Testdriver:Dashcam logo

Testdriver:Dashcam

Organization
testdriverai
testdriver:dashcam

Record test execution with video and logs

Overview

Publishertestdriverai
Repositorytestdriverai
Skill nametestdriver:dashcam
Stars
465
Forks
39
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 testdriverai on GitHub. Read the source before you install it.

Installation

Install the Testdriver:Dashcam 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/testdriverai/testdriverai/tree/main/ai/skills/testdriver-dashcam
  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/testdriverai/testdriverai.git /tmp/testdriverai
mkdir -p .claude/skills
cp -r /tmp/testdriverai/ai/skills/testdriver-dashcam .claude/skills/testdriverai-testdriver-dashcam
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Testdriver:Dashcam 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 Testdriver:Dashcam 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 Testdriver:Dashcam 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.

Overview

Dashcam makes automatic video recordings. It also collects the logs for your tests. It captures screen recordings, application logs, and test details. You can review them later.

Basic Usage

With Presets

Most presets include Dashcam automatically:

javascript
import { test } from 'vitest';
import { chrome } from 'testdriverai/presets';

test('my test', async (context) => {
  const { testdriver, dashcam } = await chrome(context, {
    url: 'https://example.com'
  });
  
  // Test executes with recording automatically
  await testdriver.find('login button').then(el => el.click());
  
  // Dashcam URL available after test
  console.log('Replay:', dashcam.url);
});

Manual Setup

For more control, make a Dashcam instance directly:

javascript
import TestDriver from 'testdriverai';
import Dashcam from 'testdriverai/lib/core/Dashcam.js';

const client = await TestDriver.create({ os: 'linux' });
const dashcam = new Dashcam(client, {
  apiKey: process.env.DASHCAM_API_KEY
});

await dashcam.auth();
await dashcam.start();

// Run your tests

const url = await dashcam.stop();
console.log('Replay URL:', url);

Constructor

Create a new Dashcam instance:

javascript
new Dashcam(client, options)

Parameters

<ParamField path="autoStart" type="boolean" default={false}>
  Automatically start recording after authentication
</ParamField>

<ParamField path="logs" type="array" default={[]}>
  Log configurations to add automatically
</ParamField>

Methods

auth()

Authenticate with Dashcam service:

javascript
await dashcam.auth(apiKey)

Returns: Promise<void>

Example:

javascript
await dashcam.auth('your-api-key');

start()

Start recording:

javascript
await dashcam.start()

Returns: Promise<void>

Example:

javascript
await dashcam.start();
console.log('Recording started');

stop()

Stop recording and retrieve replay URL:

javascript
await dashcam.stop()

Returns: Promise<string|null> - Replay URL if available

Example:

javascript
const url = await dashcam.stop();
if (url) {
  console.log('Watch replay:', url);
} else {
  console.log('No replay URL available');
}

addFileLog()

Track a log file in the recording:

javascript
await dashcam.addFileLog(path, name)

Returns: Promise<void>

Example:

javascript
// Linux/Mac
await dashcam.addFileLog('/tmp/app.log', 'Application Log');

// Windows
await dashcam.addFileLog('C:\\logs\\app.log', 'Application Log');

addApplicationLog()

Track application-specific logs:

javascript
await dashcam.addApplicationLog(application, name)

Returns: Promise<void>

Example:

javascript
await dashcam.addApplicationLog('Google Chrome', 'Browser Logs');

addWebLog()

Track web request logs by URL pattern:

javascript
await dashcam.addWebLog(pattern, name)

Returns: Promise<void>

Example:

javascript
await dashcam.addWebLog('*example.com*', 'Web Logs');

addLog()

Generic method to add any type of log:

javascript
await dashcam.addLog(config)
<ParamField path="type" type="string" required>
  Log type: `'file'`, `'application'`, or `'web'`
</ParamField>

<ParamField path="path" type="string">
  File path (required for type='file')
</ParamField>

<ParamField path="application" type="string">
  Application name (required for type='application')
</ParamField>

<ParamField path="pattern" type="string">
  URL pattern to match (required for type='web', e.g., `"*example.com*"`)
</ParamField>

