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Moai Factory Foreman

OrganizationPopular
modu-ai
moai-factory-foreman

One unattended factory foreman iteration: watch the backlog queue, dispatch the next operator-picked card to an isolated worker, collect completion evidence on read (not on claims), and report. This is the body the project's loop.md driver invokes each iteration of a bare /loop; it can also be invoked directly to test one cycle by hand.

Overview

Publishermodu-ai
Repositorymoai-adk
Skill namemoai-factory-foreman
Stars
1.2K
Forks
226
Bundled files
Instructions only
LicenseApache-2.0
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 modu-ai on GitHub. Read the source before you install it.

Installation

Install the Moai Factory Foreman 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/modu-ai/moai-adk/tree/main/.claude/skills/moai-factory-foreman
  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/modu-ai/moai-adk.git /tmp/moai-adk
mkdir -p .claude/skills
cp -r /tmp/moai-adk/.claude/skills/moai-factory-foreman .claude/skills/moai-factory-foreman
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Moai Factory Foreman 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 Moai Factory Foreman 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 Moai Factory Foreman 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.

Factory Foreman Loop Iteration

[HARD] Before the first action of a session running this skill, read BOTH role-gated rule files in full: .claude/rules/moai/workflow/factory-dispatch.md and .claude/rules/moai/workflow/cross-session-messaging.md. A Codex-only project deploys the same files under .moai/policies/ (for example .moai/policies/workflow/factory-dispatch.md) — read whichever layout this project carries. The always-loaded surface carries only their stubs; the dispatch protocol, the card classes, and the queue boundaries this loop consumes live in those two files.

One unattended pass of the factory foreman: watch the backlog queue, dispatch the next operator-picked card to an isolated worker, collect completion evidence, report. The queue surface is moai todo (the moai gtd spelling serves the same queue); the dispatch protocol and card classes live in the factory dispatch rule (.claude/rules/moai/workflow/factory-dispatch.md). foreman — an auxiliary role of the leader: the unattended watcher that dispatches the already-picked card to an isolated worker when no leader session is holding the queue.

Running unattended

AskUserQuestion is removed from the tool pool while this skill is active — that is the mechanical guarantee that the loop cannot stop and ask. Anything that would have been a question becomes a line in the iteration report, and anything that genuinely needs the operator's decision becomes a blocked card under Boundaries below.

Deployment: start a session in the project, run bare /loop, then background the session — loop tasks carry over to the background session and keep running without a terminal. Esc cancels the pending wakeup of a waiting loop. A recurring loop expires seven days after creation; restart it when the queue still needs a foreman. Background monitors do not survive a session resume — the first iteration after a resume re-arms the queue watch.

The session's permission settings must already allow what this loop uses (queue reads, git inspection, the worker spawn). A permission prompt that surfaces while unattended stalls the iteration until someone attaches; pre-approving that surface in project settings is the operator's setup step, not something this loop can do for itself.

Boundaries (hard)

  1. The operator admits and picks work. Only backlog items whose state is already picked are dispatchable. Never run moai gtd add; never run moai gtd next <n> — that mutation is the operator's pick. Never invent, reword, or reorder cards. An empty queue is a legitimate state: say so and idle. A batch authorization (/moai:todo --auto — the operator's typed invocation-as-approval) is the card-pick gate's autonomous form (.claude/rules/moai/workflow/auto-semantics.md §9): within it, serial consumption on its own judgment, outside the keep-set (bar a [보류 card, which is ranked last, not excluded), is authorized; queue ADMISSION stays the operator's. A lane takes its card through moai factory next --card <id>, never through this loop.
  2. No approval gate is answered on the operator's behalf. When a card's next step needs a human decision that is not already recorded as made (plan-to-run kickoff approval, a review severity call, a scope choice), do not proceed. Leave the card picked, name it blocked-for-operator in the report together with the decision it waits on, and move on.
  3. One write-capable worker at a time. While a worker is in flight the iteration only reads. This is the foreman's own serialization, stricter than the doctrine it sits under — one writer per tree, owned by .claude/rules/moai/core/agent-common-protocol.md § Background Agent Execution. The foreman keeps one worker in flight so a failed iteration has exactly one author to read.
  4. Every worker runs in its own worktree (isolation: "worktree" on the spawn; relative paths in the prompt — the worker's CWD is its worktree root). Nothing writes to the shared checkout.
  5. No integration actions. No push, no pull request, no merge, no branch deletion, no worktree disposal. The card's branch is unpushed and its worktree is the work's only instance; both stay until the operator integrates them. The report names the branch and the worktree path.
  6. Verification is lane-local. The worker runs only the checks its own change can affect; the full suite belongs to CI. Never spawn background CPU load — the queue watch below is the only long-running process this loop arms.
  7. Completion is read, never trusted. A card advances only on evidence this iteration actually read.

