Javascript

Component Testing in Svelte

Published Oct 25, 2021

Updated Feb 13, 2023

4 min read

This article was written over 18 months ago and may contain information that is out of date. Some content may be relevant but please refer to the relevant official documentation or available resources for the latest information.

Component Testing in Svelte

Testing helps us trust our application, and it's a safety net for future changes. In this tutorial, we will set up our Svelte project to run tests for our components.

Starting a new project

Let's start by creating a new project:

pnpm dlx create-vite
// Project name: › testing-svelte
// Select a framework: › svelte
// Select a variant: › svelte-ts

cd testing-svelte
pnpm install

There are other ways of creating a Svelte project, but I prefer using Vite. One of the reasons that I prefer using Vite is that SvelteKit will use it as well. I'm also a big fan of pnpm, but you can use your preferred package manager. Make sure you follow Vite's docs on starting a new project using npm or yarn.

Installing required dependencies

Jest: I'll be using this framework for testing. It's the one that I know best, and feel more comfortable with. Because I'm using TypeScript, I need to install its type definitions too.
ts-jest: A transformer for handling TypeScript files.
svelte-jester: precompiles Svelte components before tests.
Testing Library: Doesn't matter what framework I'm using, I will look for an implementation of this popular library.

pnpm install --save-dev jest @types/jest @testing-library/svelte svelte-jester ts-jest

Configuring tests

Now that our dependencies are installed, we need to configure jest to prepare the tests and run them.

A few steps are required:

Convert *.ts files
Complile *.svelte files
Run the tests

Create a configuration file at the root of the project:

// jest.config.js
export default {
  transform: {
    '^.+\\.ts$': 'ts-jest',
    '^.+\\.svelte$': [
      'svelte-jester',
      {
        preprocess: true,
      },
    ],
  },
  moduleFileExtensions: ['js', 'ts', 'svelte'],
};

Jest will now use ts-jest for compiling *.ts files, and svelte-jester for *.svelte files.

Creating a new test

Let's test the Counter component created when we started the project, but first, I'll check what our component does.

<script lang="ts">
  let count: number = 0;
  const increment = () => {
    count += 1;
  };
</script>

<button on:click={increment}>
  Clicks: {count}
</button>

<style>
  button {
    font-family: inherit;
    font-size: inherit;
    padding: 1em 2em;
    color: #ff3e00;
    background-color: rgba(255, 62, 0, 0.1);
    border-radius: 2em;
    border: 2px solid rgba(255, 62, 0, 0);
    outline: none;
    width: 200px;
    font-variant-numeric: tabular-nums;
    cursor: pointer;
  }

  button:focus {
    border: 2px solid #ff3e00;
  }

  button:active {
    background-color: rgba(255, 62, 0, 0.2);
  }
</style>

This is a very small component where a button when clicked, updates a count, and that count is reflected in the button text. So, that's exactly what we'll be testing.

I'll create a new file ./lib/__tests__/Counter.spec.ts

/**
 * @jest-environment jsdom
 */

import { render, fireEvent } from '@testing-library/svelte';
import Counter from '../Counter.svelte';

describe('Counter', () => {
  it('it changes count when button is clicked', async () => {
    const { getByText } = render(Counter);
    const button = getByText(/Clicks:/);
    expect(button.innerHTML).toBe('Clicks: 0');
    await fireEvent.click(button);
    expect(button.innerHTML).toBe('Clicks: 1');
  });
});

We are using render and fireEvent from testing-library. Be mindful that fireEvent returns a Promise and we need to await for it to be fulfilled. I'm using the getByText query, to get the button being clicked. The comment at the top, informs jest that we need to use jsdom as the environment. This will make things like document available, otherwise, render will not be able to mount the component. This can be set up globally in the configuration file.

What if we wanted, to test the increment method in our component? If it's not an exported function, I'd suggest testing it through the rendered component itself. Otherwise, the best option is to extract that function to another file, and import it into the component.

Let's see how that works.

// lib/increment.ts
export function increment (val: number) {
    val += 1;
    return val
  };

<!-- lib/Counter.svelte -->
<script lang="ts">
  import { increment } from './increment';
  let count: number = 0;
</script>

<button on:click={() => (count = increment(count))}>
  Clicks: {count}
</button>
<!-- ... -->

Our previous tests will still work, and we can add a test for our function.

