Monorepo Management logo

Monorepo Management

CommunityPopular
wshobson
monorepo-management

Master monorepo management with Turborepo, Nx, and pnpm workspaces to build efficient, scalable multi-package repositories with optimized builds and dependency management. Use when setting up monorepos, optimizing builds, or managing shared dependencies.

Overview

Publisherwshobson
Repositoryagents
Skill namemonorepo-management
Stars
39.8K
Forks
4.2K
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by wshobson on GitHub. Read the source before you install it.

Installation

Install the Monorepo Management AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/wshobson/agents.git /tmp/agents
mkdir -p .claude/skills
cp -r /tmp/agents/plugins/developer-essentials/skills/monorepo-management .claude/skills/monorepo-management
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Monorepo Management in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Monorepo Management on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Monorepo Management is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Monorepo Management

Build efficient, scalable monorepos that enable code sharing, consistent tooling, and atomic changes across multiple packages and applications.

When to Use This Skill

  • Setting up new monorepo projects
  • Migrating from multi-repo to monorepo
  • Optimizing build and test performance
  • Managing shared dependencies
  • Implementing code sharing strategies
  • Setting up CI/CD for monorepos
  • Versioning and publishing packages
  • Debugging monorepo-specific issues

Core Concepts

1. Why Monorepos?

Advantages:

  • Shared code and dependencies
  • Atomic commits across projects
  • Consistent tooling and standards
  • Easier refactoring
  • Simplified dependency management
  • Better code visibility

Challenges:

  • Build performance at scale
  • CI/CD complexity
  • Access control
  • Large Git repository

2. Monorepo Tools

Package Managers:

  • pnpm workspaces (recommended)
  • npm workspaces
  • Yarn workspaces

Build Systems:

  • Turborepo (recommended for most)
  • Nx (feature-rich, complex)
  • Lerna (older, maintenance mode)

Turborepo Setup

Initial Setup

bash
# Create new monorepo
npx create-turbo@latest my-monorepo
cd my-monorepo

# Structure:
# apps/
#   web/          - Next.js app
#   docs/         - Documentation site
# packages/
#   ui/           - Shared UI components
#   config/       - Shared configurations
#   tsconfig/     - Shared TypeScript configs
# turbo.json      - Turborepo configuration
# package.json    - Root package.json

Configuration

json
// turbo.json
{
  "$schema": "https://turbo.build/schema.json",
  "globalDependencies": ["**/.env.*local"],
  "pipeline": {
    "build": {
      "dependsOn": ["^build"],
      "outputs": ["dist/**", ".next/**", "!.next/cache/**"]
    },
    "test": {
      "dependsOn": ["build"],
      "outputs": ["coverage/**"]
    },
    "lint": {
      "outputs": []
    },
    "dev": {
      "cache": false,
      "persistent": true
    },
    "type-check": {
      "dependsOn": ["^build"],
      "outputs": []
    }
  }
}
json
// package.json (root)
{
  "name": "my-monorepo",
  "private": true,
  "workspaces": ["apps/*", "packages/*"],
  "scripts": {
    "build": "turbo run build",
    "dev": "turbo run dev",
    "test": "turbo run test",
    "lint": "turbo run lint",
    "format": "prettier --write \"**/*.{ts,tsx,md}\"",
    "clean": "turbo run clean && rm -rf node_modules"
  },
  "devDependencies": {
    "turbo": "^1.10.0",
    "prettier": "^3.0.0",
    "typescript": "^5.0.0"
  },
  "packageManager": "pnpm@8.0.0"
}

Package Structure

json
// packages/ui/package.json
{
  "name": "@repo/ui",
  "version": "0.0.0",
  "private": true,
  "main": "./dist/index.js",
  "types": "./dist/index.d.ts",
  "exports": {
    ".": {
      "import": "./dist/index.js",
      "types": "./dist/index.d.ts"
    },
    "./button": {
      "import": "./dist/button.js",
      "types": "./dist/button.d.ts"
    }
  },
  "scripts": {
    "build": "tsup src/index.ts --format esm,cjs --dts",
    "dev": "tsup src/index.ts --format esm,cjs --dts --watch",
    "lint": "eslint src/",
    "type-check": "tsc --noEmit"
  },
  "devDependencies": {
    "@repo/tsconfig": "workspace:*",
    "tsup": "^7.0.0",
    "typescript": "^5.0.0"
  },
  "dependencies": {
    "react": "^18.2.0"
  }
}

Detailed patterns and worked examples

Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.

Best Practices

  1. Consistent Versioning: Lock dependency versions across workspace
  2. Shared Configs: Centralize ESLint, TypeScript, Prettier configs
  3. Dependency Graph: Keep it acyclic, avoid circular dependencies
  4. Cache Effectively: Configure inputs/outputs correctly
  5. Type Safety: Share types between frontend/backend
  6. Testing Strategy: Unit tests in packages, E2E in apps
  7. Documentation: README in each package
  8. Release Strategy: Use changesets for versioning

Common Pitfalls

  • Circular Dependencies: A depends on B, B depends on A
  • Phantom Dependencies: Using deps not in package.json
  • Incorrect Cache Inputs: Missing files in Turborepo inputs
  • Over-Sharing: Sharing code that should be separate
  • Under-Sharing: Duplicating code across packages
  • Large Monorepos: Without proper tooling, builds slow down

Publishing Packages

bash
# Using Changesets
pnpm add -Dw @changesets/cli
pnpm changeset init

# Create changeset
pnpm changeset

# Version packages
pnpm changeset version

# Publish
pnpm changeset publish
yaml
# .github/workflows/release.yml
- name: Create Release Pull Request or Publish
  uses: changesets/action@v1
  with:
    publish: pnpm release
  env:
    GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
    NPM_TOKEN: ${{ secrets.NPM_TOKEN }}

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Monorepo Management AI skill do?

Master monorepo management with Turborepo, Nx, and pnpm workspaces to build efficient, scalable multi-package repositories with optimized builds and dependency management. Use when setting up monorepos, optimizing builds, or managing shared dependencies.

Why use Monorepo Management on TypingMind?

Because you install it once and use it with any model. Monorepo Management is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Monorepo Management in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wshobson/agents/tree/main/plugins/developer-essentials/skills/monorepo-management. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Monorepo Management?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Monorepo Management?

As many as you like. As long as a model supports skills, you can use Monorepo Management with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Monorepo Management AI skill free?

Yes. It is published on GitHub by wshobson under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