MultiModel Code Review Workflow Packaged as Reusable Skill

A developer has packaged a multi-model code review workflow as a reusable skill/runbook, available on GitHub. The approach uses one orchestrator agent coordinating several independent reviewer agents/models, then consolidates findings into a final review. The key insight: different models catch different bugs, and when multiple models independently flag the same issue, confidence goes up. The orchestrator deduplicates, filters weak findings, checks obvious false positives, and publishes one clean result.

Two Modes

The workflow supports two modes:

• PR review: prepares local base/, head/, PR_DIFF.patch, and PR_METADATA.json, spawns multiple analysis-only reviewers, then the orchestrator posts one summary plus inline PR comments. Designed to avoid GitHub pending-review collisions.
• Non-PR scope review: review a folder/module/deploy/scripts area; reviewers inspect a shared local snapshot; the orchestrator publishes a consolidated REVIEW.md into the repo.

Tested Model Setup

The tested setup uses GPT-5.5 as orchestrator, with GPT-5.5, DeepSeek V4 Pro, Kimi K2.6, Qwen 3.6 Plus, and GLM-5.1 as reviewers. However, the workflow is agent/model agnostic. It has been tested with OpenClaw and reportedly gives great results.

Key Lessons

• Shared snapshot: don't let every subagent clone/fetch the repo independently. The orchestrator should prepare a shared snapshot and pass local paths to reviewers. Faster, cheaper, and fewer weird false positives.
• All non-GPT models were run through Fireworks. For large tasks, the author suggests swapping Kimi and Qwen for something else, as those two sometimes choke.

The repo is at github.com/rmichelena/multireview. The author seeks feedback, especially from those running multi-agent code review workflows.