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simonasrazm / simon-factory-lights-out

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SFLO — Simon Factory Lights Out: a gated pipeline protocol for building software with AI agents

ai-agents autonomous-agents claude-code llm protocol
16
2
100% credibility
Found Mar 26, 2026 at 16 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

SFLO is a gated pipeline system that orchestrates AI agents to collaboratively build software through five sequential quality gates: discovery, building, testing, verification, and shipping.

How It Works

1
💡 Discover SFLO

You learn about SFLO, a smart system that guides AI helpers to build complete software projects reliably through careful steps.

2
📥 Easy Setup

You simply tell your AI assistant to add SFLO to your workspace, and it handles everything with one quick command.

3
✅ All Set

After a fresh start, SFLO is quietly ready in your space, waiting for your ideas.

4
🚀 Kick Off a Project

You say something fun like 'SFLO: build a job board with search and filters,' and the process springs to life automatically.

5
📋 Plan the Project

A planning AI reads your idea and writes a clear scope with goals, data needs, and success measures.

6
🔄 Build and Check Loop

Building AI creates the software, checking AI tests it thoroughly, and they repeat up to 10 times until it passes quality checks.

7
👀 Final Review

Planning AI double-checks everything, sending it back for fixes if needed, up to a few rounds.

🎉 Project Ready

SFLO delivers your complete, high-quality software or asks for your help if more tweaks are needed, feeling like having a pro team at work.

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AI-Generated Review

What is simon-factory-lights-out?

Simon Factory Lights Out (SFLO) is a Python protocol for building software through a gated pipeline with AI agents. You kick it off by saying "SFLO: build a job board," and it enforces five strict gates—Discover, Build, Test, Verify, Ship—each requiring a validated artifact like a scope doc or QA report before advancing. Hooks for OpenClaw and Claude Code automate the flow, looping devs and QA up to 10 times until quality hits B+ or better, turning chaotic agent chats into a lights-out factory.

Why is it gaining traction?

Its Scout agent auto-matches specialized agents (PM for web, QA for tests) from your agents/ dir based on your prompt, no config needed. Gated progression prevents half-baked outputs, with CLI commands like `scaffold.py next` or full runner mode handling iterations hands-free. Python simplicity plus runtime hooks make it plug-and-play for agent-driven building, standing out from freeform prompts by enforcing protocol without babysitting.

Who should use this?

AI enthusiasts in OpenClaw or Claude Code prototyping CLIs, dashboards, or sites via agents. PMs defining scopes and verifications for agent teams. Devs wanting structured iteration on small software projects, like vulnerability scanners or filtered job boards, without endless debugging loops.

Verdict

Early alpha with 16 stars and 1.0% credibility score—docs are solid, MIT-licensed, but expect rough edges and missing agents. Try it for agent pipelines if you're in supported runtimes; skip for production until more battle-tested.

(178 words)

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