multi-review

name	multi-review
description	Review the current diff — first a cheap deterministic SonarQube-in-Docker pre-pass (fix + re-scan until clean), then fan out to three single-focused reviewer subagents in parallel, auto-apply the safe quality cleanups, and tell the human which areas to review first. Explicit invocation only — user types /multi-review.
disable-model-invocation	true

/multi-review — review, fix, hand off

Run a quality review of the current uncommitted changes, apply the low-risk fixes yourself, and end by pointing the human at what still needs their eyes.

Steps

1. Confirm there's something to review. Run git --no-pager diff --stat HEAD. If empty, say "Nothing to review." and stop.

2. Cheap deterministic pre-pass FIRST — exhaust SonarQube before spending tokens on the LLM reviewers. Run .claude/skills/multi-review/sonar-diff-review.sh (boots a reused SonarQube container in Docker and scans only the changed backend Java files). Then loop on its exit code:

   • exit 0 (Sonar clean, or nothing for it to scan) → the cheap pre-pass is done; go to step 3 (the LLM reviewers).
   • exit 2 (Sonar found issues) → read its printed findings and petclinic-backend/target/sonar-diff-review.json. Fix the clearly-safe / mechanical ones (same auto-fix-vs-defer triage as step 4); leave genuine judgment calls for the human. Then re-run the script to confirm. Repeat this fix→re-scan loop until Sonar reports exit 0 (or only human-deferred issues remain — cap at 3 iterations, then move on and list the rest for the human).
   • exit 1 (Sonar could not run — e.g. Docker down / disk full) → note it briefly and continue to step 3 anyway; never block the review on Sonar infrastructure. Do not fix anything beyond what Sonar flagged during this pre-pass.

3. Fan out in parallel. In ONE message, make THREE Agent calls at once (so they run concurrently — never one after another): reviewer-reuse, reviewer-simplification, reviewer-efficiency. Give each the same task: "Review the current working-tree change (git --no-pager diff HEAD) for your single dimension. Return findings as a short list of file:line — issue → suggestion. If clean, say so." Collect the three results into one set of findings grouped by dimension (Reuse / Simplification / Efficiency), dropping duplicates.

4. Triage each finding:

   • Auto-fix — mechanical, low-risk, clearly correct (dead code, obvious duplication, a needless allocation, a trivially simpler form). Apply it.
   • Defer to human — anything ambiguous, behavior-changing, or a judgment call. Leave it untouched. This is quality only — do not chase correctness bugs here; if you happen to spot one, just note it for the human.

5. Apply the safe fixes and keep a short list of what you changed (file + what).

6. Hand off (the important part). End your response with this section, ordered by risk — widest blast radius and deferred findings first:

   ## Review these first (human)
   - <file:area> — <why it needs human judgment: deferred finding / risky change / high blast radius>
   

   Be specific about files and why. If nothing needs human review, say so explicitly.

Output shape

1. One-line summary: Sonar: S issues fixed over R rounds · N LLM findings · auto-fixed X · deferred Y.
2. What you auto-fixed (bullets with paths), separating the Sonar pre-pass fixes from the LLM-review fixes.
3. The Review these first (human) section.

Do not commit or push — leave that to the human.