Returns: Promise<void>

Example:

javascript
await dashcam.addLog({
  name: 'Test Output',
  type: 'file',
  path: '/tmp/test.log'
});

await dashcam.addLog({
  name: 'Chrome Logs',
  type: 'application',
  application: 'Google Chrome'
});

await dashcam.addLog({
  name: 'Web Logs',
  type: 'web',
  pattern: '*example.com*'
});

isRecording()

Check if currently recording:

javascript
await dashcam.isRecording()

Returns: Promise<boolean> - True if recording is active

Example:

javascript
if (await dashcam.isRecording()) {
  console.log('Recording in progress');
}

Properties

recording

Current recording state:

javascript
dashcam.recording // boolean

apiKey

Configured API key:

javascript
dashcam.apiKey // string

client

Associated TestDriver client:

javascript
dashcam.client // TestDriver instance

Complete Examples

Basic Recording

javascript
import { test } from 'vitest';
import TestDriver from 'testdriverai';
import Dashcam from 'testdriverai/lib/core/Dashcam.js';

test('record test execution', async () => {
  const client = await TestDriver.create({ os: 'linux' });
  const dashcam = new Dashcam(client);
  
  await dashcam.auth();
  await dashcam.start();
  
  // Run your test
  await client.find('button').then(el => el.click());
  
  const url = await dashcam.stop();
  console.log('Replay:', url);
  
  await client.cleanup();
});

With Log Tracking

javascript
test('record with logs', async () => {
  const client = await TestDriver.create({ os: 'linux' });
  const dashcam = new Dashcam(client);
  
  await dashcam.auth();
  
  // Add log files before starting
  await dashcam.addFileLog('/tmp/testdriver.log', 'TestDriver Log');
  await dashcam.addFileLog('/tmp/app.log', 'Application Log');
  
  await dashcam.start();
  
  // Test execution
  await client.find('login button').then(el => el.click());
  
  const url = await dashcam.stop();
  console.log('Replay with logs:', url);
  
  await client.cleanup();
});

Auto-start Configuration

javascript
test('auto-start recording', async () => {
  const client = await TestDriver.create({ os: 'linux' });
  const dashcam = new Dashcam(client, {
    autoStart: true,
    logs: [
      {
        name: 'App Log',
        type: 'file',
        path: '/tmp/app.log'
      }
    ]
  });
  
  await dashcam.auth(); // Automatically starts recording
  
  // Test execution
  await client.find('submit button').then(el => el.click());
  
  const url = await dashcam.stop();
  console.log('Replay:', url);
  
  await client.cleanup();
});

Using with Presets

javascript
import { chrome } from 'testdriverai/presets';

test('preset with dashcam', async (context) => {
  const { testdriver, dashcam } = await chrome(context, {
    url: 'https://example.com',
    dashcam: true // Enabled by default
  });
  
  // Test runs with automatic recording
  await testdriver.find('button').then(el => el.click());
  
  // URL automatically available
  console.log('Replay:', dashcam.url);
});

Disabling Dashcam in Presets

javascript
test('without dashcam', async (context) => {
  const { testdriver } = await chrome(context, {
    url: 'https://example.com',
    dashcam: false // Disable recording
  });
  
  // Test runs without recording (faster)
  await testdriver.find('button').then(el => el.click());
});

Platform Differences

Windows

On Windows, Dashcam uses PowerShell commands and installs via npm:

javascript
// Windows-specific paths
await dashcam.addFileLog(
  'C:\\Users\\testdriver\\Documents\\testdriver.log',
  'TestDriver Log'
);

Linux/Mac

On Linux/Mac, Dashcam uses shell commands:

javascript
// Unix-specific paths
await dashcam.addFileLog('/tmp/testdriver.log', 'TestDriver Log');

Frequently asked questions

What does the Testdriver:Dashcam AI skill do?

Record test execution with video and logs

Why use Testdriver:Dashcam on TypingMind?

Because you install it once and use it with any model. Testdriver:Dashcam 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 Testdriver:Dashcam in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/testdriverai/testdriverai/tree/main/ai/skills/testdriver-dashcam. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Testdriver:Dashcam?

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 Testdriver:Dashcam?

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

Is the Testdriver:Dashcam AI skill free?

It is published on GitHub by testdriverai. 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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