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Nestjs Expert

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Jeffallan
nestjs-expert

Creates and configures NestJS modules, controllers, services, DTOs, guards, and interceptors for enterprise-grade TypeScript backend applications. Use when building NestJS REST APIs or GraphQL services, implementing dependency injection, scaffolding modular architecture, adding JWT/Passport authentication, integrating TypeORM or Prisma, or working with .module.ts, .controller.ts, and .service.ts files. Invoke for guards, interceptors, pipes, validation, Swagger documentation, and unit/E2E testing in NestJS projects.

Overview

PublisherJeffallan
Repositoryclaude-skills
Skill namenestjs-expert
Stars
11.5K
Forks
1.1K
Bundled files
6
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.

  • 6 bundled files

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

  • Open source

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

Installation

Install the Nestjs Expert 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/Jeffallan/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/skills/nestjs-expert .claude/skills/nestjs-expert
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nestjs Expert 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 Nestjs Expert 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 Nestjs Expert 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.

NestJS Expert

Senior NestJS specialist with deep expertise in enterprise-grade, scalable TypeScript backend applications.

Core Workflow

  1. Analyze requirements — Identify modules, endpoints, entities, and relationships
  2. Design structure — Plan module organization and inter-module dependencies
  3. Implement — Create modules, services, and controllers with proper DI wiring
  4. Secure — Add guards, validation pipes, and authentication
  5. Verify — Run npm run lint, npm run test, and confirm DI graph with nest info
  6. Test — Write unit tests for services and E2E tests for controllers

Reference Guide

Load detailed guidance based on context:

TopicReferenceLoad When
Controllersreferences/controllers-routing.mdCreating controllers, routing, Swagger docs
Servicesreferences/services-di.mdServices, dependency injection, providers
DTOsreferences/dtos-validation.mdValidation, class-validator, DTOs
Authenticationreferences/authentication.mdJWT, Passport, guards, authorization
Testingreferences/testing-patterns.mdUnit tests, E2E tests, mocking
Express Migrationreferences/migration-from-express.mdMigrating from Express.js to NestJS

Code Examples

Controller with DTO Validation and Swagger

typescript
// create-user.dto.ts
import { IsEmail, IsString, MinLength } from 'class-validator';
import { ApiProperty } from '@nestjs/swagger';

export class CreateUserDto {
  @ApiProperty({ example: 'user@example.com' })
  @IsEmail()
  email: string;

  @ApiProperty({ example: 'strongPassword123', minLength: 8 })
  @IsString()
  @MinLength(8)
  password: string;
}

// users.controller.ts
import { Body, Controller, Post, HttpCode, HttpStatus } from '@nestjs/common';
import { ApiCreatedResponse, ApiTags } from '@nestjs/swagger';
import { UsersService } from './users.service';
import { CreateUserDto } from './dto/create-user.dto';

@ApiTags('users')
@Controller('users')
export class UsersController {
  constructor(private readonly usersService: UsersService) {}

  @Post()
  @HttpCode(HttpStatus.CREATED)
  @ApiCreatedResponse({ description: 'User created successfully.' })
  create(@Body() createUserDto: CreateUserDto) {
    return this.usersService.create(createUserDto);
  }
}

Service with Dependency Injection and Error Handling

typescript
// users.service.ts
import { Injectable, ConflictException, NotFoundException } from '@nestjs/common';
import { InjectRepository } from '@nestjs/typeorm';
import { Repository } from 'typeorm';
import { User } from './entities/user.entity';
import { CreateUserDto } from './dto/create-user.dto';

@Injectable()
export class UsersService {
  constructor(
    @InjectRepository(User)
    private readonly usersRepository: Repository<User>,
  ) {}

  async create(createUserDto: CreateUserDto): Promise<User> {
    const existing = await this.usersRepository.findOneBy({ email: createUserDto.email });
    if (existing) {
      throw new ConflictException('Email already registered');
    }
    const user = this.usersRepository.create(createUserDto);
    return this.usersRepository.save(user);
  }

  async findOne(id: number): Promise<User> {
    const user = await this.usersRepository.findOneBy({ id });
    if (!user) {
      throw new NotFoundException(`User #${id} not found`);
    }
    return user;
  }
}

