AIに複数レビューさせるのが面倒すぎて、自動で回すプラグインを作ってみた Running Multiple AI Reviews by Hand Was Too Tedious, So I Built a Plugin That Automates It

Introduction

I built a plugin for Claude Code / Codex called dev-orchestra and released 1.0.0.

You make a one-sentence request, and it runs design → implementation → testing → independent reviews by multiple AIs → sorting the findings → fixes → re-testing, in order.

I’ll leave the detailed explanation of what it can do to the next post. Here I’ll write about why I built it and how it went once I built and used it.

dev-orchestra is public on GitHub (MIT license). If it looks useful to you, I’d be happy if you gave it a star.

→ github.com/istb16/dev-orchestra

Before: I Had Become the Courier

Ever since I started having AI write code, one thing had bothered me. AI is bad at reviewing code it wrote itself. When it reviews, it still holds the same assumptions that produced the bug.

So I had started having a different AI review the code too. Here’s what I was doing:

1. Have the agent in terminal A implement it
2. Copy the diff and ask the agents in terminals B and C to review it
3. Wait for them to finish
4. Copy the results back to terminal A and have it fix things

This turned out to be more tedious than I’d imagined.

• When it finishes is different every time. Sometimes it comes back in tens of seconds, sometimes it takes several minutes. In the meantime, all I can do is wait
• The more reviewers I add, the more copy-paste round trips there are
• The findings that come back are duplicated or off the mark. I have to sort out which ones to fix myself before passing them back

In other words, I was supposed to be having AI do the work, yet I had become the courier between AIs.

Was Showing It to Another AI Worth It?

I kept at it despite the effort because it felt like it was paying off.

I checked it with numbers a little later. Once the early version of dev-orchestra was ready, I wrote up a run with it in an earlier post. I had three AIs review a script I had written and was running myself (1418 lines), and they found 11 real bugs. Three of them were ones I would almost certainly never have noticed myself.

That said, this is the result of a single run. The third reviewer (a local LLM) hit only 1 of 6, and there were also defects all three missed. Showing code to several AIs doesn’t make it perfect.

Even so, it was clearly better than “have one AI write it and call it done”. The problem wasn’t the effect; it was the effort.

What I Built: dev-orchestra

So I made it possible to hand off the courier job entirely. You just talk to your agent as usual.

この issue を設定済みのワークフローで実装して

dev-orchestra then picks only the steps the request needs and runs them in order.

A typo fix gets just an edit, a spec change gets everything: the steps change with the size of the request.

What Worked Well

1. Triage strikes the right balance

This was what helped most.

When multiple AIs review code, the findings overlap, and some are off the mark. Pass all of them along with “fix these” and you get a pile of unnecessary changes.

dev-orchestra mechanically merges duplicate findings, then the orchestrator AI sorts them into accepted, rejected, and duplicate. Only the accepted findings reach the fixer. This sorting lands at just the right balance, feeling almost the same as doing it myself.

2. Implementation got more accurate

I didn’t measure and compare this; it’s my impression. Since I started running design, implementation and review as separate steps, I feel implementations mostly come out as intended.

For example, even for a request that was only to write test code, the result was better after going through the review and fix steps.

What I Rebuilt in Three Weeks of Actual Work

I released 0.1.0 on 9/10 and 1.0.0 on 10/12. That’s about a month, but I actually worked on it for about three weeks. In that time I cut around 30 versions. That’s the result of fixing “this is a problem” as I used it. Here are the ones that stuck with me.

The Problem: Tokens Run Out Fast

The biggest problem while building it was that every run costs money (tokens).

Because it runs multiple AIs through the real CLIs, tokens drain fast even when I only meant to run a test. I hit the limit several times and work stopped.

So I gradually added mechanisms to avoid calling AIs needlessly, like “the second review covers only the fixes” and “fewer reviewers for small changes” in the table above.

Still, I don’t think it’s enough yet. Making token use more efficient is the biggest challenge after 1.0.0.

Give It a Try If You Like

In Claude Code, it installs with two lines.

claude plugin marketplace add istb16/dev-orchestra
claude plugin install dev-orchestra@dev-orchestra

It works with Codex too. What it can do and the setups you can use it with are covered in the introduction post.

Because it runs multiple AIs, it uses more tokens than getting by with a single AI. I recommend starting with small requests.

Summary

• Having another AI review code an AI wrote finds bugs you wouldn’t notice yourself
• But by hand, copy-pasting between terminals and waiting turned me into the courier
• dev-orchestra is what lets me hand that off. A one-sentence request runs everything from design to review and fixes
• What worked well: triage strikes the right balance, and I felt implementation (even just tests) got more accurate (my impression)
• The problem: token consumption. Efficiency is the challenge after 1.0.0
• 1.0.0 in about three weeks of actual work. Give it a try if you like

GitHub: istb16/dev-orchestra (a star would be very encouraging)