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@rhinehart514/founder-os

v8.2.2

Published

Multi-agent primitives for Claude Code: /stochastic (consensus), /model-chat (debate), /fanout (research), /skillbuilder (skill-craft), /autoresearch (hill-climb).

Readme

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.

What you get

Instead of one confident answer, /stochastic gives you a quick read on where independent Claude Code runs agree, where they split, and what unusual ideas only one or two runs noticed.

Consensus
- Postgres is the safer default if the app needs concurrent users, hosted backups, or analytics.
- SQLite is better for a local-first prototype or single-user desktop workflow.

Divergences
- 6/10 agents preferred Postgres immediately.
- 3/10 agents preferred SQLite until there is real multi-user pressure.
- 1/10 suggested starting SQLite with a planned migration boundary.

Outliers
- Add a tiny repository layer now so the storage decision stays reversible.
- Use the first paying/team user as the migration trigger, not an abstract scale guess.

Use it for architecture calls, launch plans, debugging hypotheses, API design, research questions, or any moment where a single pass feels too smooth.

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.

Marketplace install

claude plugin marketplace add rhinehart514/founder-os
claude plugin install founder-os@founder-os

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