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E2e Testing

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billy-enrizky
e2e-testing

Test web applications end-to-end by simulating user interactions and verifying expected outcomes. Trigger when the user asks to: test a web app, verify a user flow, run end-to-end tests, QA a feature, check that a page works correctly, validate user journeys, or test a deployment.

Overview

Publisherbilly-enrizky
Repositoryopenbrowser-ai
Skill namee2e-testing
Stars
279
Forks
31
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 billy-enrizky on GitHub. Read the source before you install it.

Installation

Install the E2e 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.
    https://github.com/billy-enrizky/openbrowser-ai/tree/main/plugin/skills/e2e-testing
  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/billy-enrizky/openbrowser-ai.git /tmp/openbrowser-ai
mkdir -p .claude/skills
cp -r /tmp/openbrowser-ai/plugin/skills/e2e-testing .claude/skills/e2e-testing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable E2e 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 E2e 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 E2e 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.

End-to-End Testing

Simulate real user interactions and verify web application behavior using Python code execution. Covers navigation, form interaction, content assertions, and multi-page flows.

All code runs via openbrowser-ai -c. The daemon starts automatically and persists variables across calls. All browser functions are async -- use await.

The CLI daemon also persists cookies and login state in ~/.config/openbrowser/profiles/daemon/storage_state.json, so authenticated sessions can be reused across later runs.

Setup

Before running, verify openbrowser-ai is installed:

bash
openbrowser-ai --help

If not found, install:

bash
# macOS/Linux
curl -fsSL https://raw.githubusercontent.com/billy-enrizky/openbrowser-ai/main/install.sh | sh

# Windows (PowerShell)
irm https://raw.githubusercontent.com/billy-enrizky/openbrowser-ai/main/install.ps1 | iex

Workflow

Step 1 -- Navigate and verify page load

bash
openbrowser-ai -c - <<'EOF'
await navigate("https://staging.example.com")
state = await browser.get_browser_state_summary()

assert "example" in state.url.lower(), f"Unexpected URL: {state.url}"
assert state.title, "Page title is empty"
print(f"Page loaded: {state.title} ({state.url})")
EOF

Step 2 -- Content assertions

bash
openbrowser-ai -c - <<'EOF'
# Check for expected text using JS
has_welcome = await evaluate("""
(function(){ return !!document.body.textContent.match(/Welcome to Example App/i) })()
""")
assert has_welcome, "Welcome message not found"

# Check specific element content
h1_text = await evaluate("document.querySelector('h1')?.textContent?.trim()")
assert h1_text == "Example App", f'Expected "Example App", got "{h1_text}"'

# Check no error messages
error_count = await evaluate("document.querySelectorAll('.error-message').length")
assert error_count == 0, f"Found {error_count} error messages on page"

print("All content assertions passed")
EOF

Step 3 -- Test user interactions (login flow)

bash
openbrowser-ai -c - <<'EOF'
# Get form fields
state = await browser.get_browser_state_summary()
for idx, el in state.dom_state.selector_map.items():
    if el.attributes.get("type") in ("email", "text", "password") or el.tag_name == "button":
        etype = el.attributes.get("type", "")
        placeholder = el.attributes.get("placeholder", "")
        print(f"[{idx}] <{el.tag_name}> type={etype} placeholder=\"{placeholder}\"")

# Fill and submit
await input_text(index=3, text="test@example.com")
await input_text(index=4, text="test-password")
await click(index=5)  # Login button
await wait(2)

# Assert logged in
state = await browser.get_browser_state_summary()
assert "dashboard" in state.url.lower() or "welcome" in state.title.lower(), \
    f"Login may have failed. URL: {state.url}, Title: {state.title}"
print("Login test passed")
EOF

Step 4 -- Test navigation flows

bash
openbrowser-ai -c - <<'EOF'
# Click settings link
state = await browser.get_browser_state_summary()
for idx, el in state.dom_state.selector_map.items():
    if "settings" in el.get_all_children_text(max_depth=1).lower():
        await click(index=idx)
        break

await wait(1)

# Assert URL changed
path = await evaluate("window.location.pathname")
assert "/settings" in path, f"Expected /settings path, got {path}"

# Test back button
await go_back()
await wait(1)
state = await browser.get_browser_state_summary()
print(f"After back: {state.url}")
EOF

