Agent skill · software engineering · juliusbrussee
loop-factory
Run a spec-driven agent loop where coding tasks live as markdown specs that move through inbox → active → archive, get implemented by Claude Code or Codex, and pass a review gate before they count as done. Use when the user mentions "loop factory", a "spec-driven loop", an "agent factory", wants repeatable/reviewable agent work, or when a repo has a factory/specs/inbox or factory/specs/active directory. Also covers installing and scaffolding the loop-factory CLI into a project.
Why this skill is useful
Adds a CLI tool that automates the implementation and verification of coding tasks based on markdown specs, fundamentally extending the AI's capabilities.
What it needs
Requires python installed locally. About 5k tokens when loaded. Last updated 2026-07-08. 144 stars on the source repository.
What this skill does
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. 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: 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/: 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills juliusbrussee/loop-factory