L

loop-factory

by @juliusbrusseev
4.3(120)

この Skill は、仕様書(spec)駆動の AI エージェントワークフローを実装し、コーディングタスクを Markdown 仕様書として受信箱→作業中→アーカイブの状態で進め、AI エージェントが実装し、レビューゲートを通過したら完了とします。繰り返し可能でレビュー可能なエージェント作業に適しています。

spec-drivenagent-looptask-automationreview-gateautomationGitHub
インストール方法
npx skills add https://github.com/juliusbrussee/skills --skill loop-factory
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Before / After 効果比較

1 组
使用前

このスキルがない場合、ユーザーは手動でタスクステータスを追跡し、異なるAIツールと連携し、コミュニケーションコストが高く、タスクの漏れが発生しやすく、納品サイクルが長くなります。

使用後

このSkillを使用すると、タスクは受信箱、作業中、アーカイブを自動的に流れ、AIエージェントが仕様に従って実装し、レビューゲートが品質を保証し、納品サイクルが大幅に短縮されます。

SKILL.md

Loop Factory

Loop Factory turns "ask an agent to build something" into a visible assembly line. Each task is a markdown spec. The folder the spec lives in is its state. Agents implement and verify; they never decide what to build.

   📥 inbox/          🔧 active/           📦 archive/
   tasks not      ──► task an agent    ──► finished work,
   started yet        is building now      reviewed + accepted

The one rule that governs everything: automate implementation and verification, not product decisions. If a spec is missing a decision, record the open question — do not invent product direction.

Invoke this skill on demand by typing /loop-factory (one manual pass), or let a cron fire it unattended — see Autonomous mode.

When to use this skill

  • Setting up Loop Factory in a repo (installing the CLI, running init).
  • Picking a spec out of the inbox and dispatching it to Claude Code or Codex.
  • Implementing a spec against its acceptance criteria.
  • Reviewing finished work and archiving accepted specs.
  • Backpropagating implementation learnings into the living specs/docs.

Setup (first time in a repo)

The loop-factory CLI is the state engine. Install it once, then scaffold any git repo:

# get the CLI (clone the source repo, install editable)
git clone https://github.com/JuliusBrussee/Loop-Factory.git
python3 -m pip install -e ./Loop-Factory

# scaffold the factory/ folders in your target project
cd your-project
loop-factory init
loop-factory doctor      # confirm git + agent CLIs are wired up

No runtime dependencies beyond Python 3.10+. If you are already inside the Loop-Factory repo, you can skip the install and run python3 bin/loop-factory <command> directly. Full install notes, agent-CLI setup, and how to copy the native agent adapters: references/install.md.

The loop

  1. Inspect the queue. Run loop-factory scan to see inbox and active specs.
  2. Grill new specs first. Before dispatching a fresh spec, interrogate it one question at a time (who owns this decision, what's out of scope, riskiest assumption, smallest acceptable version). Record answers under a # Grill Gate section. A vague spec produces vague code.
  3. Dispatch. Generate the implementation prompt and move the spec to active/:
    loop-factory dispatch --agent claude --limit 1 --stage
    
    This writes a prompt to factory/prompts/ and a run record to factory/runs/. It does not run the AI unless you add --execute.
  4. Implement. Build only the acceptance criteria. Match existing code style. Stay scoped.
  5. Verify. Run every command listed in the spec's verification: frontmatter. Capture the output as evidence.
  6. Review. Generate a review prompt and check the diff against the criteria:
    loop-factory review <spec-id> --agent claude
    
    Pass → archive. Fail → leave the spec in active/ for another pass.
  7. Archive accepted work.
    loop-factory archive <spec-id> --accepted
    
    --accepted is required on purpose — nothing leaves active/ without a confirmed pass.
  8. Backpropagate. If building the feature changed how the system actually works, sync that back into specs/docs without changing product intent:
    loop-factory backprop --agent claude
    

Autonomous mode

Run a single self-contained pass that drains the inbox with no human present. Trigger it when invoked with "autonomous" / "drain the inbox", by a scheduled cron, or repeatedly by /loop (e.g. /loop 30m /loop-factory).

A pass does exactly this:

  1. Scan factory/specs/inbox/.
  2. Filter to ready specs only. A spec is ready when its grill gate is complete — grill: completed in frontmatter, or a filled-in # Grill Gate section. Skip anything else and log it as "needs grilling." Never grill autonomously: the grill gate needs human answers, and inventing them would be deciding product direction.
  3. For each ready spec: stage → implement against acceptance criteria → run verification.
  4. Stop at the review gate. Leave built specs in active/. Do not archive — acceptance stays a human decision.
  5. If verification fails, leave the spec in active/ with a note in factory/runs/. Do not retry in a loop or weaken the criteria to make it pass.
  6. Report: built, skipped (needs grilling), failed-verification — and stop.

This keeps the boundary intact: the loop is a tireless builder, never a silent decider.

Two ways to fire it on a recurring basis:

  • /loop (local, recommended for active work) — /loop 30m /loop-factory re-runs this skill on any interval against your local working tree. Built specs land in active/; no git push or PR needed. Only runs while a Claude Code session is open.
  • Cron (cloud, unattended) — a scheduled routine that runs whether or not you're online (minimum 1-hour interval) and returns work as a pull request.

Both, plus the safety knobs, are in references/autonomous.md.

Core commands

loop-factory scan                              # list inbox + active specs
loop-factory dispatch --agent <claude|codex> --stage   # prompt + move to active
loop-factory review <spec-id> --agent <claude|codex>   # generate review prompt
loop-factory archive <spec-id> --accepted      # archive a passed spec
loop-factory backprop --agent <claude|codex>   # sync docs/specs with code
loop-factory doctor                            # health-check setup

Swap --agent claude for --agent codex anywhere — the loop is identical. Default behavior writes prompt files; add --execute only when the local codex/claude CLI is installed and the user asked for live execution. Full flag reference: references/commands.md.

Writing specs

A spec is markdown with frontmatter (id, title, agent, risk, verification) and a body covering Context, Acceptance Criteria, Constraints, and Review Notes. The acceptance criteria are the contract — they're what the agent builds toward and what the reviewer checks against, so make them concrete and testable. Format, examples, and the grill-gate questions: references/spec-authoring.md.

Boundaries

  • Only a human or an explicit CLI command moves a spec from inbox to active.
  • Only an accepted review moves a spec from active to archive.
  • A failed review leaves the spec active — it does not get deleted or silently re-dispatched.
  • Backprop may update specs/docs but must never change product intent on its own.
  • Generated files under factory/prompts/, factory/runs/, and factory/reviews/ are artifacts and audit trail — read them, don't treat them as source of truth. The spec is the source of truth.

Working with subagents

When isolation helps, hand stages to dedicated subagents instead of doing everything in one context:

  • a spec-implementer to write the code for one active spec,
  • a spec-reviewer (context-isolated) to judge the diff,
  • a spec-backpropagator to fold learnings back into specs/docs.

Use one agent when work is sequential, touches the same files, or is high-risk. Fan out to multiple only when specs are independent or review should stay context-isolated.

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統計データ

インストール数1.8K
評価4.3 / 5.0
バージョン
更新日2026年9月25日
比較事例1 件

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対応プラットフォーム

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作成2026年7月26日
最終更新2026年9月25日
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