// lib/__tests__/increment.spec.ts

import { increment } from '../increment';

describe('increment', () => {
  it('it returns value+1 to given value when called', async () => {
    expect(increment(0)).toBe(1);
    expect(increment(-1)).toBe(0);
    expect(increment(1.2)).toBe(2.2);
  });
});

In this test, there's no need to use jsdom as the test environment. We are just testing the function.

If our method was exported, we can then test it by accessing it directly.

<!-- lib/Counter.svelte -->
<script lang="ts">
  let count: number = 0;
  export const increment = () => {
    count += 1;
  };
</script>

<button on:click={increment}>
  Clicks: {count}
</button>
<!-- ... -->

// lib/__tests__/Counter.spec.ts

describe('Counter Component', () => {
 // ... other tests

  describe('increment', () => {
    it('it exports a method', async () => {
      const { component } = render(Counter);
      expect(component.increment).toBeDefined();
    });

    it('it exports a method', async () => {
      const { getByText, component } = render(Counter);
      const button = getByText(/Clicks:/);
      expect(button.innerHTML).toBe('Clicks: 0');
      await component.increment()
      expect(button.innerHTML).toBe('Clicks: 1');
    });
  });
});

When the method is exported, you can access it directly from the returned component property of the render function.

NOTE: I don't recommend exporting methods from the component for simplicity if they were not meant to be exported. This will make them available from the outside, and callable from other components.

Events

If your component dispatches an event, you can test it using the component property returned by render.

To dispatch an event, we need to import and call createEventDispatcher, and then call the returning funtion, giving it an event name and an optional value.

<!-- lib/Counter.svelte -->
<script lang="ts">
  import { createEventDispatcher } from 'svelte';
  const dispatch = createEventDispatcher();

  let count: number = 0;
  export const increment = () => {
    count += 1;
    dispatch('countChanged', count);
  };
</script>

<button on:click={increment}>
  Clicks: {count}
</button>
<!-- ... -->

// lib/__tests__/Counter.spec.ts
// ...

  it('it emits an event', async () => {
    const { getByText, component } = render(Counter);
    const button = getByText(/Clicks:/);
    let mockEvent = jest.fn();
    component.$on('countChanged', function (event) {
      mockEvent(event.detail);
    });
    await fireEvent.click(button);

    // Some examples on what to test
    expect(mockEvent).toHaveBeenCalled(); // to check if it's been called
    expect(mockEvent).toHaveBeenCalledTimes(1); // to check how any times it's been called
    expect(mockEvent).toHaveBeenLastCalledWith(1); // to check the content of the event
    await fireEvent.click(button);
    expect(mockEvent).toHaveBeenCalledTimes(2);
    expect(mockEvent).toHaveBeenLastCalledWith(2);
  });

//...

For this example, I updated the component to emit an event: countChanged. Every time the button is clicked, the event will emit the new count. In the test, I'm using getByText to select the button to click, and component.

Then, I'm using component.$on(eventName), and mocking the callback function to test the emitted value (event.detail).

Props

You can set initial props values, and modifying them using the client-side component API.

Let's update our component to receive the initial count value.

<!-- lib/Counter.svelte -->
<script lang="ts">
// ...
  export let count: number = 0;
// ...
</script>

<!-- ... -->

Converting count to an input value requires exporting the variable declaration.

Then we can test:

default values
initial values
updating values

// lib/__tests__/Counter.ts
// ...
describe('count', () => {
    it('defaults to 0', async () => {
      const { getByText } = render(Counter);
      const button = getByText(/Clicks:/);
      expect(button.innerHTML).toBe('Clicks: 0');
    });

    it('can have an initial value', async () => {
      const { getByText } = render(Counter, {props: {count: 33}});
      const button = getByText(/Clicks:/);
      expect(button.innerHTML).toBe('Clicks: 33');
    });

    it('can be updated', async () => {
      const { getByText, component } = render(Counter);
      const button = getByText(/Clicks:/);
      expect(button.innerHTML).toBe('Clicks: 0');
      await component.$set({count: 41})
      expect(button.innerHTML).toBe('Clicks: 41');
    });
});
// ...

We are using the second argument of the render method to pass initial values to count, and we are testing it through the rendered button

To update the value, we are calling the $set method on component, which will update the rendered value on the next tick. That's why we need to await it.

