能力标签
多模型AI辩论工具
⚙️
Agent工作流

多模型AI辩论工具

基于 JavaScript · 无代码搭建完整 AI 自动化流程
英文名:llm-peer-review
⭐ 6 Stars 🍴 1 Forks 💻 JavaScript 📄 MIT 🏷 AI 7.0分
7.0AI 综合评分
AI辩论多模型代码审查工作流JavaScript
✦ AI Skill Hub 推荐

经 AI Skill Hub 精选评估,多模型AI辩论工具 获评「推荐使用」。这款Agent工作流在功能完整性、社区活跃度和易用性方面表现出色,AI 评分 7.0 分,适合有一定技术背景的用户使用。

📚 深度解析

多模型AI辩论工具 是一套完整的 AI Agent 自动化工作流方案。随着 AI 能力的不断提升,基于 Agent 的自动化工作流正在成为提升个人和团队效率的核心方式。区别于传统的 RPA 自动化(模拟鼠标键盘操作),AI Agent 工作流通过理解任务意图、动态规划执行路径,能够处理更复杂的非结构化任务。

多模型AI辩论工具 工作流的设计遵循"最小配置,最大复用"原则:核心逻辑已经封装好,用户只需配置自己的 API Key 和业务参数即可快速上手。工作流内置错误处理和重试机制,在网络波动或 API 限速等情况下仍能稳定运行,适合作为生产环境的自动化基础设施。

在实际部署时,建议先在测试环境中运行 3-5 次,验证各个环节的输出结果符合预期,再部署到生产环境。AI Skill Hub 评分 7.0 分,是同类 Agent 工作流中的精选推荐。

📋 工具概览

多模型AI辩论工具 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。

GitHub Stars
⭐ 6
开发语言
JavaScript
支持平台
Windows / macOS / Linux
维护状态
轻量级项目,按需更新
开源协议
MIT
AI 综合评分
7.0 分
工具类型
Agent工作流
Forks
1

📖 中文文档

以下内容由 AI Skill Hub 根据项目信息自动整理,如需查看完整原始文档请访问底部「原始来源」。

多模型AI辩论工具 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。

📌 核心特色
  • 可视化 Agent 工作流编排,无需编写复杂代码
  • 支持多步骤自动化任务链,实现全流程无人值守
  • 与外部 API、数据库和第三方服务无缝集成
  • 内置错误处理与自动重试机制,保障稳定运行
  • 提供可复用的自动化模板,快速在同类场景部署
🎯 主要使用场景
  • 自动化日常重复性工作,将精力集中于创造性任务
  • 构建数据采集 → 处理 → 输出的完整自动化管线
  • 实现跨平台、跨系统的数据流转和业务协同
以下安装命令基于项目开发语言和类型自动生成,实际以官方 README 为准。
安装命令
# 方式一:npm 全局安装
npm install -g llm-peer-review

# 方式二:npx 直接运行(无需安装)
npx llm-peer-review --help

# 方式三:项目依赖安装
npm install llm-peer-review

# 方式四:从源码运行
git clone https://github.com/mayankmankhand/llm-peer-review
cd llm-peer-review
npm install
npm start
📋 安装步骤说明
  1. 访问 GitHub 仓库获取工作流文件
  2. 在对应平台(Dify / Flowise / Make 等)中找到「导入工作流」功能
  3. 上传工作流文件
  4. 按照提示配置必要的环境变量和 API Key
  5. 运行测试确认流程正常后投入使用
以下用法示例由 AI Skill Hub 整理,涵盖最常见的使用场景。
常用命令 / 代码示例
# 命令行使用
llm-peer-review --help

# 基本用法
llm-peer-review [options] <input>

# Node.js 代码中使用
const llm_peer_review = require('llm-peer-review');

const result = await llm_peer_review.run(options);
console.log(result);
以下配置示例基于典型使用场景生成,具体参数请参照官方文档调整。
配置示例
# llm-peer-review 配置说明
# 查看配置选项
llm-peer-review --config-example > config.yml

# 常见配置项
# output_dir: ./output
# log_level: info
# workers: 4

# 环境变量(覆盖配置文件)
export LLM_PEER_REVIEW_CONFIG="/path/to/config.yml"
📑 README 深度解析 真实文档 完整度 78/100 含工作流图 查看 GitHub 原文 →
以下内容由系统直接从 GitHub README 解析整理,保留代码块、表格与列表结构。

LLM Peer Review

AI peer review for your work. (Also: a structured workflow.)

