Adversarial review
Review matters. The market has already priced it: CodeRabbit, a company that does nothing but AI code review, grew into a high-value business on that premise alone. If a dedicated reviewer is worth a company, it's worth a seat on your team.
lmctl's edge is where and who the reviewer is:
- A different provider and model than the author. This is adversarial review, not self-review. A model can't rubber-stamp its own blind spots when the reviewer isn't that model. Diversity of provider is the point — see Players & model diversity.
- A local player with full codebase context. The reviewer is a member of your team running on your machine. It can read the whole repository, its history, the durable-memory record, and the surrounding code — not just a PR diff. A remote SaaS reviewer sees the diff; a local adversarial player sees the project.
Recommended setups
We've used and tested two pairings intensively:
- Claude as Lead + Codex as Reviewer
- Codex as Lead + Claude as Reviewer
Both put a strong, independent model on the review seat. A teamfile for the first:
_MEMBER_ alias=Lead provider=claude
_MEMBER_ alias=Coder provider=codex
_MEMBER_ alias=Reviewer provider=codex
The Lead plans and delegates, the Coder implements, and the Reviewer — a different provider than the Lead — reads the change against the full project and pushes back. Swap the providers to get the second setup.
How the approaches compare
The qualities that matter for a reviewer, framed across three approaches:
| Quality | lmctl adversarial local reviewer | Single-model self-review | Remote PR-only reviewer |
|---|---|---|---|
| Independent model (not the author)? | Yes | No | Yes |
| Full repo history & context? | Yes | Yes | No — sees the diff |
| Runs locally on your machine? | Yes | Yes | No |
| Knows durable-memory / project record? | Yes | Sometimes | No |
This is a qualitative frame, not a benchmark — the takeaway is that the local adversarial reviewer is the only column with "yes" across all four rows.
Related
- Players & model diversity
- Context & durable memory — what the reviewer reads from.
- Concepts & glossary
We have real usage data from running these setups intensively; side-by-side review examples and the measured analysis are being prepared and will be presented here.