Skip to main content
ClaudeWave
Skill4.6k repo starsupdated yesterday

nx-generate

nx-generate executes Nx generators to scaffold projects, libraries, features, and other code artifacts within monorepos. This skill discovers available generators from plugins and workspace-specific sources, validates options against generator schemas, and runs the appropriate generator command. Use it when users request code scaffolding tasks such as creating new libraries, applications, or features, or when they explicitly mention running Nx generators. The skill prioritizes local workspace generators over external plugins to maintain consistency with repository conventions.

Install in Claude Code
Copy
git clone --depth 1 https://github.com/tech-leads-club/agent-skills /tmp/nx-generate && cp -r /tmp/nx-generate/packages/skills-catalog/skills/(tooling)/nx-generate ~/.claude/skills/nx-generate
Then start a new Claude Code session; the skill loads automatically.

SKILL.md

# 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
component-common-domain-detectionSkill

Finds duplicate business logic spread across multiple components and suggests consolidation. Use when asking "where is this logic duplicated?", "find common code between services", "what can be consolidated?", "detect shared domain logic", or analyzing component overlap before refactoring. Do NOT use for code-level duplication detection (use linters) or dependency analysis (use coupling-analysis).

component-flattening-analysisSkill

Detects misplaced classes and fixes component hierarchy problems — finds code that should belong inside a component but sits at the root level. Use when asking "clean up component structure", "find orphaned classes", "fix module hierarchy", "flatten nested components", or analyzing why namespaces have misplaced code. Do NOT use for dependency analysis (use coupling-analysis) or domain grouping (use domain-identification-grouping).

component-identification-sizingSkill

Maps architectural components in a codebase and measures their size to identify what should be extracted first. Use when asking "how big is each module?", "what components do I have?", "which service is too large?", "analyze codebase structure", "size my monolith", or planning where to start decomposing. Do NOT use for runtime performance sizing or infrastructure capacity planning.

coupling-analysisSkill

Analyzes coupling between modules using the three-dimensional model (strength, distance, volatility) from "Balancing Coupling in Software Design". Use when asking "are these modules too coupled?", "show me dependencies", "analyze integration quality", "which modules should I decouple?", "coupling report", or evaluating architectural health. Do NOT use for domain boundary analysis (use domain-analysis) or component sizing (use component-identification-sizing).

decomposition-planning-roadmapSkill

Creates step-by-step decomposition plans and migration roadmaps for breaking apart monolithic applications. Use when asking "what order should I extract services?", "plan my migration", "create a decomposition roadmap", "prioritize what to split", "monolith to microservices strategy", or tracking decomposition progress. Do NOT use for domain analysis (use domain-analysis) or component sizing (use component-identification-sizing).

domain-analysisSkill

Maps business domains and suggests service boundaries in any codebase using DDD Strategic Design. Use when asking "what are the domains in this codebase?", "where should I draw service boundaries?", "identify bounded contexts", "classify subdomains", "DDD analysis", or analyzing domain cohesion. Do NOT use for grouping existing components into domains (use domain-identification-grouping) or dependency analysis (use coupling-analysis).

domain-identification-groupingSkill

Groups existing components into logical business domains to plan service-based architecture. Use when asking "which components belong together?", "group these into services", "organize by domain", "component-to-domain mapping", or planning service extraction from an existing codebase. Do NOT use for identifying new domains from scratch (use domain-analysis) or analyzing coupling (use coupling-analysis).

frontend-blueprintSkill

AI frontend specialist and design consultant that guides users through a structured discovery process before generating any code. Collects visual references, design tokens, typography, icons, layout preferences, and brand guidelines to ensure the final output matches the user's vision with high fidelity. Use when the user asks to build, design, create, or improve any frontend interface — websites, landing pages, dashboards, components, apps, emails, forms, modals, or any UI element. Also triggers on "build me a UI", "design a page", "create a component", "improve this layout", "make this look better", "frontend", "interface", "redesign", or when the user provides mockups, screenshots, or design references. Do NOT use for backend logic, API design, database schemas, or non-visual code tasks.