Run your company with an AI team. No servers, no SaaS dashboards, no complex tooling — just markdown files and Claude Code.

Clone this repo, answer a few questions, and you immediately have:

An entire AI team with specialized roles. Not one generic AI — a software architect, product manager, engineer, GTM director, content strategist, technical writer, sales advisor, and more. Each agent has deep context about your company, your tech stack, your market, and your codebase.

A work queue you can fire and forget. Queue up tasks, walk away, come back to completed work with detailed reports. The system picks the highest-priority task, assigns it to the right specialist, and executes autonomously.

A research-before-code pipeline. Unsure about an approach? Queue a research task. The AI investigates, writes a plan document, and waits for your approval before a single line of code is written. You can send it back for revisions. When you approve, an implementation task is auto-created.

Agents that verify their own work. Every task goes through goal-backward verification — the agent re-reads acceptance criteria, traces through the code, and confirms everything actually works before marking complete. No more "it compiles but doesn't do the thing."

A natural language interface. You don't need to memorize commands. Just say "what should I do next?" or "I need to build user auth" and the system figures out the right action. The slash commands are power-user shortcuts, not prerequisites.

Institutional memory that compounds. Learnings accumulate over time. Patterns get discovered. Mistakes get recorded. Your AI team gets smarter about YOUR codebase with every task it completes.

Full founder control. Nothing ships without your review. You approve plans, review completed work, and make the strategic calls. The AI handles execution — you handle direction.

Here's what's wild — you get all of this with zero infrastructure. No CI/CD pipelines, no project management SaaS, no API integrations. It's all markdown files that Claude Code reads and follows.

git clone https://github.com/cloudrepo-io/founder-os.git my-company
cd my-company

# In Claude Code
/init

Answer a few questions about your company — name, tech stack, which team members you want, market context — and the framework configures itself.

# Queue up some work
/queue:add Build user authentication

# Or investigate something first
/queue:research Evaluate authentication approaches for our stack

# Let the AI team handle it
/queue:work

# Check your dashboard
/founder:dashboard

Or just talk naturally:

"What should I work on next?"
"I need to build a payment integration"
"How's the project going?"
"Review what the team did today"

Known work — you know what needs building:

/queue:add → /queue:work → done → /founder:review

Needs investigation — scope unclear, multiple approaches:

/queue:research → /queue:work → plan → /queue:approve → /queue:work → done

The research pipeline ensures major changes get proper thought before code is written. Plans go through a review loop until you're satisfied.

1. You queue a task (or just describe what you need)
2. The system loads your company context, tech stack, and accumulated learnings
3. The right specialist agent picks up the task
4. The agent executes, verifying against acceptance criteria as it goes
5. On completion, a detailed report is generated
6. You review and approve (or request changes)

All of this is driven by markdown files that define the workflow. There is no backend. There is no database. The queue is JSON files. The agents are markdown personas. The commands are markdown instructions. And it all just works because Claude Code reads and follows them.

your-company/
├── .claude/
│   ├── commands/           # Slash commands
│   │   ├── queue/          # Queue management (6 commands)
│   │   ├── team/           # Team management
│   │   ├── founder/        # Founder tools
│   │   ├── config/         # Configuration
│   │   ├── learnings/      # Knowledge capture
│   │   ├── create-command.md  # Command creation wizard
│   │   └── init.md         # Setup wizard
│   ├── agents/             # Your AI team
│   │   └── _templates/     # Agent templates
│   ├── queue/              # Work queue system
│   │   ├── active.json     # In-progress work
│   │   ├── backlog.json    # Pending work
│   │   ├── completed/      # Archived work
│   │   ├── plans/          # Research plan documents
│   │   └── reports/        # Completion reports
│   ├── learnings/          # Institutional memory
│   ├── team.json           # Team roster
│   ├── company.yaml        # Company configuration
│   └── founder-actions.md  # Human task tracker
├── CLAUDE.md               # AI team operating manual (the brain)
└── README.md               # This file

This is the core insight: Claude Code already knows how to read files, follow instructions, and execute work. You don't need a framework with dependencies, a build step, or an API. You just need well-structured markdown files that tell it what to do and how to do it.

Every "feature" in founder-os is a markdown file:

• Agent with deep domain expertise? A markdown file with a persona and context.
• Autonomous work queue? A markdown file describing the pickup-execute-complete flow.
• Research pipeline with approval gates? Markdown files defining each step.
• Quality verification? A checklist in the CLAUDE.md that agents follow.

When you want to add a new capability, you create a new markdown file. That's it.

You're not replacing yourself — you're amplifying yourself. The AI team handles implementation, research, content, documentation, and testing. You handle strategy, decisions, and reviews. The queue system is the interface between you and your team.

Your AI team gets smarter over time. Learnings accumulate, patterns get discovered, and mistakes get recorded. A fresh Claude session isn't starting from zero — it reads your learnings and benefits from everything that came before.

This is an open-source framework. Contributions welcome:

• New agent templates
• Additional commands
• Documentation improvements
• Example configurations

MIT — see LICENSE for details.

This framework powers how we build new features at CloudRepo — a universal artifact repository for all your packages, containers, and dependencies. If you're building libraries, tools, or apps that you want to share with the world, come check it out.