Multi model review

Multi-Model Review

Two independent review lanes run in parallel, then every merged finding is adversarially verified against the actual code. Replaces the retired multi-model-review workflow: per MODELS.md there is no Claude shim - the main loop (Root) runs the codex lane itself via direct background Bash.

Inputs: repo (absolute path, required), base (default main), focus (default: correctness, security, edge cases, API contracts, maintainability).

Shared review contract (put in BOTH lane prompts):

Repo: <repo>. Review the diff git diff <base>...HEAD; if that diff is empty, review staged+unstaged changes instead. Focus: <focus>. You are READ-ONLY: do not edit any file. Read the changed files fully - not just the diff hunks. Report findings with file:line, why each matters, and the precise fix. If the change is clean, use verdict "ship" with zero findings - do not invent issues.

Phase 1 - launch both lanes in parallel (same message)

Opus lane - Agent(model: 'opus', effort: 'high', run_in_background: true). Prompt = shared contract + "You are the Claude reviewer lane. Set reviewer to "opus-5". Return ONLY a JSON object matching <skill-dir>/references/findings-schema.json (read it first) - no prose around it."

<skill-dir> throughout means this skill's base directory (provided when the skill is invoked) - substitute the real absolute path in every prompt/command.

Codex lane - direct codex exec, no relay agent:

1. Fill <skill-dir>/references/adversarial-prompt.md (this skill's own template - its output contract matches findings-schema.json): TARGET_LABEL = "diff vs <base> in <repo>", USER_FOCUS = focus, REVIEW_COLLECTION_GUIDANCE = "Collect the diff yourself with git (git diff <base>...HEAD, falling back to uncommitted changes if empty) and read changed files fully.", REVIEW_INPUT = "Use git and file reads in the repo working directory." Write it to <scratchpad>/mmr-prompt.md.
2. Run ONE bare background Bash command (600000ms timeout):

codex exec -m gpt-5.6-sol -c model_reasoning_effort="high" -s read-only --cd <repo> --output-schema <skill-dir>/references/findings-schema.json --output-last-message <scratchpad>/mmr-codex-findings.json - < <scratchpad>/mmr-prompt.md

Effort routing per MODELS.md (2026-07-24 recalibration): "high" (Sol worker) is the default review tier; "medium" only for trivial bounded diffs. Never xhigh.

3. On completion, read mmr-codex-findings.json; set reviewer to "gpt-5.6-sol" if absent.

Phase 2 - lane failure semantics (never skip)

• A lane that errored or returned unusable output = degraded coverage: say so explicitly in the final report; the other lane's verdict stands alone.
• BOTH lanes failed = no verdict. Report the failure and stop - a total lane failure must never read as a clean "ship".
• Zero raw findings across live lanes = verdict "ship"; skip Phase 3.

Phase 3 - adversarial verify

Spawn one verify agent - Agent(model: 'opus', effort: 'high') - with the shared contract, the merged lane JSON, and:

Adversarially VERIFY each finding against the actual code: open the cited file:line, confirm the claim is real on THIS diff (not stale, hypothetical, or about pre-existing code), dedupe overlapping findings across lanes (set confirmedBy to "both" when lanes agree - that raises confidence), and reject false positives with concrete evidence. Order confirmed findings by severity. Overall verdict: "blocker" if any confirmed critical/high, "changes-recommended" if any confirmed finding remains, else "ship". Return ONLY a JSON object matching <skill-dir>/references/verified-schema.json.

For a tiny finding set (≤3), Root may verify inline instead of spawning.

Presenting results

• Lead with the overall verdict, then confirmed findings ranked by severity, verbatim in substance; list rejected findings with their rejection reasons.
• Flag degraded coverage prominently if a lane failed.
• Never auto-apply fixes. The main loop applies fixes only when the user asks or an approved implementation task already covers them.