Reliably test your most difficult user flows
npx skills add https://github.com/testdriverai/testdriverai --skill testdriver:what-is-testdriver
<!-- Generated from what-is-testdriver.mdx. DO NOT EDIT. -->
Modern testing tools like Playwright are designed to test a single web application, running in a single browser tab using selectors.
However, selectors are often either unreliable or unavailable in complex scenarios, leading to brittle and flaky tests:
| Challenge | Problem | Examples |
|-----------|---------|----------|
| Fast moving teams | Frequently change UI structure, breaking CSS/XPath selectors | Agile teams, startups, vibe-coders |
| Dynamic content | Cannot be targeted with selectors | AI chatbots, PDFs, images, videos |
| Software you don't own | May lack proper accessibility attributes | Other websites, extensions, third-party applications |
| Multi-application workflows | Cannot be tested with web-only tools | Desktop apps, browser extensions, IDEs |
| Visual states | Impossible to verify with code-based selectors | Charts, graphs, videos, images, spelling errors, UI layout |
TestDriver is a complete testing platform built specifically for handling these scenarios. It consists of a Javascript SDK, hosted infrastructure, and debugging tools that make it easy to write, run, and maintain tests for your most difficult user flows.
Here is an example of a TestDriver test that installs a production Chrome extension from the Chrome Web Store and verifies that it appears in the extensions menu:
import { describe, expect, it } from "vitest";
import { TestDriver } from "testdriverai/vitest/hooks";
describe("Chrome Extension Test", () => {
const testdriver = TestDriver(context);
// Launch Chrome with Loom loaded by its Chrome Web Store ID
await testdriver.provision.chromeExtension({
extensionId: 'liecbddmkiiihnedobmlmillhodjkdmb'
});
// Click on the extensions button (puzzle piece icon) in Chrome toolbar
const extensionsButton = await testdriver.find("The puzzle-shaped icon in the Chrome toolbar.");
await extensionsButton.click();
// Look for Loom in the extensions menu
const loomExtension = await testdriver.find("Loom extension in the extensions dropdown");
expect(loomExtension.found()).toBeTruthy();
});
<Tip>vitest is the preferred test runner for TestDriver.</Tip>
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Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Use when implementing any feature or bugfix, before writing implementation code
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
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