<img src="docs/images/ask-gpt-summary.png" alt="ask-gpt summary showing agreed points, disagreed points, recommended actions, and key insights" width="700">

A real /ask-gpt debate output: Claude and ChatGPT argue across up to three rounds and hand you a structured verdict (what they agreed on, where they disagreed, and a prioritized action list). You approve what gets implemented.

This toolkit gives you slash commands for every step of a project: explore the problem, create a plan, build, review, then run a debate (up to 3 rounds) between Claude and ChatGPT (or Gemini). Works for product specs, research plans, competitive analysis, and code.

Inspired by Zevi Arnovitz's workflow on Lenny's Podcast. The key difference: Zevi manually copies feedback between models. This toolkit automates the entire debate loop with two commands (/ask-gpt and /ask-gemini).

This is the same consensus/divergence synthesis that Perplexity's Model Council produces and what Karpathy's LLM Council does for general Q&A, applied to a full project lifecycle with multi-round adversarial debate and an implementation workflow.

New to slash commands? A slash command is a shortcut you type into your AI editor's chat panel - it starts with / and tells the AI to run a specific workflow. The editors that support them are Claude Code (Anthropic's CLI plus editor panel for Claude) and Cursor (an AI-powered code editor built on VS Code). You'll need one of these installed; see SETUP.md if you're starting from scratch.

---

What's New

Latest release: v6.3.3 (September 2026), a patch that teaches the pre-push tripwire six credential formats it could not see, GitLab's personal access token above all, since the toolkit authenticates with glab on a GitLab install; it also gives that scanner the test file it never had (#164). v6.3.2 (September 2026) was a patch that gave the cycle summary the treatment the review page got: /document now writes one standing page per repository at artifacts/html/cycle.html, replaced each run, leading with what changed and why instead of a file inventory and carrying a running log of earlier cycles so time away does not lose them (#163). v6.3.1 (September 2026) was a patch on v6.3.0 that made two of its promises true in a real run: the evidence on the review page is written by the audit rather than typed, and the standing page is rendered after the fix loop so it shows what is still open and can go empty (#162). v6.3.0 (September 2026), additive on top of v6.2.0, rewrote what a review finding says. The four-field template is gone - a finding is one sentence carrying the defect and the harm, a second only when it can say who is hit or when it fires, a fix line that states a cost, and the check's real output attached instead of paraphrased. The length caps are counted by the renderer rather than requested by a prompt, and the review page becomes one standing page per repository that carries only what is open and can go empty (#161); What v6.3.0 adds has the bullets. v6.2.0 added the design workflow described under /explore above (#160). v6.1.1 was a one-fix patch on v6.1.0, which is additive on top of v6.0.0 (August 2026). v6.0.0 is the one that changed behavior and is still the release to read first: if you are upgrading from v5.x or earlier, start with its bullets directly below.

Setup installs from main, not from a tag, so you get everything on this page, not just the tagged release. The bullets directly below describe v6.0.0; What v6.1.0 adds, What v6.2.0 adds, and What v6.3.0 adds list what the newer releases build on top of it. See the CHANGELOG for the full split.

  • The loop no longer stops at a report. A review used to hand you a list and wait for "fix it". It now fixes the findings that survived its own audit, re-verifies each fix with something other than whatever made it, and starts the next stage on its own. Two per-run phrases take control back, and they do different things: say "report only" and the run changes nothing, say "no chaining" and it finishes its stage without starting the next. Nothing was renamed or removed; what changed is what a command does once it starts.
  • Findings have to prove themselves before you see them. Every finding now ships with a receipt (a read-only command, plus what its output must show), and the run executes it. What survives goes to a fresh skeptic told to refute it, and a Block-severity finding faces three. Expect shorter reports: the first live run killed four of seven findings. What was thrown out is listed rather than hidden.
  • Nothing gets pushed without a secret scan. A tripwire reads every outgoing commit before any push the loop makes, looking for secrets, never-push files, and changes to shared settings. It reads commit by commit rather than the final diff, because a secret added and then removed leaves no trace at the end but still lands in history.
  • It works on GitLab now, not just GitHub. Commands read your git remote and pick gh or glab from what they find, so there is nothing to configure at setup time. GitHub is unchanged.
  • HTML artifacts gained a second viewport. Plans, reviews, and cycle summaries still open in your local browser as before; when the session can publish, it asks your consent, then publishes the same file as a private Claude-hosted page and gives you the link. Purely additive in this release: if publishing is unavailable, nothing is lost. Every publish is recorded in artifacts/html/index.jsonl, and each page is named from its own title. (v6.1.0 then made the hosted page the primary viewport and dropped the ask; see What v6.1.0 adds.)

