Web Tester Skill

Transforms any website or project into a structured QA run: Discover → Plan → Execute → Report

Overview of Phases

Phase 1 → DISCOVERY   : Explore the site, understand its purpose and features
Phase 2 → USE CASES   : Generate end-user use case list
Phase 3 → TEST PLAN   : Convert use cases into concrete, executable test cases
Phase 4 → EXECUTION   : Run tests with Playwright, capture screenshots
Phase 5 → REPORT      : Compile HTML + Markdown report with all results

Choosing Your Execution Tool

Pick the first option that works in your environment:

Option 1: Playwright MCP Tools (recommended)

If Playwright MCP tools are available (e.g.,

mcp__plugin_playwright_playwright__*

), use them — they are the fastest and most token-efficient option (~2.8x faster than CLI, fewer tool calls needed since each MCP call does more work):

browser_navigate → navigate to URL
browser_snapshot → get page state and element refs
browser_take_screenshot → capture screenshots
browser_click → click elements
browser_type → type into inputs
browser_evaluate → run JS assertions
browser_resize → test mobile viewports
browser_console_messages → check for JS errors

Option 2: Playwright CLI

The

@playwright/cli

is a CLI designed for coding agents. Use it when MCP tools are not available. Install if needed:

bash

npm install -g @playwright/cli@latest

Key commands for QA testing:

bash

# Open a page
playwright-cli open https://example.com

# Take a snapshot (returns element refs for interaction)
playwright-cli snapshot

# Screenshot the page
playwright-cli screenshot

# Click, fill forms, type
playwright-cli click <ref>
playwright-cli fill <ref> "text value"
playwright-cli type "search query"
playwright-cli press Enter

# Mobile viewport testing — use a named session with mobile config
playwright-cli -s=mobile open https://example.com
# (configure viewport in .playwright/cli.config.json)

# Check console errors via snapshot output
playwright-cli snapshot

# Close when done
playwright-cli close

Playwright CLI is headless by default. Add

--headed

to watch the browser visually. Use

playwright-cli show

to open a dashboard of all active sessions.

Option 3: Python Playwright Scripts

If Python and Playwright are installed, use the bundled scripts in

scripts/

bash

# Install if needed
pip install playwright && playwright install chromium

# Run discovery
python scripts/discover.py --url <URL> --output discovery.json

# Run tests
python scripts/run_tests.py --url <URL> --test-plan test_plan.json --output test_results/

# Generate report
python scripts/generate_report.py --results test_results/results.json --output qa_report.html

Option 4: Manual Testing

If none of the above are available, read the source code directly and perform manual analysis. Use

curl

fetch

for basic HTTP checks.

Phase 1: Discovery

What to gather

URL — if the user provides a live URL, navigate to it and explore
Project files — if no live URL, inspect source files in the project directory
Purpose — what does the site do? (landing page, e-commerce, dashboard, blog, etc.)
Key pages — home, auth, main feature pages, forms, checkout, etc.
Tech stack — optional but helpful for targeted checks

Discovery with Playwright MCP

1. browser_navigate to the URL
2. browser_snapshot to get the page structure
3. browser_take_screenshot for visual reference
4. Examine links, forms, headings, navigation from the snapshot
5. Navigate to discovered subpages and repeat

Discovery with Playwright CLI

bash

playwright-cli open <URL>
playwright-cli screenshot --name discovery_home
playwright-cli snapshot
# Examine snapshot output for links, forms, navigation, headings
# Follow important links to discover subpages
playwright-cli goto <subpage-url>
playwright-cli screenshot --name discovery_subpage

Discovery with Python script

bash

python scripts/discover.py --url <URL> --max-pages 10 --output discovery.json

Local project (no live URL)

When only source files are available:

Start a local server:

python -m http.server 8080 --directory <project-path>

(or

npx serve <project-path>

or any other static server)

Run discovery against
```
http://localhost:8080
```
If the port is busy, try 8081, 8082, etc.

Important:

file://

URLs may be blocked by some tools. Always prefer HTTP serving.

Phase 2: Generate Use Cases

After discovery, produce a use case list from the perspective of an end user.

Format

UC-01: [Actor] can [action] so that [goal]
UC-02: [Actor] can [action] so that [goal]
...

Categories to cover

Navigation — user browses pages, menu works, links resolve
Authentication — sign up, log in, password reset, logout
Core feature flows — the main thing the site does (purchase, search, submit form, etc.)
Content validation — required text, images, prices, labels appear correctly
Forms — fill in, submit, validate errors, success states
Responsiveness — works on mobile viewport
Error handling — 404 pages, empty states, invalid inputs
Performance / visual — no broken images, no console errors, reasonable load
Accessibility basics — alt text on images, heading hierarchy, landmark elements

Aim for 10–25 use cases depending on site complexity.

Phase 3: Test Plan

Convert each use case into a concrete test case:

TC-01 [UC-01]: Homepage Navigation
  Given: User opens the site root URL
  When:  Page finishes loading
  Then:  - Page title is not empty
         - Navigation menu is visible
         - Logo/brand element is present
         - No 404/500 status code
         Checks: title, nav links count > 0, hero text present

Test case types to include

Type	Examples
Presence checks	Element exists, text is visible, image loads
Functional checks	Button clickable, form submits, menu expands
Data validation	Price format, phone format, required fields
Navigation checks	Links don't 404, routing works
Form validation	Empty submit shows errors, valid submit succeeds
Responsiveness	Mobile viewport renders without overflow
Console errors	No JS errors on page load
Accessibility basics	Images have alt text, headings hierarchy, landmarks

If using the Python scripts, save the test plan as

test_plan.json

— see

references/test_case_schema.md

for the JSON schema.

