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Nx Generate

OrganizationPopular
tech-leads-club
nx-generate

Generate code using Nx generators — scaffold projects, libraries, features, or run workspace-specific generators with proper discovery, validation, and verification. Use when user says "create a new library", "scaffold a component", "generate code with Nx", "run a generator", "nx generate", or any code scaffolding task in a monorepo. Prefers local workspace-plugin generators over external plugins. Do NOT use for running build/test/lint tasks (use nx-run-tasks) or workspace configuration (use nx-workspace).

Overview

Publishertech-leads-club
Repositoryagent-skills
Skill namenx-generate
Stars
7K
Forks
560
Bundled files
Instructions only
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 tech-leads-club 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/tech-leads-club/agent-skills/tree/main/packages/skills-catalog/skills/(tooling)/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.

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

Generator Discovery Flow

Step 1: List 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, @nx/js:library)
  • Local workspace generators (defined in the repo's own plugins)

Step 2: Match Generator to User Request

Based on the user's request, identify which generator(s) could fulfill their needs. Consider:

  • What artifact type they want to create (library, application, etc.)
  • Which framework or technology stack is relevant
  • Whether they mentioned specific generator names

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 and conventions.

It's possible that the user request is something that no Nx generator exists for whatsoever. In this case, you can stop using this skill and try to help the user another way. HOWEVER, the burden of proof for this is high. Before aborting, carefully consider each and every generator that's available. Look into details for any that could be related in any way before making this decision.

Pre-Execution Checklist

Before running any generator, complete these steps:

1. Fetch Generator Schema

Use the --help flag to understand all available options:

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

Pay attention to:

  • Required options that must be provided
  • Optional options that may be relevant to the user's request
  • Default values that might need to be overridden

2. Read Generator Source Code

Understanding what the generator actually does helps you:

  • Know what files will be created/modified
  • Understand any side effects (updating configs, installing deps, etc.)
  • Identify options that might not be obvious from the schema

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: They are typically in tools/generators/ or a local plugin directory. You can search the repo for the generator name to find it.

2.5 Reevaluate if the generator is right

Once you have built up an understanding of what the selected generator does, reconsider: Is this the right generator to service the user request? If not, it's okay to go back to the Generator Discovery Flow and select a different generator before proceeding. If you do, make sure to go through the entire pre-execution checklist once more.

3. Understand Repo Context

Before generating, examine the target area of the codebase:

  • Look at similar existing artifacts (other libraries, applications, etc.)
  • Identify patterns and conventions used in the repo
  • Note naming conventions, file structures, and configuration patterns
  • Try to match these patterns when configuring the generator

For example, if similar libraries are using a specific test runner, build tool or linter, try to match that if possible. If projects or other artifacts are organized with a specific naming convention, try to match it.

4. Validate Required Options

Ensure all required options have values:

  • Map the user's request to generator options
  • Infer values from context where possible
  • Ask the user for any critical missing information

Execution

Keep in mind that you might have to prefix things with npx/pnpx/yarn if the user doesn't have nx installed globally. Many generators will behave differently based on where they are executed. For example, first-party nx library generators use the cwd to determine the directory that the library should be placed in. This is highly important.

Consider Dry-Run (Optional)

Running with --dry-run first is strongly encouraged but not mandatory. Use your judgment:

  • For complex generators or unfamiliar territory: do a dry-run first
  • For simple, well-understood generators: may proceed directly
  • Dry-run shows file names and created/deleted/modified markers, but not content
  • There are cases where a generator does not support dry-run (for example if it had to install an npm package) - in that case --dry-run might fail. Don't be discouraged but simply move on to running the generator for real and iterating from there.

Running the Generator

Execute the generator with:

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

CRITICAL: Always include --no-interactive to prevent prompts that would hang the execution.

Example:

bash
nx generate @nx/react:library --name=my-utils --no-interactive

Handling Generator Failures

If the generator fails:

  1. Diagnose the error - Read the error message carefully
  2. Identify the cause - Missing options, invalid values, conflicts, etc.
  3. Attempt automatic fix - Adjust options or resolve conflicts
  4. Retry - Run the generator again with corrected options

Common failure reasons:

  • Missing required options
  • Invalid option values
  • Conflicting with existing files
  • Missing dependencies
  • Generator doesn't support certain flag combinations

Post-Generation

1. Modify Generated Code (If Needed)

Generators provide a starting point, but the output may need adjustment to match the user's specific requirements:

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

2. Format Code

Run formatting on all generated/modified files:

bash
nx format --fix

Languages other than javascript/typescript might need other formatting invocations too.

3. Run Verification

Verify that the generated code works correctly. What this looks like will vary depending on the type of generator and the targets available. If the generator created a new project, run its targets directly Use your best judgement to determine what needs to be verified.

Example:

bash
nx lint <new-project>
nx test <new-project>
nx build <new-project>

4. Handle Verification Failures

When verification fails:

If scope is manageable (a few lint errors, minor type issues):

  • Fix the issues
  • Re-run verification to confirm

If issues are extensive (many errors, complex problems):

  • Attempt simple, obvious fixes first
  • If still failing, escalate to the user with:
    • Description of what was generated
    • What verification is failing
    • What you've attempted to fix
    • Remaining issues that need user input

Error Handling

Generator Failures

  • Check the error message for specific causes
  • Verify all required options are provided
  • Check for conflicts with existing files
  • Ensure the generator name and options are correct

Missing Options

  • Consult the generator schema for required fields
  • Infer values from context when reasonable
  • Ask the user for values that cannot be inferred

Key Principles

  1. Local generators first - Always prefer workspace/local generators over external plugin generators when both could work

  2. Understand before running - Read both the schema AND the source code to fully understand what will happen

  3. No prompts - Always use --no-interactive to prevent hanging

  4. Generators are starting points - Modify the output as needed to fully satisfy the user's requirements

  5. Verify changes work - Don't just generate; ensure the code builds, lints, and tests pass

  6. Be proactive about fixes - Don't just report errors; attempt to resolve them automatically when possible

  7. Match repo patterns - Study existing similar code in the repo and match its conventions

Frequently asked questions

What does the Nx Generate AI skill do?

Generate code using Nx generators — scaffold projects, libraries, features, or run workspace-specific generators with proper discovery, validation, and verification. Use when user says "create a new library", "scaffold a component", "generate code with Nx", "run a generator", "nx generate", or any code scaffolding task in a monorepo. Prefers local workspace-plugin generators over external plugins. Do NOT use for running build/test/lint tasks (use nx-run-tasks) or workspace configuration (use nx-workspace).

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/tech-leads-club/agent-skills/tree/main/packages/skills-catalog/skills/(tooling)/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?

It is published on GitHub by tech-leads-club. Check the repository for licensing terms. 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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