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rulebook-ai

rulebook-ai中频

by botingw

综合评分详见右侧面板

Elevate vibe coding to vibe engineering: Get consistent Github Copilot custom instructions, Cursor, Roo Code, Cline, Windsurf, Claude Code, Gemini Cli, Codex CLI, kilo code, warp …

效率

安装此 Skill

一键安装(GitHub 托管)
git clone https://github.com/botingw/rulebook-ai ~/.claude/skills/rulebook-ai

简介

rulebook-ai 是一个命令行工具,用于将统一的规则、上下文和工具打包并部署到多种 AI 编码助手(如 Cursor、Claude Code、Copilot 等),实现跨工具的便携式 AI 环境管理。它通过可版本化、可组合的 Pack 和 Profile 机制,解决 AI 助手缺乏项目记忆、指令不一致和专业化不足的问题,提升 vibe coding 到 vibe engi

SKILL.md 预览

Python 3.9+ License: MIT

  • For bug report, use issues or use rulebook-ai command rulebook-ai bug-report
  • For real-time chat, community support, and to share your ideas, Join our Discord
  • Have specific feedback or want a personal demo? Book a Chat on my Calendar

Rulebook-AI: The AI Environment Manager

rulebook-ai is a command-line tool for packaging and deploying consistent, expert environments—rules, context, and tools—to your favorite AI coding assistants.

Stop wasting time re-explaining your project's architecture or manually copy-pasting instructions between different AIs. With rulebook-ai, you define your AI's environment once, and deploy it anywhere.

The Problem: AI Assistants are Generic and Isolated

AI coding assistants are powerful, but they operate in a vacuum.

  1. They are forgetful: They have no long-term memory of your project's specific architecture, libraries, or goals.
  2. They are inconsistent: Instructions you give to Cursor don't work in Gemini, and your Copilot context is siloed.
  3. They are unspecialized: An AI's general knowledge is not enough for expert-level tasks. A great "React developer" AI needs different rules, context, and tools than a great "DevOps engineer" AI.

The Solution: Portable and Composable AI Environments

rulebook-ai solves this by treating an AI's entire operational context as a portable Environment that you can manage like code. An Environment consists of three parts:

  • Rules: The AI's operating instructions and workflows.
  • Context: A persistent knowledge base (your project's "memory").
  • Tools: Helper scripts the AI can use to perform tasks.

These environments are packaged into versionable, shareable Packs.

Why rulebook-ai is the Answer

| Value Proposition | How rulebook-ai Delivers | | ---------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Portability Across Assistants | Define an environment once in a universal Pack. The project sync command automatically translates and deploys it to any supported AI (Cursor, Gemini, Copilot, etc.). Use the best AI for the job without losing context. | | Deep Specialization for Any Task | Create or use packs for specific roles (Product Manager, DevOps) or technologies (React, AWS, Data Science). Instantly "onboard" your AI with the expert knowledge and tools it needs for the task at hand. | | Composable & Versionable Context | Treat your AI's environment as code. Mix and match packs to build the perfect setup for any project. Use Profiles (named groups of packs) to instantly switch between entire configurations. | | Community-Driven Expertise | Don't reinvent the wheel. rulebook-ai is a platform for a community of experts to build and share packs, creating a public library of best practices for AI-assisted development. | | Clean & Predictable Workspace | The tool cleanly separates your user-owned content (memory/, tools/) from framework-managed artifacts (.rulebook-ai/, generated rules), keeping your project tidy and predictable. | | Total Control Over Sources | Go beyond the public index. Add packs directly from any GitHub repo (github:) or develop and test them from your local filesystem (local:). You have a secure path for private packs and a seamless workflow for creating new ones. |

Quick Start with uvx

# 1. Install uv if you don't have it yet
curl -fsSL https://astral.sh/uv/install.sh | bash

# 2. Add a pack to your project (e.g., the light-spec starter pack)
uvx rulebook-ai packs add light-spec

# 3. Sync the environment to your workspace
uvx rulebook-ai project sync

This will create a .rulebook-ai directory to manage state, and populate memory/ and tools/ with starters. It also generates the assistant-specific rule files (e.g., .cursor/rules/, GEMINI.md).