Upgrading from v5.2.0 or earlier? v6.0.0 also carries v5.3.0 (the application-security review domain) and v5.4.0 (bounded, verifier-gated loops) - one re-run of setup picks up all of it.

Requirements

This toolkit runs on macOS, Linux, or WSL (Windows Subsystem for Linux). Windows users: install WSL first. Native Windows PowerShell also works for setup and all non-debate commands; only /ask-gpt and /ask-gemini require bash/WSL.

---

`/explore` - Understand before you build

Asks 3-4 focused questions about scope, success criteria, and constraints before any code is written. Has two modes: scoping (you have a concrete idea, pressure-test the scope) and vision (you're thinking big-picture, challenge the premise itself). It picks a mode by reading your input, then asks you to confirm. You can switch modes any time. Useful when you're not yet sure what you're actually building. When the conversation converges (no open questions left, and in vision mode a scope dial reading Hold or Reduce), it hands off to /create-plan on its own.

The design workflow. When the feature has a look (a page, a screen, a component), /explore runs a named "Design exploration" step. It checks whether your repo already has a design system and asks you once, then sets a load level: nothing to design, improve something that exists, or build something new. New work gets an idea list you react to, three seeded working prototypes side by side to pick from, and then, during /execute, a fresh-context design critic that scores each round until the design clears a bar or the round budget runs out. A repo that already has a design system keeps it: only layout, composition, motion, and copy vary, and anything further asks you first. Your repo's answers live in DESIGN-PROFILE.md, a file that is yours (seeded once by setup, never overwritten). Image and video generation are optional and sit behind your own keys; without a key the workflow hands you the prompt to run elsewhere and continues. The rules live in .claude/skills/shared/design-rules.md; the keys are covered in API-KEYS.md.

Full prompt: .claude/commands/explore.md

`/execute` - Build it, update the plan as you go

Walks through the plan step by step, updating status emojis and progress in real time. Spawns parallel agents for [parallel] steps, runs [sequential] steps in order. Small failures get at most 3 fix attempts per step. Stops on critical blockers (e.g. the plan assumed an API supports a feature it doesn't) instead of pushing through a broken plan. On a clean finish it hands off to /review; on a blocker it stops there and asks you.

Full prompt: .claude/commands/execute.md

Manual Setup (run the script yourself)

Prefer to run the setup script yourself? Pick the script that matches your shell:

Bash (WSL, macOS, Linux):

bash /path/to/llm-peer-review/scripts/setup/setup.sh /path/to/your-project

PowerShell (setup and non-debate commands - see Requirements):

powershell -ExecutionPolicy Bypass -File C:\path\to\llm-peer-review\scripts\setup\setup.ps1 -Target "C:\path\to\your-project"

Or run from inside your project directory (no target needed):

cd /path/to/your-project
bash /path/to/llm-peer-review/scripts/setup/setup.sh

Note: If you run the script from inside the toolkit repository without specifying a target, it shows an error to prevent accidentally copying files into the wrong place.

Preview first (optional): add --dry-run (bash) or -DryRun (PowerShell) to see exactly what an install or upgrade would do - version gap, migrations, files that would be overwritten, custom files that are left alone, and where backups go - without changing anything:

bash /path/to/llm-peer-review/scripts/setup/setup.sh /path/to/your-project --dry-run

Every real run prints the same pre-flight report before it touches anything.