Wrapping up

Testing components using Jest and Testing Library can help you avoid errors when developing, and also can make you more confident when applying changes to an existing codebase. I hope this blog post is a step forward to better testing.

You can find these examples in this repo

This Dot is a consultancy dedicated to guiding companies through their modernization and digital transformation journeys. Specializing in replatforming, modernizing, and launching new initiatives, we stand out by taking true ownership of your engineering projects.

We love helping teams with projects that have missed their deadlines or helping keep your strategic digital initiatives on course. Check out our case studies and our clients that trust us with their engineering.

About the author(s)

Ignacio Falk
I love to learn new things and share what I know. Mentor, Writer, Speaker Member of the local community
@flakolefluk @flakolefluk

Introduction to Zod for Data Validation

As web developers, we're often working with data from external sources like APIs we don't control or user inputs submitted to our backends. We can't always rely on this data to take the form we expect, and we can encounter unexpected errors when it deviates from expectations. But with the Zod library, we can define what our data ought to look like and parse the incoming data against those defined schemas. This lets us work with that data confidently, or to quickly throw an error when it isn't correct. Why use Zod? TypeScript is great for letting us define the shape of our data in our code. It helps us write more correct code the first time around by warning us if we are doing something we shouldn't. But TypeScript can't do everything for us. For example, we can define a variable as a string or a number, but we can't say "a string that starts with user_id_ and is 20 characters long" or "an integer between 1 and 5". There are limits to how much TypeScript can narrow down our data for us. Also, TypeScript is a tool for us developers. When we compile our code, our types are not available to the vanilla JavaScript. JavaScript can't validate that the data we actually use in our code matches what we thought we'd get when we wrote our TypeScript types unless you're willing to manually write code to perform those checks. This is where we can reach for a tool like Zod. With Zod, we can write data schemas. These schemas, in the simplest scenarios, look very much like TypeScript types. But we can do more with Zod than we can with TypeScript alone. Zod schemas let us create additional rules for data parsing and validation. A 20-character string that starts with user_id_? It's z.string().startsWith('user_id_').length(20). An integer between 1 and 5 inclusive? It's z.number().int().gte(1).lte(5). Zod's primitives give us many extra functions to be more specific about *exactly* what data we expect. Unlike TypeScript, Zod schemas aren't removed on compilation to JavaScript—we still have access to them! If our app receives some data, we can verify that it matches the expected shape by passing it to your Zod schema's parse function. You'll either get back your data in exactly the shape you defined in your schema, or Zod will give you an error to tell you what was wrong. Zod schemas aren't a replacement for TypeScript; rather, they are an excellent complement. Once we've defined our Zod schema, it's simple to derive a TypeScript type from it and to use that type as we normally would. But when we really need to be sure our data conforms to the schema, we can always parse the data with our schema for that extra confidence. Defining Data Schemas Zod schemas are the variables that define our expectations for the shape of our data, validate those expectations, and transform the data if necessary to match our desired shape. It's easy to start with simple schemas, and to add complexity as required. Zod provides different functions that represent data structures and related validation options, which can be combined to create larger schemas. In many cases, you'll probably be building a schema for a data object with properties of some primitive type. For example, here's a schema that would validate a JavaScript object representing an order for a pizza: ` Zod provides a number of primitives for defining schemas that line up with JavaScript primitives: string, number, bigint, boolean, date, symbol, undefined, and null. It also includes primitives void, any, unknown, and never for additional typing information. In addition to basic primitives, Zod can define object, array, and other native data structure schemas, as well as schemas for data structures not natively part of JavaScript like tuple and enum. The documentation contains considerable detail on the available data structures and how to use them. Parsing and Validating Data with Schemas With Zod schemas, you're not only telling your program what data should look like; you're also creating the tools to easily verify that the incoming data matches the schema definitions. This is where Zod