Step 5 -- Test error handling

bash
openbrowser-ai -c - <<'EOF'
# Submit form with invalid data
await input_text(index=3, text="not-an-email")
await click(index=5)
await wait(1)

# Assert validation errors appear
errors = await evaluate("""
(function(){
  const errs = document.querySelectorAll(".error, .invalid-feedback, [aria-invalid=\"true\"]");
  return Array.from(errs).map(e => e.textContent.trim());
})()
""")
assert len(errors) > 0, "Expected validation errors but found none"
print(f"Validation errors shown: {errors}")

# Assert page did not navigate
path = await evaluate("window.location.pathname")
print(f"Still on: {path}")
EOF

Step 6 -- Test responsive behavior

bash
openbrowser-ai -c - <<'EOF'
viewport = await evaluate("""
(function(){
  return {
    width: window.innerWidth,
    height: window.innerHeight
  }
})()
""")
vw = viewport["width"]
vh = viewport["height"]
print(f"Viewport: {vw}x{vh}")

# Check mobile menu visibility
mobile_display = await evaluate("""
(function(){
  const el = document.querySelector(".mobile-menu");
  return el ? window.getComputedStyle(el).display : "not found";
})()
""")
print(f"Mobile menu display: {mobile_display}")
EOF

Step 7 -- Test multi-page flows

bash
openbrowser-ai -c - <<'EOF'
test_results = []

# Cart page
await navigate("https://staging.example.com/cart")
await wait(1)
cart_title = await evaluate("document.querySelector('h1')?.textContent?.trim()")
test_results.append({"test": "cart_page_loads", "passed": cart_title is not None, "detail": cart_title})

# Checkout
cart_count = await evaluate("JSON.parse(localStorage.getItem('cart'))?.items?.length || 0")
test_results.append({"test": "cart_has_items", "passed": cart_count > 0, "detail": f"{cart_count} items"})

# Print results
import json
passed = sum(1 for t in test_results if t["passed"])
total = len(test_results)
print(f"\nResults: {passed}/{total} passed")
print(json.dumps(test_results, indent=2))
EOF

Tips

  • Code is piped via stdin using heredoc (-c - <<'EOF'), so all Python syntax works without shell escaping issues.
  • Use Python assert statements for test assertions -- they produce clear error messages on failure.
  • Always verify page state after each interaction before proceeding.
  • Test both happy paths and error paths for thorough coverage.
  • Use await wait(N) after interactions that trigger page loads or animations.
  • Variables persist between -c calls while the daemon is running, so you can accumulate test results across calls.
  • Use evaluate() for DOM state assertions and browser.get_browser_state_summary() for page metadata.

Cleanup

This step is mandatory. Run it after the test run finishes, whether assertions passed or failed. Without it, the daemon keeps Chrome running until its 10-minute idle timeout, leaving a stale browser process, a locked profile, and (on macOS/Linux desktop) a visible window.

Stop the daemon, then verify it is gone:

bash
openbrowser-ai daemon stop
openbrowser-ai daemon status

daemon stop closes every tab, exits Chrome, flushes saved cookies/login state to the profile, and shuts down the daemon process. daemon status should report the daemon is not running. If it still reports running, the daemon is wedged, force-kill it:

bash
pkill -f 'openbrowser.*daemon' || true

E2E runs fail often by design (that is what assertions are for). Guarantee cleanup with a shell trap so a failed assertion never leaks a browser:

bash
trap 'openbrowser-ai daemon stop >/dev/null 2>&1 || true' EXIT
# ... openbrowser-ai -c calls here ...

Do not rely on the idle timeout. Do not call done() as a substitute, done() only marks the task complete inside the agent loop, it does not close the browser.

Frequently asked questions

What does the E2e Testing AI skill do?

Test web applications end-to-end by simulating user interactions and verifying expected outcomes. Trigger when the user asks to: test a web app, verify a user flow, run end-to-end tests, QA a feature, check that a page works correctly, validate user journeys, or test a deployment.

Why use E2e Testing on TypingMind?

Because you install it once and use it with any model. E2e 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 E2e Testing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/billy-enrizky/openbrowser-ai/tree/main/plugin/skills/e2e-testing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use E2e 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 E2e Testing?

As many as you like. As long as a model supports skills, you can use E2e Testing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the E2e Testing AI skill free?

Yes. It is published on GitHub by billy-enrizky 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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