Local edits are guarded: if you edited a toolkit-managed file, setup detects it (via a hash manifest at .claude/.toolkit-manifest.json) and asks before overwriting - in a terminal it prompts, in scripts and AI-agent runs it stops and lists the files. Add --force (bash) or -Force (PowerShell) after the target path to proceed without the prompt; every replaced file is still backed up first and listed at the end of the run. The guard needs the hash manifest to compare against, so it applies from the second run onward: a first install into a project that already keeps its own root VERSION or .gitattributes replaces them with a backup and no prompt (the pre-flight labels them "provenance unknown").

What setup does: - Copies into your project: commands, skills (including the prebuilt HTML shells), agent definitions (.claude/agents/ - the worker roles /review, /index, /document, and /execute dispatch, carrying their model, effort, and tool settings), both rules files (toolkit.md, html-outputs.md), and all runtime and helper scripts (ask-gpt.js, ask-gemini.js, browse.js, generate-index.js, render-html.js, session-init.js, pre-push-check.js, correction-ledger.js, gen-media.js, open-artifact.sh), plus VERSION and .env.local.example. Detects and removes any legacy INDEX.md. CODEBASE_MAP.md (a semantic map of your project) is generated on your first /explore run, when Claude auto-invokes /index. Setup also lands .gitignore (merged, not overwritten), .gitattributes, VERSION, artifacts/README.md, and the quarantined .claude/scripts/package.json and package-lock.json, and creates an empty plans/ folder for your plan files. - Preserves your work: CLAUDE.md, LESSONS.md (plus its companion LESSONS-detail.md), DESIGN-PROFILE.md (the design workflow's per-repo answers, see /explore above), and settings.local.json are skipped if they already exist - those are yours to customize. Custom files you add inside .claude/commands/, .claude/skills/, .claude/agents/, .claude/scripts/, or .claude/rules/ are never modified or deleted (the toolkit's installer test suite plants a custom file in each of those folders, .claude/agents/ included, and checks it survives an upgrade byte for byte), and anything setup does overwrite is backed up to a timestamped .toolkit-backup-* folder first. One thing setup does add to settings.local.json even on a first install: the two browse.js permission entries that need your project's absolute path, so /review-browser runs without a prompt. - Always updates: the managed rules files (.claude/rules/toolkit.md, .claude/rules/html-outputs.md). - Stays in the toolkit repo: setup scripts (setup.sh, setup.ps1, install-alias.*) are never copied.

See How It Works for details on which files are yours vs. managed by the toolkit.

One install covers everything - it stays inside .claude/scripts/ so your

For /review-browser, install the Chromium browser:

