founder-os
Multi-agent primitives for Claude Code.
Claude Code gives you one answer. founder-os lets you ask multiple agents, compare consensus, surface disagreement, and synthesize the useful part.
claude plugin install rhinehart514/founder-os
Try this first:
/founder-os:stochastic n=10 Should we use Postgres or SQLite for this project?
It polls 10 agents with the same prompt, then aggregates consensus, divergences, and outliers.
The primitives
| Skill | What it does |
|---|---|
/stochastic | Poll N agents (default 10) with identical prompts. Aggregate by consensus, divergences, outliers. |
/model-chat | Spawn 5 agents into a debate room. Round-robin turns, shared history, synthesizer merges. |
/fanout | Fan-out N researchers (Sonnet) in parallel, fan-in with Opus synthesis. |
/skillbuilder | Build flawless Claude Code skills. Studies existing skills as reference, ensures correct format, pushes for genuine intelligence. |
/autoresearch | Autonomous hill-climb. Read code, generate mutations, measure, keep-or-revert, repeat. |
Install
Plugin install
claude plugin install rhinehart514/founder-os
Skills become /founder-os:stochastic, /founder-os:model-chat, /founder-os:fanout, /founder-os:skillbuilder, /founder-os:autoresearch.
Direct install (short skill names)
npm i -g @rhinehart514/founder-os
Or from source:
git clone https://github.com/rhinehart514/founder-os.git
cd founder-os && bash bin/install.sh
Skills become /stochastic, /model-chat, /fanout, /skillbuilder, /autoresearch.
Restart Claude Code after installing.
Usage
/stochastic Should we use Postgres or SQLite for this use case?
/stochastic n=5 What's the best approach to rate limiting here?
/model-chat Is this API design good?
/model-chat agents=3 rounds=3 Should we ship this feature?
/fanout What are the tradeoffs of server components vs client components?
/fanout n=3 model=haiku Research authentication patterns for this stack
/skillbuilder Create a skill for code review with verification gates
/skillbuilder audit /stochastic
/autoresearch Optimize the Lighthouse score
/autoresearch Shrink the bundle size
Why this works
Same model, different runs = systematically different outputs. Polling multiple agents filters hallucinations (consensus), surfaces genuine judgment calls (divergences), and catches ideas a single pass misses (outliers).
What's included
skills/
stochastic/SKILL.md
model-chat/SKILL.md
fanout/SKILL.md
skillbuilder/SKILL.md
autoresearch/SKILL.md
autoresearch/templates/ # hill-climb runner, dashboard, config
agents/
parent.md # orchestrator
qa.md # quality checker
researcher.md # research agent
License
MIT