
    • Determine team size: pick 4–5 cast (user-domain) agents, then add the 4 always-on built-ins (Scribe + Ralph + Rai + Fact Checker — see their dedicated sections in squad.agent.md). A typical fresh squad has 8–9 total roster entries, not 4–5.
    • Determine assignment shape from the user's project description.
    • Derive resonance signals from the session and repo context.
    • Select a universe. Allocate character names from that universe.
    • Scribe is always "Scribe" — exempt from casting.
    • Ralph is always "Ralph" — exempt from casting.
    • Rai is always "Rai" — exempt from casting.
    • Fact Checker is always "Fact Checker" — exempt from casting (same pattern as Scribe / Ralph / Rai).
  4. Propose the team with their cast names. Example (names will vary per cast):
🏗️  {CastName1}  — Lead          Scope, decisions, code review
⚛️  {CastName2}  — Frontend Dev  React, UI, components
🔧  {CastName3}  — Backend Dev   APIs, database, services
🧪  {CastName4}  — Tester        Tests, quality, edge cases
📋  Scribe       — (silent)      Memory, decisions, session logs
🔄  Ralph        — (monitor)     Work queue, backlog, keep-alive
🛡️  Rai         — (background)  RAI awareness, content safety
🔍  Fact Checker — (verifier)    Verification + Devil's Advocate
  1. Use the ask_user tool to confirm the roster. Provide choices so the user sees a selectable menu.

    The question must be self-contained because the client may render the blocking input request without the assistant text that preceded it.

    The question must reuse every roster line from the exact proposal emitted in step 4, in the same order. Do not regenerate, summarize, truncate, omit, or replace any roster row with an ellipsis. Every proposed member must appear with their emoji, cast name, role, and concise responsibility/scope — including all four always-on built-ins (Scribe, Ralph, Rai, Fact Checker).

    Shape:

    Confirm this team (universe: {ActualUniverse}):
    
    {Every roster line from step 4, copied in full and in order}
    
    Look right?

    Never use a bare "Look right?" question and never rely on preceding assistant output to identify the team being confirmed.

    • choices: ["Yes, hire this team", "Add someone", "Change a role"]

⚠️ STOP. Your response ENDS here. Do NOT proceed to Phase 2. Do NOT create any files or directories. Wait for the user's reply.


Phase 2: Create the Team

Trigger: The user replied to Phase 1 with confirmation ("yes", "looks good", or similar affirmative), OR the user's reply to Phase 1 is a task (treat as implicit "yes").

If the user said "add someone" or "change a role," go back to Phase 1 step 3 and re-propose. Do NOT enter Phase 2 until the user confirms.

  1. Create the .squad/ directory structure (see .squad/templates/ for format guides or use the standard structure: team.md, routing.md, ceremonies.md, decisions.md, decisions/inbox/, casting/, agents/, orchestration-log/, skills/, log/, rai/).

Casting state initialization: Copy .squad/templates/casting-policy.json to .squad/casting/policy.json (or create from defaults). Create registry.json (entries: persistent_name, universe, created_at, legacy_named: false, status: "active") and history.json (first assignment snapshot with unique assignment_id).

Seeding: Each agent's history.md starts with the project description, tech stack, and the user's name so they have day-1 context. Agent folder names are the cast name in lowercase (e.g., .squad/agents/ripley/). The Scribe's charter includes maintaining decisions.md and cross-agent context sharing. Rai's charter is seeded from the Rai-charter.md template, and .squad/rai/policy.md is seeded from rai-policy.md. Fact Checker's charter is seeded from fact-checker-charter.md and .squad/fact-checker/policy.md is seeded from fact-checker-policy.md.

Team.md structure: team.md MUST contain a section titled exactly ## Members (not "## Team Roster" or other variations) containing the roster table. This header is hard-coded in GitHub workflows (squad-heartbeat.yml, squad-issue-assign.yml, squad-triage.yml, sync-squad-labels.yml) for label automation. If the header is missing or titled differently, label routing breaks.

Merge driver for append-only files: Create or update .gitattributes at the repo root to enable conflict-free merging of .squad/ state across branches:

.squad/decisions.md merge=union
.squad/agents/*/history.md merge=union
.squad/log/** merge=union
.squad/orchestration-log/** merge=union
.squad/rai/audit-trail.md merge=union

The union merge driver keeps all lines from both sides, which is correct for append-only files. This makes worktree-local strategy work seamlessly when branches merge — decisions, memories, and logs from all branches combine automatically.

  1. Say: "✅ Team hired. Try: '{FirstCastName}, set up the project structure'"

  2. Post-setup input sources (optional — ask after team is created, not during casting):

    • PRD/spec: "Do you have a PRD or spec document? (file path, paste it, or skip)" → If provided, follow PRD Mode flow.
    • GitHub issues: "Is there a GitHub repo with issues I should pull from? (owner/repo, or skip)" → If provided, follow GitHub Issues Mode flow.
    • Human members: "Are any humans joining the team? (names and roles, or just AI for now)" → If provided, add per Human Team Members section.
    • Copilot agent: "Want to include @copilot? It can pick up issues autonomously. (yes/no)" → If yes, follow Copilot Coding Agent Member section and ask about auto-assignment.
    • These are additive. Don't block — if the user skips or gives a task instead, proceed immediately.

Frequently asked questions

What does the Coordinator Init Mode AI skill do?

The complete two-phase Init Mode protocol the Squad coordinator runs when no team exists yet in the current repo. Phase 1 = propose the team (no files created, wait for user confirm). Phase 2 = create .squad/ scaffolding, casting state, .gitattributes for merge drivers, and the always-on built-ins (Scribe, Ralph, Rai, Fact Checker). Loaded on demand when the coordinator detects no .squad/team.md exists.

Why use Coordinator Init Mode on TypingMind?

Because you install it once and use it with any model. Coordinator Init Mode 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 Coordinator Init Mode in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bradygaster/squad/tree/dev/packages/squad-cli/templates/skills/coordinator-init-mode. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Coordinator Init Mode?

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 Coordinator Init Mode?

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

Is the Coordinator Init Mode 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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