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Turborepo Caching

CommunityPopular
wshobson
turborepo-caching

Configure Turborepo for efficient monorepo builds with local and remote caching. Use when setting up Turborepo, optimizing build pipelines, or implementing distributed caching.

Overview

Publisherwshobson
Repositoryagents
Skill nameturborepo-caching
Stars
39.8K
Forks
4.2K
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Turborepo Caching 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/turborepo-caching .claude/skills/turborepo-caching
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Turborepo Caching 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 Turborepo Caching 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 Turborepo Caching 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.

Turborepo Caching

Production patterns for Turborepo build optimization.

When to Use This Skill

  • Setting up new Turborepo projects
  • Configuring build pipelines
  • Implementing remote caching
  • Optimizing CI/CD performance
  • Migrating from other monorepo tools
  • Debugging cache misses

Core Concepts

1. Turborepo Architecture

Workspace Root/
├── apps/
│   ├── web/
│   │   └── package.json
│   └── docs/
│       └── package.json
├── packages/
│   ├── ui/
│   │   └── package.json
│   └── config/
│       └── package.json
├── turbo.json
└── package.json

2. Pipeline Concepts

ConceptDescription
dependsOnTasks that must complete first
cacheWhether to cache outputs
outputsFiles to cache
inputsFiles that affect cache key
persistentLong-running tasks (dev servers)

Templates

Template 1: turbo.json Configuration

json
{
  "$schema": "https://turbo.build/schema.json",
  "globalDependencies": [".env", ".env.local"],
  "globalEnv": ["NODE_ENV", "VERCEL_URL"],
  "pipeline": {
    "build": {
      "dependsOn": ["^build"],
      "outputs": ["dist/**", ".next/**", "!.next/cache/**"],
      "env": ["API_URL", "NEXT_PUBLIC_*"]
    },
    "test": {
      "dependsOn": ["build"],
      "outputs": ["coverage/**"],
      "inputs": ["src/**/*.tsx", "src/**/*.ts", "test/**/*.ts"]
    },
    "lint": {
      "outputs": [],
      "cache": true
    },
    "typecheck": {
      "dependsOn": ["^build"],
      "outputs": []
    },
    "dev": {
      "cache": false,
      "persistent": true
    },
    "clean": {
      "cache": false
    }
  }
}

Template 2: Package-Specific Pipeline

json
// apps/web/turbo.json
{
  "$schema": "https://turbo.build/schema.json",
  "extends": ["//"],
  "pipeline": {
    "build": {
      "outputs": [".next/**", "!.next/cache/**"],
      "env": ["NEXT_PUBLIC_API_URL", "NEXT_PUBLIC_ANALYTICS_ID"]
    },
    "test": {
      "outputs": ["coverage/**"],
      "inputs": ["src/**", "tests/**", "jest.config.js"]
    }
  }
}

Template 3: Remote Caching with Vercel

bash
# Login to Vercel
npx turbo login

# Link to Vercel project
npx turbo link

# Run with remote cache
turbo build --remote-only

# CI environment variables
TURBO_TOKEN=your-token
TURBO_TEAM=your-team
yaml
# .github/workflows/ci.yml
name: CI

on:
  push:
    branches: [main]
  pull_request:

env:
  TURBO_TOKEN: ${{ secrets.TURBO_TOKEN }}
  TURBO_TEAM: ${{ vars.TURBO_TEAM }}

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - uses: actions/setup-node@v4
        with:
          node-version: 20
          cache: "npm"

      - name: Install dependencies
        run: npm ci

      - name: Build
        run: npx turbo build --filter='...[origin/main]'

      - name: Test
        run: npx turbo test --filter='...[origin/main]'

Template 4: Self-Hosted Remote Cache

typescript
// Custom remote cache server (Express)
import express from "express";
import { createReadStream, createWriteStream } from "fs";
import { mkdir } from "fs/promises";
import { join } from "path";

const app = express();
const CACHE_DIR = "./cache";

// Get artifact
app.get("/v8/artifacts/:hash", async (req, res) => {
  const { hash } = req.params;
  const team = req.query.teamId || "default";
  const filePath = join(CACHE_DIR, team, hash);

  try {
    const stream = createReadStream(filePath);
    stream.pipe(res);
  } catch {
    res.status(404).send("Not found");
  }
});

// Put artifact
app.put("/v8/artifacts/:hash", async (req, res) => {
  const { hash } = req.params;
  const team = req.query.teamId || "default";
  const dir = join(CACHE_DIR, team);
  const filePath = join(dir, hash);

  await mkdir(dir, { recursive: true });

  const stream = createWriteStream(filePath);
  req.pipe(stream);

  stream.on("finish", () => {
    res.json({
      urls: [`${req.protocol}://${req.get("host")}/v8/artifacts/${hash}`],
    });
  });
});

