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
数据来源：ClawHub。 在 ClawSkills 查看
选择你使用的 Agent
方法一：命令行安装（推荐）
推荐（无需提前安装 clawhub）
npx clawhub@latest --dir ~/.claude/skills install peer-review或使用 clawhub CLI（需提前安装）
clawhub --dir ~/.claude/skills install peer-review⚠️ 需要 Node.js 18+，没有 Node？请使用下方方法二直接下载 ZIP。 安装 Node.js →
方法二：手动下载安装（无需 Node）
下载 ZIP，解压后将文件夹放到以下路径，重启 Agent 即可：
安装路径
~/.claude/skills/peer-review/💡解压后将文件夹放到上方路径，重启 Agent 即可生效
Peer Review 是一款 Design & Creative 技能，为你的 AI 助手赋予强大的新能力。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:
Edge cases:
安装只需几秒，即可在 AI 工作流中立即使用。
Peer Review 通过模型上下文协议（MCP）直接与 Claude、Cursor、OpenClaw 集成，让你的 AI 智能体获得默认情况下没有的专业工具。
安装后，你可以在对话中自然地调用其功能——只需描述你想做的事，AI 助手就会使用 Peer Review 来完成。
安装 Peer Review 只需几秒。将技能文件夹放到对应目录：
# 个人级（所有项目可用）
~/.claude/skills/peer-review/
# 项目级（仅当前项目）
.claude/skills/peer-review/放置好技能文件夹后，重启你的 AI 客户端（Claude Code、Cursor、Gemini CLI、OpenClaw 等）。之后可以用 /peer-review 主动调用，或让 AI 根据上下文自动发现并使用。
Peer Review 兼容 Claude、Cursor、OpenClaw。它遵循开放的 MCP（模型上下文协议）标准，无需任何修改即可在所有兼容 MCP 的 AI 客户端上运行。
安装 Peer Review 后，可以对 AI 说这些话来触发它
Help me get started with Peer Review
Explains what Peer Review does, walks through the setup, and runs a quick demo based on your current project
Use Peer Review to multi-model peer review layer using local LLMs via Ollama to catch ...
Invokes Peer Review with the right parameters and returns the result directly in the conversation
What can I do with Peer Review in my design & creative workflow?
Lists the top use cases for Peer Review, with example commands for each scenario
将技能文件夹放到 ~/.claude/skills/peer-review/ 目录（个人级，所有项目可用），或 .claude/skills/peer-review/（项目级）。重启 AI 客户端后，用 /peer-review 主动调用，或让 AI 根据上下文自动发现并使用。
Peer Review 支持 Claude、Cursor、OpenClaw，可与这些 AI 平台无缝集成，扩展其能力。
Peer Review 可免费安装使用。请查阅仓库了解许可证信息。
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
Automate my design & creative tasks using Peer Review
Identifies repetitive steps in your workflow and sets up Peer Review to handle them automatically
Peer Review 属于「Design & Creative」分类，该分类的技能帮助 AI 智能体在此领域执行专业任务。