npx --prefix .claude/scripts playwright-core install chromium

On Linux/WSL, also: sudo npx playwright-core install-deps chromium

```

The debate commands and the browser command are optional. Skip the API keys if you don't want /ask-gpt and /ask-gemini. Skip the Chromium install if you don't want /review-browser. The core workflow commands work either way.

</details>

Never set up a dev environment before? Follow the step-by-step guide in SETUP.md. It covers Windows (WSL), Mac, Node.js, GitHub CLI, Cursor, and API keys - everything you need from scratch.
Not using Cursor? The setup guide assumes Cursor, but the toolkit works with any editor that supports Claude Code. Copy the relevant setup page into any AI assistant and ask it to rewrite the steps for your editor.

---

Install the toolkit's runtime packages (one-time, stays in .claude/scripts/).

npm install --prefix .claude/scripts

Open .env.local and paste your API keys

Edit .env.local and paste your OPENAI_API_KEY and GEMINI_API_KEY

Optional: FAL_KEY for video generation in the design workflow (see API-KEYS.md)

For /ask-gpt and /ask-gemini, set up your API keys:

cp .env.local.example .env.local

AI debate commands (/ask-gpt, /ask-gemini): set up API keys

cp .env.local.example .env.local

The Workflow

flowchart TD W(["/worktree (optional)"]) -.-> A(["/explore"]) A --> B(["/create-plan"]) B --> G{"You approve the plan"} G --> C(["/execute"]) C --> D(["/review"]) D --> H(["/document"]) D -.-> E(["/ask-gpt or /ask-gemini"]) E -.-> F(["Agreed · Disagreed · Actions"]) F -.-> H
Solid arrows run on their own; dotted arrows you start yourself. You type /explore and approve the plan. From there /create-plan, /execute, /review, and /document chain automatically. /worktree and the AI debates stay optional and always start with you. If the diagram doesn't render: you type /explore -> /create-plan writes the plan -> you approve it -> /execute -> /review -> /document. Optional and typed by you: /worktree before the run, /ask-gpt or /ask-gemini after the review.

You don't have to use every command every time. Following the order prevents the most common mistake: coding before you've thought it through.

Two ways to take back control, and they do different things. Say "no chaining" to stop the handoff: that run finishes its own stage and does not start the next one. Say "report only" to stop the changing: the run tells you what it found and edits nothing. They are deliberately separate, so you can have either without the other, and both last for one run only. One thing to know about a chained cycle: the review that follows /execute starts on its own, so there is no moment to type "report only" for it. If you want that review to change nothing, say "no chaining" when you approve the plan, then type /review report only yourself.

Want to see this in action? Follow the 5-minute walkthrough in DEMO-SCRIPT.md.
Working on multiple things at once? Use /worktree first to create an isolated copy, then open it in a new Cursor window and run /explore there.

---

Already Have Your Own Workflow?

The toolkit now runs as a loop rather than a set of one-shot commands, and that is the change most likely to affect you if you arrive with your own commands, scripts, or way of working.

What "a loop" means here. The old behavior was report-first: a command found problems, showed you a list, and waited. The new behavior is that a command finds problems, checks them, fixes the ones that survive the check, verifies the fixes, and hands off to the next stage on its own. You type /explore and approve the plan. The rest runs. Two phrases take control back: say "report only" on a run you start and it reports without changing anything (for the review that chains from /execute, see the note under "How key commands work"), or "no chaining" and it finishes that one stage without starting the next.

You do not have to give up what you already have. The loop is a pattern you can add to your own commands, and the rest of this section is how to do that safely, followed by what to check so your files survive an upgrade.

