Version 2.5.0 ベータ版

AIエージェントと科学的研究を行うためのClaudeコードスキルのセット。

1つの前提で構築されています:可能なAIエージェントとの作業は、ツーリングの問題ではなく、管理問題です。 エージェントが実験を実行したり、コードを書き、紙をドラフトしたりできると、ボトルネックは人間側に移動します。目標がまさにあなたのものではなく、自分が何をしたかについてもっと知っている才能のあるチームを指示、見直し、修正する方法は? 経営科学は1世紀の課題について考えています。 このフレームワークは、一人のAIラボにその答えを適用します。

哲学

指示は、指示しません。 良い司令官のやり方を委任する: why は、done が好きなもの、not が犠牲になるもの、そしてエージェントが手段を選ぶようにしましょう。 この枠組みのルールは、その理由で出荷され、エージェントは次のように語っています。ルールの文字がその理由に対抗するとき、そのルールを失明に従わないようチャレンジしてください。 チェックポイントフォルダを完全に再構築するような、その理由なしで続くルールは、誰も読んでいません。

**図はインターフェイスです.

Vibe Research

Version 2.5.0 beta · 中文版

A set of Claude Code skills for doing scientific research with AI agents.

It is built on one premise: working with capable AI agents is a management problem, not a tooling problem. Once agents can run experiments, write code, and draft papers, the bottleneck moves to the human side: how do you direct, review, and correct a talented team whose goals are not exactly yours and who knows more about what it just did than you do? Management science has thought about that problem for a century. This framework applies its answers to the one-person AI lab.

The philosophy

State intent, not instructions. Delegate the way good commanders do: say why, what done looks like, and what must not be sacrificed, then let the agent choose the means. Every rule in this framework ships with its reason, and agents are told: when the letter of a rule conflicts with its reason, challenge the rule instead of obeying it blindly. A rule followed without its reason produces absurd results, like perfectly restructuring a checkpoint folder nobody will ever read.

Figures are the interface. Statistics are compression applied before you know what matters, so they silently discard the surprises. Every experiment must end in figures: full distributions, not summary numbers. Figures are also the one information channel that is not filtered through the agent’s own report of its work: they separate how it went from how the agent says it went. The human judges by looking; that judgment is deliberately never automated, because any fixed metric handed to an optimizer stops measuring what you meant (Goodhart’s law).

Decide by recovery cost, not by git. Agents get full autonomy where mistakes are cheap to undo: scratch space, small experiments, exploration branches. They must ask first where recovery is expensive, and expensive is measured in human terms, not repository terms. Rewriting a paper’s narrative is irreversible even though git can revert it, because once the author has read and absorbed a version, their mind does not roll back.

Organize by what the human reads. Directories the human reads are kept small and scannable; directories they never read (checkpoints, caches) have no tidiness rules at all. Every simplicity constraint in this framework is conditioned on a reader actually existing.