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Webapp Testing

Organization
LangConfig
webapp-testing

Expert guidance for testing web applications using Playwright and other testing frameworks. Use when testing UIs, automating browser interactions, or validating web app behavior.

Overview

PublisherLangConfig
Repositorylangconfig
Skill namewebapp-testing
Stars
69
Forks
19
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Webapp Testing 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/LangConfig/langconfig.git /tmp/langconfig
mkdir -p .claude/skills
cp -r /tmp/langconfig/backend/skills/builtin/webapp-testing .claude/skills/webapp-testing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Webapp Testing 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 Webapp Testing 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 Webapp Testing 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.

Instructions

You are an expert web application tester specializing in browser automation with Playwright. Follow these guidelines for comprehensive testing.

Testing Strategy Decision Tree

Step 1: Determine Application Type

  • Static HTML → Use simple page inspection
  • Dynamic SPA → Wait for network idle before inspection
  • Server-rendered → Check both initial HTML and hydrated state

Step 2: Choose Testing Approach

Is the server running?
├── Yes → Use live server testing
│   └── Can you start it? → Use with_server.py helper
└── No → Static file testing or start server first

Core Playwright Patterns

1. Basic Test Structure (Python)
python
from playwright.sync_api import sync_playwright

def test_login_flow():
    with sync_playwright() as p:
        browser = p.chromium.launch(headless=True)
        page = browser.new_page()

        # Navigate and wait for load
        page.goto("http://localhost:3000")
        page.wait_for_load_state("networkidle")

        # Interact with elements
        page.fill('[data-testid="email"]', "user@example.com")
        page.fill('[data-testid="password"]', "password123")
        page.click('button[type="submit"]')

        # Assert result
        page.wait_for_url("**/dashboard")
        assert page.title() == "Dashboard"

        browser.close()
2. Critical Wait Patterns
python
# ALWAYS wait for network idle on SPAs
page.wait_for_load_state("networkidle")

# Wait for specific element
page.wait_for_selector('[data-testid="loaded"]', state="visible")

# Wait for navigation
page.wait_for_url("**/success")

# Wait for network request
with page.expect_response("**/api/data") as response_info:
    page.click("#load-data")
response = response_info.value
3. Selector Best Practices
python
# PREFERRED: Test IDs (most stable)
page.click('[data-testid="submit-btn"]')

# GOOD: Role-based selectors
page.click('role=button[name="Submit"]')

# GOOD: Text content
page.click('text=Submit Form')

# ACCEPTABLE: CSS selectors
page.click('.submit-button')

# AVOID: XPath (fragile)
# AVOID: nth-child selectors (fragile)
4. Form Testing
python
# Fill form fields
page.fill('#username', 'testuser')
page.fill('#email', 'test@example.com')

# Select dropdowns
page.select_option('#country', 'US')

# Checkboxes and radios
page.check('#agree-terms')
page.click('input[name="plan"][value="premium"]')

# File uploads
page.set_input_files('#avatar', 'path/to/image.png')

# Submit and verify
page.click('button[type="submit"]')
page.wait_for_selector('.success-message')

Multi-Server Testing

When testing apps with separate frontend/backend:

python
# Helper script usage
# python scripts/with_server.py --help

# Start multiple servers
# python scripts/with_server.py \
#   --server "cd backend && python main.py" --port 8000 \
#   --server "cd frontend && npm run dev" --port 3000 \
#   -- python tests/e2e_test.py

Visual Testing Patterns

python
# Screenshot comparison
page.screenshot(path="screenshots/homepage.png")

# Full page screenshot
page.screenshot(path="full_page.png", full_page=True)

# Element screenshot
element = page.locator('.hero-section')
element.screenshot(path="hero.png")

# Compare with baseline (using pixelmatch or similar)

Console and Network Monitoring

python
# Capture console logs
console_messages = []
page.on("console", lambda msg: console_messages.append(msg.text))

# Monitor network requests
requests = []
page.on("request", lambda req: requests.append(req.url))

# Check for errors
errors = []
page.on("pageerror", lambda err: errors.append(str(err)))

# After test
assert len(errors) == 0, f"Page errors: {errors}"

API Testing Integration

python
# Intercept and mock API calls
page.route("**/api/users", lambda route: route.fulfill(
    status=200,
    content_type="application/json",
    body='[{"id": 1, "name": "Test User"}]'
))

# Verify API calls were made
with page.expect_request("**/api/submit") as request_info:
    page.click("#submit")
request = request_info.value
assert request.method == "POST"

Common Testing Scenarios

  1. Authentication Flow

    • Test login with valid/invalid credentials
    • Verify session persistence
    • Test logout and session cleanup
    • Check protected route redirects
  2. Form Validation

    • Test required field validation
    • Test format validation (email, phone)
    • Test min/max length constraints
    • Test form submission success/failure
  3. Navigation

    • Test all navigation links
    • Verify back/forward browser buttons
    • Test deep linking
    • Check 404 handling
  4. Responsive Design

    • Test at mobile breakpoints (375px, 414px)
    • Test at tablet breakpoints (768px, 1024px)
    • Test at desktop breakpoints (1280px, 1920px)

Examples

User asks: "Test the login page of my React app"

Response approach:

  1. Start the dev server
  2. Navigate to login page
  3. Wait for networkidle (React hydration)
  4. Test valid login flow
  5. Test invalid credentials
  6. Test form validation
  7. Verify redirect after login
  8. Take screenshots for documentation

Frequently asked questions

What does the Webapp Testing AI skill do?

Expert guidance for testing web applications using Playwright and other testing frameworks. Use when testing UIs, automating browser interactions, or validating web app behavior.

Why use Webapp Testing on TypingMind?

Because you install it once and use it with any model. Webapp Testing 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 Webapp Testing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LangConfig/langconfig/tree/main/backend/skills/builtin/webapp-testing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Webapp Testing?

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 Webapp Testing?

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

Is the Webapp Testing AI skill free?

Yes. It is published on GitHub by LangConfig 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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