点击这里观看开场视频

想让Codex帮你安装吗？把这个粘贴到一个新的Codex帖子里：

如果某个技能能自我提升呢？

递归自我提升是人工智能研究者用来描述那些在我们不知情的情况下变得更好的系统。通常这是一个思想实验。这个实验运行在你的笔记本电脑上。

给技能RSI一个目标，它会写一个特工技能来追逐它。把你已经拥有的技能交给它，它会让那个技能变得更好。然后循环开始：研究技能，形成一个有用的理论，构建变体，测试它，保持赢家。一次又一次，独自完成，直到你停止它。

在写出一行之前，它先研究该领域。它通过引用的主张、从业者词汇和互文语境进行研究，然后将它们汇编成本体论：一个工作模型，说明技能服务对象、优秀产出是什么样子、值得捕捉的失败模式、新手会模糊的专家区分，以及那些强烈观点的权威，从而让“好”的含义更加清晰。这个本体论成为每个后续循环的共享地图，也是防止工作漂移的护栏。

Skill RSI

Watch the Intro Video here

Want Codex to install it for you? Paste this into a fresh Codex thread:

Set up Skill RSI from https://github.com/justinwetch/Skill-RSI. Clone the repo, read AGENTS.md, install dependencies, build the UI, configure and install the Codex plugin, run the plugin smoke checks, and tell me when to start a fresh Codex thread. Do not start any model-backed Skill RSI run yet.

What if a skill could improve itself?

Recursive self-improvement is the phrase AI researchers reach for when they describe systems that get better without us in the loop. Usually it’s a thought experiment. This one runs on your laptop.

Give Skill RSI a goal and it writes an Agent Skill to chase it. Hand it a skill you already have and it makes that one better instead. Then the loop starts: study the skill, form a theory about what would help, build a variant, test it, keep the winner. Again and again, on its own, until you stop it.

Before it writes a single line, it studies the domain. It researches the field into sourced claims, practitioner vocabulary, and intertextual context, then compiles them into an ontology: a working model of who the skill serves, what excellent output looks like, the failure modes worth catching, the expert distinctions novices flatten, and the authorities whose strong opinions sharpen what “good” means here. That ontology becomes the shared map every later loop builds on and the guardrail that keeps the work from drifting.

Once there’s a champion to beat, Skill RSI deconstructs it into more than a dozen testable surfaces, every one carrying a hypothesis, a regression risk, and a way to measure whether the change actually worked. Each loop the current champion is the control and the next variant is the treatment, both run against the same prompts under the same criteria. One variable moves at a time, so when a challenger wins, you know exactly what won.

Skill RSI improves skills the way a research lab works: every loop is an experiment whose results are kept and built on, including a record of dead ends, so it never wastes time re-testing what doesn’t work. Each pass starts better informed than the last, converging on what does.

You set the goal. The research, the experiments, the evaluation, and the record-keeping run themselves.

This is the sequel to SkillEval. SkillEval could tell you which of two skills was better. The question it left open was what to build next, and that part was still on you. Skill RSI closes the loop.

For the origin story, see Open Sourcing SkillEval.