Adding the loop to your own workflow

Automatic fixing is only safe because of what sits around it. If you take the "fix it automatically" half without these, you get the risk with none of the protection. In rough order of how much they matter:

Add thisWhyCheck your command
**An escape phrase**Some runs you want to look before anything movesDoes your command honor "report only" by producing its report and changing nothing? If the phrase does nothing, you have no brake
**Proof attached to every finding**A finding with no proof cannot be checked, so fixing it is guessworkDoes each finding carry a read-only command someone could run, plus a line saying what that output showed?
**A second opinion before the fix**The thing that found a problem is the worst judge of whether it is realDoes anything with a fresh view check a finding before it gets fixed? Finder and judge must not be the same actor
**A different checker after the fix**Models favor their own output and will not reliably catch their own mistakesDoes whatever wrote the fix also declare it verified? If yes, that verification is worth very little
**A retry limit and somewhere to fall back to**Unbounded retrying is how an automatic command turns a small problem into a large oneIs there a number on the attempts, and a commit or checkpoint to revert to when they run out?
**A deletion guard**A fix that restores something a person removed on purpose is a correct-looking regressionBefore re-adding anything, does your command check whether a human deleted it deliberately?

The short version: auto-fixing is a privilege earned by verification. Add the verification first and the automation second.

project's own package.json is never touched.

npm install --prefix .claude/scripts

Browser QA (/review-browser): install Chromium

npx --prefix .claude/scripts playwright-core install chromium

Troubleshooting

  • Commands don't show up in Cursor - Make sure .claude/commands/ exists in your project root with .md files inside. The editor workspace root must be the folder that contains .claude/.
  • /ask-gpt or /ask-gemini fails - Check that npm install --prefix .claude/scripts was run and .env.local has valid API keys.
  • /ask-gpt or /ask-gemini prints a "deprecated model" warning - v4.5.0 auto-overrides outdated GPT_MODEL or GEMINI_MODEL env values with the current default. Edit .env.local to remove or update the stale value if you want to silence the warning. See API-KEYS.md.
  • "setup.sh: command not found" - Run the full command from the setup instructions, not just setup.sh on its own.
  • "target directory does not exist" - Create the project folder first: mkdir -p /path/to/project
  • Script errors with /bin/bash^M or "bad interpreter" - Line-ending issue. Your shell scripts have Windows-style line endings (CRLF) instead of Unix-style (LF). Easiest fix: delete the folder and clone fresh. Advanced fix: run git add --renormalize . && git checkout -- . in the repo.
  • Setup stops with "locally modified file(s) will be overwritten" - You edited a toolkit-managed file, and setup will not silently replace it. Re-run with --force (bash) / -Force (PowerShell) added after the target path to proceed - your version is backed up to .toolkit-backup-<timestamp>/ and listed at the end of the run so re-applying your changes is a checklist.
  • I customized a toolkit file and upgraded - where did it go? - The setup script preserves your original at .toolkit-backup-<timestamp>/ at the project root before overwriting. Copy it back if you want to keep your version. Safe to delete the backup directory when done.
  • Setup one-liner fails partway through - Safe to rerun the command. Leftover /tmp/tmp.* folders are harmless and can be deleted. .toolkit-backup-*/ directories from prior runs are also safe to delete once you have confirmed you do not need th
🇨🇳 中文文档镜像 AI 翻译 2026-05-27
