Spawn one reviewer per configured model to adversarially review code changes. The adversarial signal comes from model diversity, not assigned personas. The deliverable is a synthesized verdict. Do NOT auto-apply changes.
Dispatch contract. Read provider-dispatch.md before launching reviewers. Configured entries are provider-qualified descriptors; the parent starts native and external read-only lanes directly. On Codex, resolve remaining Claude tool names via codex-tools.md.
Step 1, Scope
Identify what to review from context:
- If the user points at specific files or a diff, use that.
- If on a feature branch, run
git diff main...HEAD(or the appropriate base branch) for the full changeset. - If the user's message references recent work, gather the relevant files.
Package the diff or file contents with any surrounding context files the reviewers need to understand the code.
Step 2, Intent
Before spawning reviewers, state the intent in one clear paragraph from the user's message, commit messages, the PR description if one exists, and the code. If you're unsure about the intent, ask the user before proceeding.
Step 3, Spawn Reviewers
Start all reviewers in one fan-out phase, one per entry on the interrogate reviewers line of the current harness's pstack model sheet, labeled Reviewer A, B, and so on to match the entry count. If the sheet or that line is missing, use the table defaults. Native reviewers use the parent subagent primitive. External reviewers use the launcher directly and must return a complete, model-verified receipt.
| Subagent | Default model |
|---|---|
| Reviewer A | claude:opus@max |
| Reviewer B | codex:gpt-6.1-sol@max |
| Reviewer C | grok:grok-4.7@xhigh |
Route each reviewer's descriptor with read-only access and a unique output and receipt path. Use the parent subagent primitive without a model override for an auto or inherit-parent entry, so that reviewer runs on the parent model.
If a provider, login, or model is unavailable, record a dropout and continue with the completed reviewers. Never pick the closest model or silently fall back; that destroys the meaning of cross-provider agreement.
Read references/reviewer-prompt.md and fill in the same template for every reviewer with the stated intent, the diff or file contents, the rubric from references/rubric.md, and the code-quality lens from references/code-quality-review.md.
Step 4, Synthesize
As results come back, build a unified picture. Parse every reviewer's findings. Merge findings that describe the same issue differently, and note which models raised each one. Findings two or more models raised independently are the highest signal. Read a lone model's finding, but weight it accordingly. Note disagreements. If one model flags something and another explicitly says the opposite, that is context for the verdict.
Step 5, Lead Judgment
You are the lead reviewer, a pragmatic senior engineer, not a neutral aggregator. Read references/lead-judgment.md for the full framework. Put every finding in one of four buckets, with the model(s) that raised it and a one-line rationale.
- Act on. Real issues affecting correctness, security, or maintainability given the actual goals. They would block a real PR.
- Consider. Legitimate, but you're not sure they outweigh the cost of addressing them now. Worth the user's attention.
- Noted. Technically valid but not actionable. Context-dependent, premature optimization, or low-impact at this stage.
- Dismissed. Wrong, nitpicky, or missing context, with a brief reason.
Output Format
Intent
[The stated intent paragraph from Step 2]
Reviewers
- Reviewer [label]: [model name], [N findings] (one bullet per reviewer)
Act On
[Each: description, which models raised it, why it matters.]
Consider
[Each: description, which models raised it, the tradeoff.]
Noted
[Brief list.]
Dismissed
[Each with a brief rationale.]
Agreement Map
[Where models agreed and diverged, and what that pattern tells us.]