I packaged our multi-model code review workflow as a reusable skill

Hey folks — I’ve been experimenting with a workflow for code reviews where one orchestrator agent coordinates several independent reviewer agents/models, then consolidates the findings into one final review. The idea is simple: * different models catch different bugs; * if 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. I turned the workflow into a reusable skill/runbook here: [https://github.com/rmichelena/multireview](https://github.com/rmichelena/multireview) It supports two modes: 1. **PR review** * prepares local `base/`, `head/`, `PR_DIFF.patch`, `PR_METADATA.json` * spawns multiple analysis-only reviewers * orchestrator posts one summary + inline PR comments * avoids GitHub pending-review collisions 2. **Non-PR scope review** * review a folder/module/deploy/scripts area * reviewers inspect a shared local snapshot * orchestrator publishes a consolidated [`REVIEW.md`](http://REVIEW.md) into the repo The tested setup uses: * GPT-5.5 as orchestrator * GPT-5.5 * DeepSeek V4 Pro * Kimi K2.6 * Qwen 3.6 Plus * GLM-5.1 …but the workflow itself is agent/model agnostic. It’s been tested with OpenClaw and it's been giving great results. One thing I learned quickly (and which became commit #2): 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 the non-GPT models I've been running through Fireworks. If the task is large, you might want to swap Kimi and Qwen for something else, those two choke sometimes. Would love feedback, especially from anyone running multi-agent code review workflows.