英文原文章节由系统翻译为中文摘要,便于快速理解。完整原文见上方 "📑 README 深度解析"。
📌 简介

llm-peer-review 是一个专为开发者设计的 AI Peer Review 工具,它不仅是一个评审工具,更是一套结构化的开发工作流。通过集成 Claude 和 ChatGPT 的辩论机制,该工具能够针对你的 Plan 或 Code 进行多轮深度讨论,并最终为你提供一份结构化的判决报告(Verdict),清晰列出双方达成一致的点、存在分歧的地方以及优先级排序后的建议操作(Recommended Actions),帮助你提升代码质���。

⚡ 功能介绍

自 v4.3.3 版本升级以来,本项目引入了多项重大功能。v4.4.0 版本新增了 Codebase map 功能,通过 `/index` 生成语义化的 `CODEBASE_MAP.md`,供 `/explore`、`/create-plan` 和 `/pair-debug` 等指令调用,取代了传统的扁平化索引。v4.4.1 版本则增强了 `/review-*` 系列技能的解释能力,采用更丰富的 4 字段结构来呈现发现的问题,让评审结果更加透明、易读。

📋 环境依赖

本项目支持 macOS、Linux 以及 Windows 的 WSL (Windows Subsystem for Linux) 环境。对于 Windows 用户,建议优先安装 WSL 以获得最佳的 Bash 工作流体验。确保你的开发环境能够运行 Node.js 相关脚本,并具备访问 AI 模型 API 的网络条件。

🛠 安装步骤(Docker/pip/源码)

安装过程主要依赖于对项目脚本的初始化。请确保在项目根目录下执行相关安装命令。如果涉及浏览器自动化评审(/review-browser),需要通过 `npx --prefix .claude/scripts playwright-core install chromium` 来安装 Chromium 浏览器内核。此外,请确保项目结构符合指令识别规范,特别是 `.claude/commands/` 目录的存在。

🚀 使用教程

本项目提供了一套完整的指令化操作体验。你可以使用 `/explore` 在编写代码前进行需求边界测试(Scoping)或愿景挑战(Vision);使用 `/execute` 按照计划逐步执行任务,该指令支持并行([parallel])与串行([sequential])任务处理,并能实时更新进度。当需要评审时,调用 `/ask-gpt` 或 `/ask-gemini`,AI 会自动进行多轮辩论并输出总结,你可以直接根据建议进行确认或修改。

⚙️ 配置说明(含 MCP / env)

配置过程非常简单。你需要通过编辑项目根目录下的 `.env.local` 文件来配置必要的 API 密钥。请将你的 `OPENAI_API_KEY` 和 `GEMINI_API_KEY` 填入其中。如果该文件不存在,请先从 `.env.local.example` 复制一份副本,以确保 AI 辩论指令能够正常调用后端模型。

🔌 API 说明

本项目通过 `/ask-gpt` 和 `/ask-gemini` 指令调用大模型能力。在使用这些 AI 辩论指令前,必须完成 API 密钥的配置工作。操作步骤为:首先复制模板文件 `cp .env.local.example .env.local`,然后将获取到的 API Key 写入该文件,从而实现与 OpenAI 或 Google Gemini 模型的安全连接。

🔄 工作流/模块

本项目定义了一套严谨的自动化开发工作流:从可选的 `/worktree` 开始,经过 `/explore` 探索需求,`/create-plan` 生成计划,再到 `/execute` 执行任务。执行完成后进入 `/review` 评审阶段,通过 `/ask-gpt` 或 `/ask-gemini` 进行 AI 辩论,最终生成包含 Agreed、Disagreed 和 Actions 的总结。只有当你批准(Approve)这些结果后,才会进入最后的 `/document` 文档化阶段。

❓ FAQ 摘要

常见问题解答:1. 如果在 Cursor 中无法显示指令,请检查项目根目录是否存在 `.claude/commands/` 文件夹及其内部的 .md 文件,并确保编辑器打开的是该文件夹的父级。2. 如果 `/ask-gpt` 或 `/ask-gemini` 运行失败,请检查是否已运行 `npm install --prefix .claude/scripts`。3. 浏览器评审功能需要额外安装 Chromium 内核。

🎯 aiskill88 AI 点评 B 级 2026-05-24

创新的多模型对比评审框架,适合需要多角度论证的场景。但项目热度不高,文档生态待完善。

📚 实用指南(长尾问题)
适合谁
  • 构建多智能体协作系统的 Agent 开发者
最佳实践
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
部署方案
  • CLI:直接 npm install -g / pip install,命令行调用
  • 云端托管:可放在 Vercel / Railway / Fly.io 等 PaaS 平台
相关搜索
llm-peer-review 中文教程llm-peer-review 安装报错怎么办llm-peer-review Agent 工作流llm-peer-review 与同类工具对比llm-peer-review 最佳实践llm-peer-review 适合谁用

⚡ 核心功能

👥 适合谁
  • 构建多智能体协作系统的 Agent 开发者
⭐ 最佳实践
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
⚠️ 常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)

👥 适合人群

自动化工程师和运维人员项目经理和业务分析师希望减少重复性工作的专业人士数字化转型团队

🎯 使用场景

  • 自动化日常重复性工作,将精力集中于创造性任务
  • 构建数据采集 → 处理 → 输出的完整自动化管线
  • 实现跨平台、跨系统的数据流转和业务协同

⚖️ 优点与不足

✅ 优点
  • +MIT 协议,可免费商用
  • +大幅减少重复性人工操作
  • +可视化流程,清晰直观
  • +可扩展性强,支持复杂场景
⚠️ 不足
  • 初始配置和调试需投入一定时间
  • 强依赖外部服务的稳定性
  • 复杂场景需具备一定技术基础
⚠️ 使用须知

AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。

建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。

📄 License 说明

✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。

🔗 相关工具推荐

📰 相关 AI 新闻
🍿 AI 圈相关吃瓜
🗺️ 相关解决方案
🧩 你可能还需要
基于当前 Skill 的能力图谱,自动补全的工具组合

❓ 常见问题 FAQ

llm-peer-review 是一款JavaScript开发的AI辅助工具。开源AI工作流:Multi-model AI debate for your entire project lifecycle. Plans, specs, research,。⭐6 · JavaScript 主要应用场景包括:项目规划和决策、代码审查与优化、技术方案评审。
💡 AI Skill Hub 点评

AI Skill Hub 点评:多模型AI辩论工具 的核心功能完整,质量良好。对于自动化工程师和运维人员来说,这是一个值得纳入个人工具库的选择。建议先在非生产环境试用,再逐步推广。

⬇️ 获取与下载
⬇ 下载源码 ZIP

✅ MIT 协议 · 可免费商用 · 直接从 aiskill88 服务器下载,无需跳转 GitHub

📚 深入学习 多模型AI辩论工具
查看分步骤安装教程和完整使用指南,快速上手这款工具
🌐 原始信息
原始名称 llm-peer-review
原始描述 开源AI工作流:Multi-model AI debate for your entire project lifecycle. Plans, specs, research,。⭐6 · JavaScript
Topics AI辩论多模型代码审查工作流JavaScript
GitHub https://github.com/mayankmankhand/llm-peer-review
License MIT
语言 JavaScript
🔗 原始来源
🐙 GitHub 仓库  https://github.com/mayankmankhand/llm-peer-review

收录时间:2026-05-24 · 更新时间:2026-05-30 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。

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