How Teams Use Peer Review to Collaborate More Effectively

Peer Review isn't just for solo use — teams can share skills and build consistent AI workflows across the organization. Multi-model peer review layer using local LLMs via Ollama to catch errors in cloud model output. Fan-out critiques to 2-3 local models, aggregate flags, synthesize consensus. Use when: validating trade analyses, reviewing agent output quality, testing local model accuracy, checking any high-stakes Claude output before publishing or acting on it. Don't use when: simple fact-checking (just search the web), tasks that don't benefit from multi-model consensus, time-critical decisions where 60s latency is unacceptable, reviewing trivial or low-stakes content. Negative examples: - "Check if this date is correct" → No. Just web search it. - "Review my grocery list" → No. Not worth multi-model inference. - "I need this answer in 5 seconds" → No. Peer review adds 30-60s latency. Edge cases: - Short text (<50 words) → Models may not find meaningful issues. Consider skipping. - Highly technical domain → Local models may lack domain knowledge. Weight flags lower. - Creative writing → Factual r This guide covers how to deploy Peer Review for your team, standardize prompts, and create shared workflows that everyone can use.

Step-by-Step Guide

1. 1

   Install Peer Review in your project directory: .claude/skills/peer-review/

2. 2

   Commit the skill folder to your repository so the whole team has access

3. 3

   Document your team's standard prompts in a shared README

4. 4

   Use Peer Review in code reviews, standups, and planning sessions

5. 5

   Iterate: collect feedback from the team and refine your prompts

Example Prompts

Copy these prompts and use them with your AI agent after installing Peer Review

More Use Cases for Peer Review