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Nx Generate

Organization
nrwl
nx-generate

Generate code using nx generators. INVOKE IMMEDIATELY when user mentions scaffolding, setup, structure, creating apps/libs, or setting up project structure. Trigger words - scaffold, setup, create a new app, create a new lib, project structure, generate, add a new project. ALWAYS use this BEFORE calling nx_docs or exploring - this skill handles discovery internally.

Overview

Publishernrwl
Repositorynx-ai-agents-config
Skill namenx-generate
Stars
28
Forks
8
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 nrwl on GitHub. Read the source before you install it.

Installation

Install the Nx Generate 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.
    https://github.com/nrwl/nx-ai-agents-config/tree/main/generated/.agents/skills/nx-generate
  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/nrwl/nx-ai-agents-config.git /tmp/nx-ai-agents-config
mkdir -p .claude/skills
cp -r /tmp/nx-ai-agents-config/generated/.agents/skills/nx-generate .claude/skills/nx-generate
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nx Generate 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 Nx Generate 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 Nx Generate 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.

Run Nx Generator

Nx generators are powerful tools that scaffold projects, make automated code migrations or automate repetitive tasks in a monorepo. They ensure consistency across the codebase and reduce boilerplate work.

This skill applies when the user wants to:

  • Create new projects like libraries or applications
  • Scaffold features or boilerplate code
  • Run workspace-specific or custom generators
  • Do anything else that an nx generator exists for

Key Principles

  1. Always use --no-interactive - Prevents prompts that would hang execution
  2. Read the generator source code - The schema alone is not enough; understand what the generator actually does
  3. Match existing repo patterns - Study similar artifacts in the repo and follow their conventions
  4. Verify with lint/test/build/typecheck etc. - Generated code must pass verification. The listed targets are just an example, use what's appropriate for this workspace.

Steps

1. Discover Available Generators

Use the Nx CLI to discover available generators:

  • List all generators for a plugin: npx nx list @nx/react
  • View available plugins: npx nx list

This includes plugin generators (e.g., @nx/react:library) and local workspace generators.

2. Match Generator to User Request

Identify which generator(s) could fulfill the user's needs. Consider what artifact type they want, which framework is relevant, and any specific generator names mentioned.

IMPORTANT: When both a local workspace generator and an external plugin generator could satisfy the request, always prefer the local workspace generator. Local generators are customized for the specific repo's patterns.

If no suitable generator exists, you can stop using this skill. However, the burden of proof is high—carefully consider all available generators before deciding none apply.

3. Get Generator Options

Use the --help flag to understand available options:

bash
npx nx g @nx/react:library --help

Pay attention to required options, defaults that might need overriding, and options relevant to the user's request.

Library Buildability

Default to non-buildable libraries unless there's a specific reason for buildable.

TypeWhen to useGenerator flags
Non-buildable (default)Internal monorepo libs consumed by appsNo --bundler flag
BuildablePublishing to npm, cross-repo sharing, stable libs for cache hits--bundler=vite or --bundler=swc

Non-buildable libs:

  • Export .ts/.tsx source directly
  • Consumer's bundler compiles them
  • Faster dev experience, less config

Buildable libs:

  • Have their own build target
  • Useful for stable libs that rarely change (cache hits)
  • Required for npm publishing

If unclear, ask the user: "Should this library be buildable (own build step, better caching) or non-buildable (source consumed directly, simpler setup)?"

4. Read Generator Source Code

This step is critical. The schema alone does not tell you everything. Reading the source code helps you:

  • Know exactly what files will be created/modified and where
  • Understand side effects (updating configs, installing deps, etc.)
  • Identify behaviors and options not obvious from the schema
  • Understand how options interact with each other

To find generator source code:

  • For plugin generators: Use node -e "console.log(require.resolve('@nx/<plugin>/generators.json'));" to find the generators.json, then locate the source from there
  • If that fails, read directly from node_modules/<plugin>/generators.json
  • For local generators: Typically in tools/generators/ or a local plugin directory. Search the repo for the generator name.

After reading the source, reconsider: Is this the right generator? If not, go back to step 2.

⚠️ --directory flag behavior can be misleading. It should specify the full path of the generated library or component, not the parent path that it will be generated in.

bash
# ✅ Correct - directory is the full path for the library
nx g @nx/react:library --directory=libs/my-lib
# generates libs/my-lib/package.json and more

# ❌ Wrong - this will create files at libs and libs/src/...
nx g @nx/react:library --name=my-lib --directory=libs
# generates libs/package.json and more

5. Examine Existing Patterns

Before generating, examine the target area of the codebase:

  • Look at similar existing artifacts (other libraries, applications, etc.)
  • Identify naming conventions, file structures, and configuration patterns
  • Note which test runners, build tools, and linters are used
  • Configure the generator to match these patterns

6. Dry-Run to Verify File Placement

Always run with --dry-run first to verify files will be created in the correct location:

bash
npx nx g @nx/react:library --name=my-lib --dry-run --no-interactive

Review the output carefully. If files would be created in the wrong location, adjust your options based on what you learned from the generator source code.

Note: Some generators don't support dry-run (e.g., if they install npm packages). If dry-run fails for this reason, proceed to running the generator for real.

7. Run the Generator

Execute the generator:

bash
nx generate <generator-name> <options> --no-interactive

Tip: New packages often need workspace dependencies wired up (e.g., importing shared types, being consumed by apps). The link-workspace-packages skill can help add these correctly.

8. Modify Generated Code (If Needed)

Generators provide a starting point. Modify the output as needed to:

  • Add or modify functionality as requested
  • Adjust imports, exports, or configurations
  • Integrate with existing code patterns

Important: If you replace or delete generated test files (e.g., *.spec.ts), either write meaningful replacement tests or remove the test target from the project configuration. Empty test suites will cause nx test to fail.

9. Format and Verify

Format all generated/modified files:

bash
nx format --fix

This example is for built-in nx formatting with prettier. There might be other formatting tools for this workspace, use these when appropriate.

Then verify the generated code works. Keep in mind that the changes you make with a generator or subsequent modifications might impact various projects so it's usually not enough to only run targets for the artifact you just created.

bash
# these targets are just an example!
nx run-many -t build,lint,test,typecheck

These targets are common examples used across many workspaces. You should do research into other targets available for this workspace and its projects. CI configuration is usually a good guide for what the critical targets are that have to pass.

If verification fails with manageable issues (a few lint errors, minor type issues), fix them. If issues are extensive, attempt obvious fixes first, then escalate to the user with details about what was generated, what's failing, and what you've attempted.

Frequently asked questions

What does the Nx Generate AI skill do?

Generate code using nx generators. INVOKE IMMEDIATELY when user mentions scaffolding, setup, structure, creating apps/libs, or setting up project structure. Trigger words - scaffold, setup, create a new app, create a new lib, project structure, generate, add a new project. ALWAYS use this BEFORE calling nx_docs or exploring - this skill handles discovery internally.

Why use Nx Generate on TypingMind?

Because you install it once and use it with any model. Nx Generate 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 Nx Generate in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nrwl/nx-ai-agents-config/tree/main/generated/.agents/skills/nx-generate. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Nx Generate?

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 Nx Generate?

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

Is the Nx Generate AI skill free?

Yes. It is published on GitHub by nrwl 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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