multi-review

name	multi-review
description	Multi-model code review. Runs code-review skill with 3 models in parallel, then synthesizes findings.

Multi Review

Runs the code-review skill with 3 different models in parallel, then synthesizes with active validation.

Process

Phase 1: Gather Reviews

1. Get the PR diff (same as code-review)

   # If PR number provided, use it. Otherwise current branch.
   gh pr diff [PR_NUMBER] > /tmp/pr-diff.txt
   

2. Run 3 parallel reviews via bash

   pi -p --model claude-opus-4-5 "Read and follow ~/dev/pi-skills/code-review/SKILL.md to review the PR. Diff is at /tmp/pr-diff.txt" > /tmp/review-opus.md &
   pi -p --model gpt-5.2-codex "Read and follow ~/dev/pi-skills/code-review/SKILL.md to review the PR. Diff is at /tmp/pr-diff.txt" > /tmp/review-codex.md &
   pi -p --model gemini-2.5-pro "Read and follow ~/dev/pi-skills/code-review/SKILL.md to review the PR. Diff is at /tmp/pr-diff.txt" > /tmp/review-gemini.md &
   wait
   

Phase 2: Active Validation (IMPORTANT)

Do not blindly trust the reviewers. Validate each finding yourself.

3. Read PR context first Before looking at sub-agent reviews, get the full picture:

   # What the PR claims to do
   gh pr view [PR_NUMBER] --json title,body
   
   # What it actually does
   cat /tmp/pr-diff.txt
   
   # What others have already said
   gh pr view [PR_NUMBER] --json comments,reviews --jq '.comments[].body, .reviews[].body'
   

   Form your own impressions. Note any issues already flagged in PR feedback.

4. Collect all findings Build a deduplicated list of every issue from all 3 reviews. Note which model(s) found each issue.

5. Validate EACH finding For every finding, actually look at the code and verify:

   • Is this a real bug/issue? (check the code, don't just trust the claim)
   • Is it a false positive? (model hallucinated or misunderstood)
   • What file/line is affected? (verify it exists and matches)

6. Score by IMPACT, not consensus Rate each validated issue by actual severity:

   • 🔴 Critical: Breaks functionality, security issue, data loss
   • 🟠 High: Real bugs, incorrect behavior, major guideline violations
   • 🟡 Medium: Performance, maintainability, edge cases
   • 🟢 Low: Style, minor improvements, nitpicks

   Consensus count (2+ models) ≠ importance.

   • Consensus often means "obvious issue any reviewer would catch"
   • Unique findings may be subtle insights worth MORE attention, not less

7. Flag unique findings for extra scrutiny When only one model found something:

   • WHY did only one catch it? (deeper insight vs hallucination?)
   • Validate more carefully - could be the most important find
   • Could also be a false positive - verify against actual code

8. Check for gaps What might ALL models have missed?

   • Complex state/timing issues (e.g., async race conditions)
   • Claimed features that don't actually work (check PR description)
   • Subtle logic errors in control flow
   • Look at the PR description - are all claims implemented?

Phase 3: Synthesized Output

9. Output format

# 🔍 Multi-Model PR Review: [PR title]

## Validated Issues

### 🔴 Critical

[Issues that must be fixed - functionality broken, security, etc.]

### 🟠 High Priority

[Real bugs, incorrect behavior - should fix before merge]

### 🟡 Medium Priority

[Performance, maintainability, edge cases - should discuss]

### 🟢 Low Priority

[Style, minor improvements - nice to have]

Each issue should include:

- **File**: path/to/file.ext#L10-L15
- **Status**: ✅ Confirmed | ⚠️ Needs verification | ❌ False positive
- **Found by**: Opus / Codex / Gemini / PR feedback
- **Description**: What's wrong and why it matters
- **Suggestion**: How to fix (if applicable)

## ❌ False Positives Filtered

[List any findings that were wrong, with brief explanation]

## ⚠️ Potential Gaps

[Things all models may have missed - especially check PR description claims]

## 📊 Model Coverage

| Issue   | Opus | Codex | Gemini | PR  | Status            |
| ------- | :--: | :---: | :----: | :-: | ----------------- |
| Issue 1 |  ✅  |  ✅   |   ❌   |  -  | ✅ Confirmed      |
| Issue 2 |  ❌  |  ✅   |   ❌   |  -  | ✅ Confirmed      |
| Issue 3 |  ✅  |  ❌   |   ✅   |  -  | ❌ False positive |
| Issue 4 |  ❌  |  ❌   |   ❌   | ✅  | ⚠️ Models missed!  |

## Final Verdict

**[MERGE / FIX FIRST / NEEDS DISCUSSION]**

[Brief explanation of verdict]

Key Principles

1. Validate, don't just synthesize - You are the senior reviewer, not a secretary
2. Unique findings deserve MORE attention - They might be the deepest insights
3. Consensus ≠ importance - Obvious issues get caught by all; critical bugs may be subtle
4. Check what's missing - The worst bugs are the ones no one found
5. Compare against PR description - Do claimed features actually work?