For a more detailed walkthrough of all features, see the Step-by-Step Tutorial.

Supported AI Assistants

rulebook-ai supports generating rule files for multiple AI coding assistants. Supported assistants include: Cursor, Windsurf, Cline, RooCode, Kilo Code, Warp, GitHub Copilot, Claude Code, Codex CLI, and Gemini CLI.

For detailed per‑assistant behavior, file locations, and format notes, see: memory/docs/user_guide/supported_assistants.md.

You can generate rules for one or more assistants using the --assistant flag, for example:

rulebook-ai project sync --assistant cursor copilot

Or generate rules for all supported assistants with:

rulebook-ai project sync --all

How It Works: The Pack System

The core of rulebook-ai is a simple, powerful workflow:

  1. Add Packs: You add one or more Packs to your project's library. A pack can be built-in, from the community, or from your own local directory.
  2. Sync Project: You run rulebook-ai project sync. The tool reads your selected packs, copies over any starter memory/ and tools/, and generates the final rule files in the correct format for each AI assistant you use.

This workflow ensures your project's "AI Environment" is explicit, versionable, and easy to manage.

Your First Environment: The Built-in Packs

rulebook-ai comes with a few packs to get you started immediately. The light-spec pack is the recommended starting point for any new project.

  • light-spec:

    • Benefit: Installs a foundational software development lifecycle environment. It teaches your AI to think like a junior developer, following systematic processes for planning, coding, and debugging. It also provides starter templates for your project's documentation (memory/).
    • Target Users: Everyone. It's the ideal first pack to add to any project.
  • medium-spec & heavy-spec:

    • Benefit: These provide more verbose rules and stricter guardrails, perfect for when you need the AI to be more cautious and detailed, such as during a complex code review or refactoring.
    • Target Users: Developers who want more explicit guidance and checks from their AI assistant.

Project Structure after Sync

flowchart TD
    A[Your Project] --> D[... your other files];

    A --> E[memory/];
    A --> F[tools/];

    A --> I[/.rulebook-ai/];
    A --> J[/.cursor/];
    A --> K[... other generated rules];


    subgraph "Version Control (git)"
        direction LR
        subgraph "Commit These (Your Environment)"
            E & F
        end
        subgraph "Ignore These (Generated Artifacts)"
            I & J & K
        end
    end

    style E fill:#cde4f9,stroke:#8ab4e2
    style F fill:#cde4f9,stroke:#8ab4e2

    style I fill:#f9d4c3,stroke:#e2a48a
    style J fill:#f9d4c3,stroke:#e2a48a
    style K fill:#f9d4c3,stroke:#e2a48a
  • Your Environment (memory/, tools/): This is your project's unique context. You own it, you edit it, and you commit it to version control.
  • Generated Artifacts (.rulebook-ai/, .cursor/, etc.): These are managed by the CLI. They should be added to .gitignore as they can be regenerated at any time.

Contributing

Join our Discord if any questions or idea discussion about contribution. This project thrives on community contributions. You can contribute by:

  • Creating and Sharing Packs: Got a great set of rules and tools for a specific framework or role? Package it up and share it! See the Pack Developer Guide.
  • Reporting Bugs or Ideas: Open an Issue in the repo. The rulebook-ai bug-report command will take you there.
  • Contributing the repo: go to CONTRIBUTING.md

统计信息

中频

使用频率(自动计算)

91

Fork

Python

语言

MIT

开源协议

2025/10/3

更新

综合评分

2.5
社区认可
3
使用热度
3
文档完整
3
功能丰富
1
维护活跃
1

基于社区认可、使用热度、文档完整度、功能丰富度、维护活跃度自动计算

标签

AI环境管理CLI工具规则管理多AI支持vibe coding开发效率

链接

GitHub

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