really shines, as it greatly simplifies the process of validating data like user inputs or third party API responses. Let's say you're writing a website form to register new users. At a minimum, you'll need to make sure the new user's email address is a valid email address. For a password, we'll ask for something at least 8 characters long and including one letter, one number, and one special character. (Yes, this is not really the best way to write strong passwords; but for the sake of showing off how Zod works, we're going with it.) We'll also ask the user to confirm their password by typing it twice. First, let's create a Zod schema to model these inputs: ` So far, this schema is pretty basic. It's only making sure that whatever the user types as an email is an email, and it's checking that the password is at least 8 characters long. But it is *not* checking if password and confirmPassword match, nor checking for the complexity requirements. Let's enhance our schema to fix that! ` By adding refine with a custom validation function, we have been able to verify that the passwords match. If they don't, parsing will give us an error to let us know that the data was invalid. We can also chain refine functions to add checks for our password complexity rules: ` Here we've chained multiple refine functions. You could alternatively use superRefine, which gives you even more fine grained control. Now that we've built out our schema and added refinements for extra validation, we can parse some user inputs. Let's see two test cases: one that's bound to fail, and one that will succeed. ` There are two main ways we can use our schema to validate our data: parse and safeParse. The main difference is that parse will throw an error if validation fails, while safeParse will return an object with a success property of either true or false, and either a data property with your parsed data or an error property with the details of a ZodError explaining why the parsing failed. In the case of our example data, userInput2 will parse just fine and return the data for you to use. But userInput1 will create a ZodError listing all of the ways it has failed validation. ` ` We can use these error messages to communicate to the user how they need to fix their form inputs if validation fails. Each error in the list describes the validation failure and gives us a human readable message to go with it. You'll notice that the validation errors for checking for a valid email and for checking password length have a lot of details, but we've got three items at the end of the error list that don't really tell us anything useful: just a custom error of Invalid input. The first is from our refine checking if the passwords match, and the second two are from our refine functions checking for password complexity (numbers and special characters). Let's modify our refine functions so that these errors are useful! We'll add our own error parameters to customize the message we get back and the path to the data that failed validation. ` Now, our error messages from failures in refine are informative! You can figure out which form fields aren't validating from the path, and then display the messages next to form fields to let the user know how to remedy the error. ` By giving our refine checks a custom path and message, we can make better use of the returned errors. In this case, we can highlight specific problem form fields for the user and give them the message about what is wrong. Integrating with TypeScript Integrating Zod with TypeScript is very easy. Using z.infer<typeof YourSchema> will allow you to avoid writing extra TypeScript types that merely reflect the intent of your Zod schemas. You can create a type from any Zod schema like so: ` Using a TypeScript type derived from a Zod schema does *not* give you any extra level of data validation at the type level beyond what TypeScript is capable of. If you create a type from z.string.min(3).max(20), the TypeScript type will still just be string. And when compiled to JavaScript, even that will be gone! That's why you still need to use parse/safeParse on incoming data to validate it before proceeding as if it really does match your requirements. A common pattern with inferring types from Zod schemas is to use the same name for both. Because the schema is a variable, there's no name conflict if the type uses the same name. However, I find that this can lead to confusing situations when trying to import one or the other—my personal preference is to name the Zod schema with Schema at the end to make it clear which is which. Conclusion Zod is an excellent tool for easily and confidently asserting that the data you're working with is exactly the sort of data you were expecting. It gives us the ability to assert at runtime that we've got what we wanted, and allows us to then craft strategies to handle what happens if that data is wrong. Combined with the ability to infer TypeScript types from Zod schemas, it lets us write and run more reliable code with greater confidence....