// Check artifact exists
app.head("/v8/artifacts/:hash", async (req, res) => {
  const { hash } = req.params;
  const team = req.query.teamId || "default";
  const filePath = join(CACHE_DIR, team, hash);

  try {
    await fs.access(filePath);
    res.status(200).end();
  } catch {
    res.status(404).end();
  }
});

app.listen(3000);
json
// turbo.json for self-hosted cache
{
  "remoteCache": {
    "signature": false
  }
}
bash
# Use self-hosted cache
turbo build --api="http://localhost:3000" --token="my-token" --team="my-team"

Template 5: Filtering and Scoping

bash
# Build specific package
turbo build --filter=@myorg/web

# Build package and its dependencies
turbo build --filter=@myorg/web...

# Build package and its dependents
turbo build --filter=...@myorg/ui

# Build changed packages since main
turbo build --filter='...[origin/main]'

# Build packages in directory
turbo build --filter='./apps/*'

# Combine filters
turbo build --filter=@myorg/web --filter=@myorg/docs

# Exclude package
turbo build --filter='!@myorg/docs'

# Include dependencies of changed
turbo build --filter='...[HEAD^1]...'

Template 6: Advanced Pipeline Configuration

json
{
  "$schema": "https://turbo.build/schema.json",
  "pipeline": {
    "build": {
      "dependsOn": ["^build"],
      "outputs": ["dist/**"],
      "inputs": ["$TURBO_DEFAULT$", "!**/*.md", "!**/*.test.*"]
    },
    "test": {
      "dependsOn": ["^build"],
      "outputs": ["coverage/**"],
      "inputs": ["src/**", "tests/**", "*.config.*"],
      "env": ["CI", "NODE_ENV"]
    },
    "test:e2e": {
      "dependsOn": ["build"],
      "outputs": [],
      "cache": false
    },
    "deploy": {
      "dependsOn": ["build", "test", "lint"],
      "outputs": [],
      "cache": false
    },
    "db:generate": {
      "cache": false
    },
    "db:push": {
      "cache": false,
      "dependsOn": ["db:generate"]
    },
    "@myorg/web#build": {
      "dependsOn": ["^build", "@myorg/db#db:generate"],
      "outputs": [".next/**"],
      "env": ["NEXT_PUBLIC_*"]
    }
  }
}

Template 7: Root package.json Setup

json
{
  "name": "my-turborepo",
  "private": true,
  "workspaces": ["apps/*", "packages/*"],
  "scripts": {
    "build": "turbo build",
    "dev": "turbo dev",
    "lint": "turbo lint",
    "test": "turbo test",
    "clean": "turbo clean && rm -rf node_modules",
    "format": "prettier --write \"**/*.{ts,tsx,md}\"",
    "changeset": "changeset",
    "version-packages": "changeset version",
    "release": "turbo build --filter=./packages/* && changeset publish"
  },
  "devDependencies": {
    "turbo": "^1.10.0",
    "prettier": "^3.0.0",
    "@changesets/cli": "^2.26.0"
  },
  "packageManager": "npm@10.0.0"
}

Debugging Cache

bash
# Dry run to see what would run
turbo build --dry-run

# Verbose output with hashes
turbo build --verbosity=2

# Show task graph
turbo build --graph

# Force no cache
turbo build --force

# Show cache status
turbo build --summarize

# Debug specific task
TURBO_LOG_VERBOSITY=debug turbo build --filter=@myorg/web

Best Practices

Do's

  • Define explicit inputs - Avoid cache invalidation
  • Use workspace protocol - "@myorg/ui": "workspace:*"
  • Enable remote caching - Share across CI and local
  • Filter in CI - Build only affected packages
  • Cache build outputs - Not source files

Don'ts

  • Don't cache dev servers - Use persistent: true
  • Don't include secrets in env - Use runtime env vars
  • Don't ignore dependsOn - Causes race conditions
  • Don't over-filter - May miss dependencies

Frequently asked questions

What does the Turborepo Caching AI skill do?

Configure Turborepo for efficient monorepo builds with local and remote caching. Use when setting up Turborepo, optimizing build pipelines, or implementing distributed caching.

Why use Turborepo Caching on TypingMind?

Because you install it once and use it with any model. Turborepo Caching 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 Turborepo Caching in TypingMind?

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

Which AI models can use Turborepo Caching?

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 Turborepo Caching?

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

Is the Turborepo Caching 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.

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