loophaus

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Run AI coding agents in autonomous loops — fresh context each iteration, PRD-tracked progress, automatic quality gates.

_{Based on Geoffrey Huntley's Ralph Wiggum technique}

The Problem

AI coding agents struggle with long tasks:

• Context rot — agent gets confused after 10+ iterations
• Goal drift — agent forgets the spec and solves the wrong problem
• No quality signal — agent says "done" but tests still fail
• Token waste — you re-explain the same context every time

The Solution

• Fresh context per iteration — Each cycle reads PRD + progress from disk, zero degradation even after 20+ iterations
• PRD-linked progress tracking — Stories are tracked in prd.json with pass/fail state, not "I think I'm done"
• Quality scoring with keep/discard — Autoresearch-inspired refinement loop measures quality (0-100) and reverts regressions
• Universal stop hook — One Node.js hook works across Claude Code, Codex CLI, and Kiro CLI

Quick Start

npm install -g @graypark/loophaus
loophaus install

Note: npx @graypark/loophaus install may fail on some npm versions due to a bin resolution cache bug. Use the global install above for reliable setup.

The installer auto-detects your host (Claude Code, Codex CLI, or Kiro CLI) and sets up everything — stop hook, commands, and skills.

Then in your AI coding session:

/loop-plan Add user authentication with JWT, bcrypt, and login UI

That's it. The interview generates a PRD, activates the loop, and starts implementing story by story.

Safety

• Every iteration creates a git checkpoint — atomic revert anytime
• Max iterations limit (default 20, configurable)
• Quality threshold = circuit breaker — score < 80 triggers refine or stop
• Cost tracking with policy enforcement (max $5, max 30 min)
• loophaus clean for data lifecycle management

Why not just script this?

1. Fresh context isolation — no degradation after 20 iterations; each cycle starts from disk, not from a decaying conversation
2. PRD-linked progress tracking — structured prd.json with pass/fail per story, not "I think I'm done"
3. Quality scoring with keep/discard — autoresearch pattern: measure, keep improvements, revert regressions

How it works

An AI agent works on a task in a continuous loop. Each iteration starts with fresh context — reading the PRD and progress files to decide what to do next. The agent implements one story, commits, updates progress, and exits. The stop hook intercepts the exit and re-injects the prompt. Repeat until all stories pass.