Nov 15, 2024

7 mins

JavaScriptZod

The HTML Dialog Element: Enhancing Accessibility and Ease of Use

The HTML Dialog Element: Enhancing Accessibility and Ease of Use Dialogs are a common component added to applications, whether on the web or in native applications. Traditionally there has not been a standard way of implementing these on the web, resulting in many ad-hoc implementations that don’t act consistently across different web applications. Often, commonly expected features are missing from dialogs due to the complexity of implementing them. However, web browsers now offer a standard dialog element. Why use the dialog element? The native dialog element streamlines the implementation of dialogs, modals, and other kinds of non-modal dialogs. It does this by implementing many of the features needed by dialogs for you that are already baked into the browser. This is helpful as it reduces the burden on the developer when making their applications accessible by ensuring that user expectations concerning interaction are met, and it can also potentially simplify the implementation of dialogs in general. Basic usage Adding a dialog using the new tag can be achieved with just a few lines of code. ` However, adding the dialog alone won’t do anything to the page. It will show up only once you call the .showModal() method against it. ` Then if you want to close it you can call the .close() method on the dialog, or press the escape key to close it, just like most other modals work. Also, note how a backdrop appears that darkens the rest of the page and prevents you from interacting with it. Neat! Accessibility and focus management Correctly handling focus is important when making your web applications accessible to all users. Typically you have to move the current focus to the active dialog when showing them, but with the dialog element that’s done for you. By default, the focus will be set on the first focusable element in the dialog. You can optionally change which element receives focus first by setting the autofocus attribute on the element you want the focus to start on, as seen in the previous example where that attribute was added to the close element. Using the .showModal() method to open the dialog also implicitly adds the dialog ARIA role to the dialog element. This helps screen readers understand that a modal has appeared and the screen so it can act accordingly. Adding forms to dialogs Forms can also be added to dialogs, and there’s even a special method value for them. If you add a element with the method set to dialog then the form will have some different behaviors that differ from the standard get and post form methods. First off, no external HTTP request will be made with this new method. What will happen instead is that when the form gets submitted, the returnValue property on the form element will be set to the value of the submit button in the form. So given this example form: ` The form element with the example-form id will have its returnValue set to Submit. In addition to that, the dialog will close immediately after the submit event is done being handled, though not before automatic form validation is done. If this fails then the invalid event will be emitted. You may have already noticed one caveat to all of this. You might not want the form to close automatically when the submit handler is done running. If you perform an asynchronous request with an API or server you may want to wait for a response and show any errors that occur before dismissing the dialog. In this case, you can call event.preventDefault() in the submit event listener like so: ` Once your desired response comes back from the server, you can close it manually by using the .close() method on the dialog. Enhancing the backdrop The backdrop behind the dialog is a mostly translucent gray background by default. However, that backdrop is fully customizable using the ::backdrop pseudo-element. With it, you can set a background-color to any value you want, including gradients, images, etc. You may also want to make clicking the backdrop dismiss the modal, as this is a commonly implemented feature of them. By default, the <dialog> element doesn’t do this for us. There are a couple of changes that we can make to the dialog to get this working. First, an event listener is needed so that we know when the user clicks away from the dialog. ` Alone this event listener looks strange. It appears to dismiss the dialog whenever the dialog is clicked, not the backdrop. That’s the opposite of what we want to do. Unfortunately, you cannot listen for a click event on the backdrop as it is considered to be part of the dialog itself. Adding this event listener by itself will effectively make clicking anywhere on the page dismiss the dialog. To correct for this we need to wrap the contents of the dialog content with another element that will effectively mask the dialog and receive the click instead. A simple element can do! ` Even this isn’t perfect though as the contents of the div may have elements with margins in them that will push the div down, resulting in clicks close to the edges of the dialog to dismiss it. This can be resolved by adding a couple of styles the the wrapping div that will make the margin stay contained within the wrapper element. The dialog element itself also has some default padding that will exacerbate this issue. ` The wrapping div can be made into an inline-block element to contain the margin, and by moving the padding from the parent dialog to the wrapper, clicks made in the padded portions of the dialog will now interact with the wrapper element instead ensuring it won’t be dismissed. Conclusion Using the dialog element offers significant advantages for creating dialogs and modals by simplifying implementation with reasonable default behavior, enhancing accessibility for users that need assistive technologies such as screen readers by using automatic ARIA role assignment, tailored support for form elements, and flexible styling options....

Oct 25, 2024

4 mins

HTMLJavaScript

What is Cypress Studio?

