> They are all just variations of "insert a canned prompt", varying only along the dimensions of (a) how and where the prompt is installed and from where it is sourced, and (b) which context or contexts the prompt runs in. There's not much advice here about which option is best, and no clear best practices seem to have emerged yet either. Personally, I find just asking Claude to review the code works well enough.

The subagent approach is structurally different from the others because it runs with clean context. That has three major effects:

1. All other things being equal, it will result in a lower cost-to-solution because of the quadratic cost scaling of an LLM session (input token or cached-input cost being paid with each new round).

2. The review model will not be able to 'cheat' by retaining assumptions from the main session, such as "x must be done like y." For people, this is why having a separate person perform code review (or, if not possible, reviewing code after a mind-clearing break) is handy; the applicability of this analogy to LLMs is vague but reasonable.

3. The main model will only see the results of the review, not the detailed reasoning that leads up to it. On one hand this avoids more context pollution, but on the other hand it might lead to duplicative logic to re-discover the mechanics behind bugs found.

> I checked the session logs to see how often the agents were actually invoking the LSP tools. The answer was they had invoked them literally once the entire time.

I think the intent behind 'install a language server plugin' is that these tools should lint automatically after every edit, without waiting for an explicit call from the LLM.