> It would be good to specifically measure what Detail alone finds, versus what all the bots together find. We could look at that as a completely separate measure.
Yea, it does seem like running a combo of review bots is the right meta. It would be pretty interesting to see how Detail stacks up against 3 or 4 review bots all together (for some anec-data though - posthog has 4 review bots and we seem to find important stuff there).
> Second, when the same AI model is doing the judging, it’s being unfair.
Yup, agreed. We use a combo of models rn (not primarily Claude). The two review bots are Codex and Gemini reviewed by Claude.
> Maybe compare to a more realistic baseline for the DIY side for more compelling benchmarketing?
This is fair critique. However, I don’t really trust myself to write a great code review skill for vLLM or OpenClaw. I also don’t think Claude Code is the right harness for this deep and broad scanning work. We find that it struggles to maintain clarity when considering many different bugs at the same time. The coding agents seem really great at single-goal tasks that they can Ralph their way to.
> We started with a DIY code review skill because it's inherent to want to customize to our codebase and infra before trying solutions that add layers which may get in our way here.
Being able to tinker deeply with the tools is pretty inherent to my love of dev tools in general. Our job is to make use of all of those customizations (our agent will use that 1 page skill when doing its bug finding). I also still think externalizing part of your dev workflow is the right way to get ahead. You really don’t want to do the work of eval-ing/maintaining that skill to make sure it still performs well with a mythos or something.
> and more on token efficiency.
I’m really confident in our ability to stretch $20 of tokens ;)
Wait, definitely no refusal! Is there a public repo with a good skill we can measure against?