Introduction Cypress Studio has been around for some time. It was introduced in Cypress v6.3.0, removed in v10, and reintroduced in v10.7.0. This blog post will dive into its current state and how to use it today. What is Cypress Studio Cypress Studio is a tool built on top of Cypress. It offers an interface for creating tests as if you were using your site without requiring code to be written. It adds functionality to query elements and to add assertions from the interface. It's important to note that Cypress Studio is still under an experimental flag and lacks support for Component Testing (another feature of Cypress). Creating an app to test We first need a working app because the tool we will use is meant for end-to-end tests. I'll pick Svelte and Vite for this demo, but you can choose any framework. Cypress is agnostic in that matter. ` Make sure to select the Svelte and Typescript options to follow along. Now open your project location, install dependencies, and start your app to ensure everything works correctly. ` Installing and setting up Cypress Studio Now it's time to add Cypress to our dev dependencies. ` With our dependency installed, let's launch it so we can start configuring it. But first let’s add an entry under scripts in our package.json file. ` ` A new window will open that will guide us through the initial setup. Select the "E2E Testing" option to create the required configuration files. Then, you can close the window. A new folder called cypress and a configuration file cypress.config.ts are created at the root of our project. To enable Cypress Studio, we must open the newly created configuration file and add the experimentalStudio property. ` For this project, we need to ensure that TypeScript is configured properly for Cypress when it runs, as it conflicts with the one in our root folder. We will extend the original typescript configuration and override some properties. Create a new tsconfig.json file inside the' cypress' folder. ` You can find more information on setting up TypeScript with Cypress in their documentation Creating tests Now that our setup is complete let's write our first test. We will create a folder called e2e and a new test file. ` Open the new file and create an outline of your tests. Don't add anything to them. I initialized my file with a few tests. ` There is an option to create spec files from the interface, but it will contain some initial content that you most likely remove. There’s third option to scaffold multiple specs files, these can serve as a learning resource for writing tests as it contains many different scenarios tested. Make sure your app is running and, in another terminal, start cypress. ` Select E2E testing and Chrome. You should now see a list with all your test files. Click on home.cy.ts. Your tests will run and pass because we have not made any assertions. This looks the same as if we haven't enabled Studio, but there's a new detail added when you hover on any of the tests: a magic wand that you can click to start recording events and adding assertions directly in the UI. Let's start with our first tests and check what assertions we can make. The first step is to navigate to a page. This interaction will be recorded and added to the test. To make assertions, right-click on an element and select the ones you want to add. After we complete our test, let's review our generated code. Go to the test file and see the changes made to it. It should look something like this. ` Cypress Studio will attempt to pick the best selector for the element. In this case, it was able to choose button because it is unique to the page. If there were more buttons, the selector would've been different. You can interact with elements as if you were using the page regularly. Note that Studio supports a limited set of commands: check, click, select, type, and uncheck. Let's record our second test and verify that the button will increase its count when clicked. That was quick. Let's review our generated test. ` So far, our tests have been very accurate and didn't need any modifications. However, take Studio as a helper to write tests visually and their selector as suggestions. Let's take the bottom link with the text "SvelteKit" as an example. It will generate the assertion: ` This selector is accurate, but modifying the elements' structure or order would break the tests. We don’t want tests to break for reasons that do not affect our users. A change in order does not have the same impact for the user as changing the text of a button or changing the url of a link. These selectors should be as specific as possible, but rely as less as possible on implementation details. Selecting images by its alt attribute it’s a way to find a specific element and at the same time ensure that element is accessible. (Accessibility has a direct impact on users) As a counterpart, when clicking any of the logos above, we will get very interesting selectors (and assertions). ` With these assertions, we check that our images have an alt attribute set and are tied to a specified link. Conclusion As with any other automated tool, check the output and solve any issues that you may find. Results depend on the structure of the page and Cypress Studio's ability to choose an adequate selector. Overall, this tool can still help you write complex tests with plenty of interactions, and you can later modify any selectors. Besides the selector, I found it helpful in writing the assertions. If you’re new to Cypress or e2e testing, it can be of great help to go from an idea to a working test. Reviewing these tests can be a great way to learn how these tests are built....

