systemix docs

What is Systemix?

Systemix runs little experiments on your website, writes down what it learned and why, and suggests the next one. You approve every decision.

That's the whole product. It runs in Claude Code, and everything it knows lives as plain files in your repo — no platform, no dashboard you don't own.

1. Proposequeue.jsonsuggests the next experiment2. Buildexperiments/<id>.mdxthe bet, written down3. MeasurePostHog evidencereal numbers, pulled daily4. LearnLEARNINGS.mdwhat happened, and whyyou approveyou approvethe notebook seeds the next idea

The four steps

  1. Propose — the daily runner suggests the next experiment as a card in .systemix/queue.json, built from what the loop has already learned. It never creates anything itself — you say yes or no.
  2. Build — the experiment becomes a file: experiments/<id>.mdx. The bet, who it's for, and the number that will prove it.
  3. Measure — the runner pulls real numbers (PostHog) into the file, every day, on its own.
  4. Learn — when the numbers are strong enough, you get a card recommending a decision. You close it; the lesson is written to experiments/LEARNINGS.md — the notebook the next proposal starts from.

What you approve

The system moves on its own, but three things always wait for your OK:

  • Starting a new experiment — the runner proposes; /init-experiment is how you accept.
  • Closing an experiment — the runner recommends promote / iterate / kill; you decide.
  • Changing its own skills or rules — always proposed, never self-applied.

Three ways to drive it

  • Claude Code skills/systemix-init, /measure, /close-experiment and more
  • CLI — systemix experiment new | list | close | learnings and systemix loop
  • MCP — experiment_new, experiment_list, experiment_close for any agent that speaks MCP

All three doors read and write the same files.

Optional adapters — a design-system spine (design/, guardrails, tokens) and Figma sync ship in the box, but the loop doesn't need them. See the design-system adapter.

Start here

Getting started — set up the loop in a new or existing repo

The loop — each step in detail, and what the daily runner does

The experiment — the anatomy of one experiment file

Why files & loops — why the memory lives on disk

Skills reference — every skill, generated from the source

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