Module Definition

typescript
// users.module.ts
import { Module } from '@nestjs/common';
import { TypeOrmModule } from '@nestjs/typeorm';
import { UsersController } from './users.controller';
import { UsersService } from './users.service';
import { User } from './entities/user.entity';

@Module({
  imports: [TypeOrmModule.forFeature([User])],
  controllers: [UsersController],
  providers: [UsersService],
  exports: [UsersService], // export only when other modules need this service
})
export class UsersModule {}

Unit Test for Service

typescript
// users.service.spec.ts
import { Test, TestingModule } from '@nestjs/testing';
import { getRepositoryToken } from '@nestjs/typeorm';
import { ConflictException } from '@nestjs/common';
import { UsersService } from './users.service';
import { User } from './entities/user.entity';

const mockRepo = {
  findOneBy: jest.fn(),
  create: jest.fn(),
  save: jest.fn(),
};

describe('UsersService', () => {
  let service: UsersService;

  beforeEach(async () => {
    const module: TestingModule = await Test.createTestingModule({
      providers: [
        UsersService,
        { provide: getRepositoryToken(User), useValue: mockRepo },
      ],
    }).compile();
    service = module.get<UsersService>(UsersService);
    jest.clearAllMocks();
  });

  it('throws ConflictException when email already exists', async () => {
    mockRepo.findOneBy.mockResolvedValue({ id: 1, email: 'user@example.com' });
    await expect(
      service.create({ email: 'user@example.com', password: 'pass1234' }),
    ).rejects.toThrow(ConflictException);
  });
});

Constraints

MUST DO

  • Use @Injectable() and constructor injection for all services — never instantiate services with new
  • Validate all inputs with class-validator decorators on DTOs and enable ValidationPipe globally
  • Use DTOs for all request/response bodies; never pass raw req.body to services
  • Throw typed HTTP exceptions (NotFoundException, ConflictException, etc.) in services
  • Document all endpoints with @ApiTags, @ApiOperation, and response decorators
  • Write unit tests for every service method using Test.createTestingModule
  • Store all config values via ConfigModule and process.env; never hardcode them

MUST NOT DO

  • Expose passwords, secrets, or internal stack traces in responses
  • Accept unvalidated user input — always apply ValidationPipe
  • Use any type unless absolutely necessary and documented
  • Create circular dependencies between modules — use forwardRef() only as a last resort
  • Hardcode hostnames, ports, or credentials in source files
  • Skip error handling in service methods

Output Templates

When implementing a NestJS feature, provide in this order:

  1. Module definition (.module.ts)
  2. Controller with Swagger decorators (.controller.ts)
  3. Service with typed error handling (.service.ts)
  4. DTOs with class-validator decorators (dto/*.dto.ts)
  5. Unit tests for service methods (*.service.spec.ts)

Knowledge Reference

NestJS, TypeScript, TypeORM, Prisma, Passport, JWT, class-validator, class-transformer, Swagger/OpenAPI, Jest, Supertest, Guards, Interceptors, Pipes, Filters

Documentation

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 Nestjs Expert AI skill do?

Creates and configures NestJS modules, controllers, services, DTOs, guards, and interceptors for enterprise-grade TypeScript backend applications. Use when building NestJS REST APIs or GraphQL services, implementing dependency injection, scaffolding modular architecture, adding JWT/Passport authentication, integrating TypeORM or Prisma, or working with .module.ts, .controller.ts, and .service.ts files. Invoke for guards, interceptors, pipes, validation, Swagger documentation, and unit/E2E testing in NestJS projects.

Why use Nestjs Expert on TypingMind?

Because you install it once and use it with any model. Nestjs Expert 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 Nestjs Expert in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Jeffallan/claude-skills/tree/main/skills/nestjs-expert. 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 Nestjs Expert?

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 Nestjs Expert?

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

Is the Nestjs Expert AI skill free?

Yes. It is published on GitHub by Jeffallan 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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