May 3, 2024

5 mins

CypressTesting

Increasing development velocity with Cursor

If you’re a developer, you’ve probably heard of Cursor by now and have either tried it out or are just curious to learn more about it. Cursor is a fork of VSCode with a ton of powerful AI/LLM-powered features added on. For around $20/month, I think it’s the best value in the AI coding space. Tech giants like Shopify and smaller companies like This Dot Labs have purchased Cursor subscriptions for their developers with the goal of increased productivity. I have been using Cursor heavily for a few months now and am excited to share how it’s impacted me personally. In this post, we will cover some of the basic features, use cases, and I’ll share some tips and tricks I’ve learned along the way. If you love coding and building like me, I hope this post will help you unleash some of the superpowers Cursor’s AI coding features make possible. Let’s jump right in! Cursor 101 The core tools of the Cursor tool belt are Autocomplete, Ask, and Agent. Feature: Autocomplete The first thing that got me hooked was Autocomplete. It just worked so much better than the tools I had used previously, like GitHub Copilot. It was quicker and smarter, and I could immediately notice the amount of keystrokes that it was saving me. This feature is great because it doesn’t really require any work or skilled prompting from the user. There are a couple of tricks for getting a little bit more out of it that I will share later, but for now, just enjoy the ride! Feature: Ask If you’ve interacted with AI/LLMs before, like ChatGPT - this is what the Ask feature is. It’s just a chat feature you can easily provide context to from your code base and choose which Model to chat with. This feature is best suited for just asking more general questions that you might have queried Google or Stack Overflow for in the past. It’s also good for planning how to implement a feature you’re working on. After chatting or planning, you can switch directly to Agent mode to pick up and take action on something you were cooking up in Ask mode. Here’s an example of planning a simple tic-tac-toe game implementation using the Ask feature: Feature: Agent Agent mode lets the AI model take the wheel and write code, make edits, or take other similar actions on your code base. The goal is that you can write prompts and give instructions, and the Agent can generate the code and build features or even entire applications for you. With great power comes great responsibility. Agents are a feature where the more you put into them, the more you get out. The more skilled you become in using them by providing better prompts and including the right context, you will continue to get better results. The AI doesn’t always get it right, but the fact that the models and the users are both getting better is exciting. Throughout this post, I will share the best use cases, tips, and tricks I have found using Cursor Agent. Here’s an example using the Agent to execute the implementation details of the tic-tac-toe game we planned using Ask: Core Concept: Context After understanding the features and the basics of prompting, context is the most important thing for getting the best results out of Cursor. In Cursor and in general, whenever you’re prompting a chat or an agent, you want to make sure that it has all the relevant information that it needs to provide an answer or result. Cursor, by default, always has some context of your code. It indexes your code base and usually keeps the open buffer in the context window at the very least. At the top left of the Ask or Agent panel, there is an @ button, and next to that are badges for all the current items that have been explicitly added to the context for the current session. The @ button has a dropdown that allows you to add files, folders, web links, past chats, git commits, and more to the context. Before you prompt, always make sure you add the relevant content it needs as context so that it has everything it needs to provide the best response. Settings and Rules Cursor has its own settings page, which you can access through Cursor → Settings → Cursor Settings. This is where you log in to your account, manage various features, and enable or disable models. In the General section, there is an option for Privacy Mode. This is one setting in particular I recommend enabling. Aside from that, just explore around and see what’s available. Models The model you use is just as important as your prompt and the context that you provide. Models are the underlying AI/LLM used to process your input. The most well-known is GPT-4o, the default model for ChatGPT. There are a lot of different models available, and Cursor provides access to most of them out of the box. Model pricing A lot of the most common models, like GPT-4o or Sonnet 3.5/3.7, are included in your Cursor subscription. Some models like o1 and Sonnet 3.7 MAX are considered premium models, and you will be billed for usage for these. Be sure to pay attention to which models you are using so you don’t get any surprise bills. Choosing a Model Some models are better suited for certain tasks than others. You can configure which models are enabled in the Cursor Settings. If you are planning out a big feature or trying to solve some complex logic issue, you may want to use one of the thinking models, like o1, o3-mini, or Deep Seek R1. For most coding tasks and as a good default, I recommend using Sonnet 3.5 or 3.7. The great thing about Cursor is that you have the options available right in your editor. The most important piece of advice that I can give in this post is to keep trying things out and experimenting. Try out different models for different tasks, get a feel for it, and find what works for you. Use cases Agents and LLM models are still far from perfect. That being said, there are already a lot of tasks they are very good at. The more effective you are with these tools, the more you will be able to get done in a shorter amount of time. Generating test cases Have some code that you would like unit tested? Cursor is very good at generating test cases and assertions for your code. The fewer barriers there are to testing a piece of code, the better the result you will get. So, try your best to write code that is easily testable! If testing the code requires some mocks or other pieces to work, do your best to provide it the context and instructions it needs before writing the tests. Always review the test cases! There could be errors or test cases that don’t make sense. Most of the time, it will get you pretty close to where you want