The iteration

  1. Queue watch. If no backlog monitor is live (first iteration, or after a resume), arm one Monitor on the queue file, re-arming it at each expiry:

    • command:

      sh
      # The queue directory, resolved the way factory.StateDirForRoot does for a
      # standard git-repository project: <moai-home>/db/<project-key>/todo,
      # keyed by the repository's canonical (primary-checkout) root.
      mh=${MOAI_HOME:-$HOME/.moai}
      root=$(git rev-parse --show-toplevel 2>/dev/null) || root=$PWD
      top=$(git worktree list --porcelain 2>/dev/null | sed -n 's/^worktree //p' | head -n 1)
      [ -n "$top" ] && root=$top
      root=$(cd "$root" && pwd -P)
      key=$(basename "$root"); key=$(printf '%s' "$key" | tr -c 'A-Za-z0-9._-' '-')
      sum=$(printf '%s' "$root" | sha256sum 2>/dev/null | cut -c1-8)
      [ -n "$sum" ] || sum=$(printf '%s' "$root" | shasum -a 256 | cut -c1-8)
      d=$mh/db/$key-$sum/todo
      last=init
      while true; do
        cur=$(cksum "$d"/backlog.db "$d"/backlog.db-wal 2>/dev/null)
        [ -n "$cur" ] || cur=missing
        if [ "$cur" != "$last" ]; then
          [ "$last" != init ] && echo "backlog changed"
          last=$cur
        fi
        sleep 5
      done
    • timeout_ms: 1800000

    • description: backlog queue watch

    The persistent option no longer exists — every Monitor now carries a deadline, timeout_ms is a required input, and the watch above is an unbounded loop that never ends on its own. 1800000 is the longest deadline the tool documents accepting; a larger value is capped rather than honoured. At expiry the loop is killed and one notice arrives with the event count, so an iteration that still needs the watch re-arms it. A non-interactive (-p) session is reported to cap the deadline lower than an interactive one; that lower cap is not observable from the tool input schema, so treat a shorter-than-requested expiry there as expected rather than as a fault.

    The watch resolves the queue directory the way factory.StateDirForRoot does for a standard git-repository project — a project-keyed directory under the moai home (MOAI_HOME when that is set to an absolute path, otherwise ~/.moai), keyed by the primary checkout's root — so a linked worktree watches the primary checkout's queue, not a directory local to its own tree. The queue is the database, and a backlog.json beside it is an export or a legacy leftover — never the queue — so a watch pointed at the JSON on a migrated project polls a file that never changes and reports nothing, forever. The write-ahead log is watched alongside the database because a committed write can sit in backlog.db-wal with the database image byte-identical until a checkpoint folds it back; watching the database alone misses exactly those mutations. The checksum poll emits one line per change and costs two tiny reads every five seconds. Do not tighten the interval, and do not arm a second watcher. Each emitted line, like each scheduled wakeup, is a prompt to run this same idempotent iteration — an iteration that finds nothing to do ends quickly.

  2. Read the queue. moai gtd list --json (lock-free). A missing queue file is an empty queue, never an error. Records carry id, text, spec_id, and state (queued | picked | dropped).

  3. Collect before dispatch. If a worker dispatched by an earlier iteration has returned, read its evidence file first (step 6). If a worker is still running, end the iteration with a one-line status.

  4. Choose the dispatchable card. The oldest picked item with no live worker and no recorded blocker. queued items are not yours to pick outside a batch authorization.

  5. Dispatch one worker with the Agent tool, isolation: "worktree". The dispatch is a fixed-field address block — a pointer, not a copy, ten lines at most:

    card: <id>
    spec: <SPEC-ID>            # only when the card carries one
    cmd: <the card's work in one line; the phase command when a SPEC is attached>
    evidence: .moai/reports/<card-id>/evidence.md

    Route the spawn to the agent the card's work matches under the standard delegation rules; where no specialist matches, a general-purpose spawn with a domain whitelist. The worker prompt carries the block plus these standing orders: rename the worktree branch to WT-<slug> (a descriptive slug, never the card id) first; implement the card; verify lane-locally; write the evidence file with decisions, verbatim output tails of the checks run, explicit gaps, and residual risk; commit by explicit pathspec; never push.

  6. Collect on evidence. When the worker returns, read the evidence file it names. Advance the card — moai gtd done <t-id> — only when the evidence shows the work complete: verbatim passing output present, gaps named. A missing, unreadable, or stale evidence file is a gap: the card stays picked, the report says why, and the card is not re-dispatched this iteration. Absence of a failure signal is not a pass.

  7. Report. Close with two to six lines: queue summary, what was dispatched or collected, which evidence was read, blocked cards and the decisions they wait on. This report is what the operator reads on reattach — name what you read, not what you were told.

Factory seam (reserved, not implemented)

The single-worker dispatch above is the only mode. Fanning a card out to numbered factory lanes — the multi-lane launcher surface — is separate work; when the foreman grows that routing, it lands here as a second dispatch mode chosen per card. Until then this loop spawns one subagent per card, reads no factory state, and launches no lanes.

Failure handling

Stop the loop (ScheduleWakeup with stop: true), with a one-line reason, when the loop cannot do its job: the queue file is repeatedly unreadable, the queue watch cannot be armed, or this skill's own surface is broken. A transient worker failure is not a loop failure — record it on the card and let the next iteration decide whether to re-dispatch.

Frequently asked questions

What does the Moai Factory Foreman AI skill do?

One unattended factory foreman iteration: watch the backlog queue, dispatch the next operator-picked card to an isolated worker, collect completion evidence on read (not on claims), and report. This is the body the project's loop.md driver invokes each iteration of a bare /loop; it can also be invoked directly to test one cycle by hand.

Why use Moai Factory Foreman on TypingMind?

Because you install it once and use it with any model. Moai Factory Foreman 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 Moai Factory Foreman in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/modu-ai/moai-adk/tree/main/.claude/skills/moai-factory-foreman. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Moai Factory Foreman?

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 Moai Factory Foreman?

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

Is the Moai Factory Foreman AI skill free?

Yes. It is published on GitHub by modu-ai under the Apache-2.0 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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