to be. Here’s an example of using the Agent mode to install packages for testing and generate unit tests for the tic-tac-toe game logic: Generating documentation This is another thing we know AI models are good at - summarizing large chunks of information. Make sure it has the context of whatever you want to document. This one, in particular, is really great because historically, keeping documentation up to date is a rare and challenging practice. Here’s an example of using the Agent mode to generate documentation for the tic-tac-toe game: Code review There are a lot of up-and-coming tools outside of Cursor that can handle this. For example, GitHub now has Copilot integrated in pull requests for code reviews. It’s never a bad idea to have whatever change set you’re looking to commit reviewed and inspected before pushing it up to the remote, though. You can provide your unstaged changes or even specific commits as context to a Cursor Ask or Agent prompt. Getting up to speed in a new code base Being able to query a codebase with the power of LLM’s is truly fantastic. It can be a great help to get up to speed in a large new codebase quickly. Some example prompts: > Please provide an overview of this project and how to get started developing with it > I need to make some changes to the way that notifications are grouped in the UI, please provide a detailed analysis and pseudo code outlining how the grouping algorithm works If you have a question about the code base, ask Cursor! Refactoring Refactoring code in a code base is a much quicker process in Cursor. You can execute refactors depending on their scope in a couple of distinct ways. For refactors that don’t span a lot of files or are less complex, you can probably get away with just using the autocomplete. For example, if you make a change to something in a file and there are several instances of the same pattern following, the autocomplete will quickly pick up on this and help you tab through the changes. If you switch to another file, this information will still be in context and can be continued most of the time. For larger refactors spanning several files, using the Agent feature will most likely be the quickest way to get it done. Add all the files you plan to make changes to the Agent tab’s context window. Provide specific instructions and/or a basic example of how to execute the refactor. Let the Agent work, if it doesn’t get it exactly right initially, you can always give it corrections in a follow-up prompt. Generating new code/features This is the big promise of AI agents and the one with the most room for mixed results. My main recommendation here is to keep experimenting. Keep learning to prompt more effectively, compare results from different models, and pay attention to the results you get from each use case. I personally get the best results building new features in small, focused chunks of work. It can also be helpful to have a dialog with the Ask feature first to plan out the feature's details that the Agent can follow up on and implement. If there are existing patterns in your codebase for accomplishing certain things, provide this information in your prompts and make sure to add the relevant code to the context. For example, if you’re adding a new form to the web page and you have other similar forms that handle validation and making back-end calls in the same way, Cursor can base the code for the new feature on this. Example prompt: Generate a form for creating a new post, follow similar patterns from the create user profile form, and look to the post schema for the fields that should be included. Remember that you can always follow up with additional prompts if you aren’t quite happy with the results of the first.. If the results are close but need to be adjusted in some way, let the agent know in the next prompt. You may find that for some things, it just doesn’t do well yet. Mentally note these things and try to get to a place where you can intuit when to reach for the Agent feature or just write some of the code the old-fashioned way. Tips and tricks The more you use Cursor, the more you will find little ways to get more out of it. Here are some of the tips and patterns that I find particularly useful in my day-to-day work. Generating UI with screenshots You can attach images to your prompts that the models can understand using computer vision. To the left of the send button, there is a little button to attach an image from your computer. This functionality is incredibly useful for generating UI code, whether you are giving it an example UI as a reference for generating new UI in your application or providing a screenshot of existing UI in your application and prompting it to change details in reference to the image. Cursor Rules Cursor Rules allow you to add additional information that the LLM models might need to provide the best possible experience in your codebase. You can create global rules as well as project-specific ones. An example use case is if your project has some updated dependency with newer APIs than the one on which the LLM has been trained. I ran into this when adding Tailwind v4 to a project; the models are always generating code based on Tailwind v3 or earlier. Here’s how we can add a rules file to handle this use case: ` If you want to see some more examples, check out the awesome-cursorrules repository. Summary Learn to use Cursor and similar tools to enhance your development process. It may not give you actual superpowers, but it may feel like it. All the features and tools we’ve covered in this post come together to provide an amazing experience for developing all types of software and applications....

Apr 18, 2025

11 mins

AICursor

Let's innovate together!

We're ready to be your trusted technical partners in your digital innovation journey.

Whether it's modernization or custom software solutions, our team of experts can guide you through best practices and how to build scalable, performant software that lasts.

Component Testing in Svelte

Component Testing in Svelte

Starting a new project

Installing required dependencies

Configuring tests

Creating a new test

Events

Props

Wrapping up

Ignacio Falk

You might also like

Introduction to Zod for Data Validation

The HTML Dialog Element: Enhancing Accessibility and Ease of Use

What is Cypress Studio?

Increasing development velocity with Cursor

Let's innovate together!

You might also like

Introduction to Zod for Data Validation

The HTML Dialog Element: Enhancing Accessibility and Ease of Use

What is Cypress Studio?

Increasing development velocity with Cursor