Phase 4: Test Execution

For each test case, follow this pattern regardless of which tool you use:

Navigate to the target page
Capture a "before" screenshot
Execute steps — clicks, form fills, scrolling, waiting
Run assertions — element presence, text content, console errors, image loading
Capture an "after" screenshot if any interaction occurred
Record result — PASS / FAIL / SKIP + error message + duration

Execution with Playwright MCP

browser_navigate to target URL
browser_take_screenshot for "before" capture
browser_snapshot to check element presence
browser_evaluate to run JS assertions:
  - document.title !== ''
  - document.querySelectorAll('nav').length > 0
  - document.querySelectorAll('img').filter(i => i.naturalWidth === 0).length
browser_click / browser_type for interactions
browser_take_screenshot for "after" capture
browser_console_messages to check for JS errors
browser_resize for mobile viewport testing

Execution with Playwright CLI

bash

# Navigate and screenshot
playwright-cli goto <URL>
playwright-cli screenshot --name tc01_before

# Get page state for assertions
playwright-cli snapshot
# Parse snapshot output to verify elements exist, text content matches, etc.

# Interact
playwright-cli click <ref>
playwright-cli fill <ref> "test@example.com"
playwright-cli press Enter

# After screenshot
playwright-cli screenshot --name tc01_after

For mobile viewport testing, open a separate session with mobile dimensions or resize the browser.

For status code testing (e.g., checking that /404 returns 404): use

playwright-cli

navigation — the tool reports HTTP status in its output. Note that navigating to a 4xx/5xx page may throw an error in some tools; catch it gracefully and record the status code from the error message.

Execution with Python scripts

bash

python scripts/run_tests.py \
  --url <URL> \
  --test-plan test_plan.json \
  --output test_results/

Console error collection

Console errors are important signals. Collect them during each test:

Playwright MCP: Use

browser_console_messages

browser_evaluate

Playwright CLI: Check snapshot output or use
```
playwright-cli
```
evaluation
Python scripts: The script registers a console listener before navigation

Image loading checks

To detect broken images, run this JS on the page:

javascript

Array.from(document.images)
  .filter(img => img.naturalWidth === 0 && img.src && !img.src.startsWith('data:'))
  .map(img => img.src)

An empty result means all images loaded. Otherwise, list the broken URLs.

Accessibility checks

Quick checks to run on each page:

javascript

// Images missing alt text
document.querySelectorAll('img:not([alt]), img[alt=""]').length

// Heading hierarchy (should have h1, not skip levels)
[...document.querySelectorAll('h1,h2,h3,h4,h5,h6')].map(h => h.tagName)

// Landmark elements
document.querySelectorAll('main, nav, header, footer, [role]').length

Phase 5: Report Generation

Report contents

Summary dashboard — total tests, pass/fail/skip counts, pass rate %, tested URL, timestamp
Use case list with traceability to test cases
Test results table — TC ID, name, status badge, duration, error message
Screenshot gallery — inline base64 screenshots per test case (or file paths)
Console errors log — any JS errors captured
Recommendations — auto-generated improvement suggestions based on failures

Report format

Primary: HTML (self-contained, with embedded screenshots as base64)
Secondary: Markdown summary for quick reading

Using the Python report generator

bash

python scripts/generate_report.py \
  --results test_results/results.json \
  --screenshots test_results/screenshots/ \
  --output qa_report.html

Manual report generation

If the Python script is not available, generate the HTML report directly. Build a self-contained HTML file with:

A dark header with site name, URL, and timestamp
Stat cards: total tests, passed, failed, skipped, pass rate
A results table with status badges (green PASS, red FAIL, yellow SKIP)
Embedded screenshots (base64-encoded PNGs)
Recommendations section based on failures

Save the report in the current working directory or wherever the user specified.

Workflow Summary (step-by-step)

1. Receive URL or project files from user
2. Pick your execution tool (MCP > Playwright CLI > Python > Manual)
3. Run discovery → understand pages, structure, features
4. Generate use case list (10–25 use cases)
5. Generate test plan → structured test cases
6. Run tests → collect results + screenshots
7. Generate HTML report
8. Show the report path to the user

Handling Common Situations

Local project without a live URL → Serve files locally:

python -m http.server 8080

npx serve .

→ Test against

http://localhost:8080

→ Do NOT use

file://

URLs — they may be blocked

Site requires authentication → Ask user for test credentials OR → Test only the public-facing pages → Mark auth-gated tests as SKIP with note

Single-page app (React/Vue/Angular) → Wait for page to fully render (networkidle or specific selectors) → Check that JS bundle loads without console errors → Use snapshot/evaluate to verify dynamically rendered content

Large site (many pages) → Focus on critical user paths first → Limit to top 5–10 most important flows → Mention in report which pages were NOT covered

Status code testing (4xx/5xx pages) → Some tools throw errors on non-2xx responses → Catch the error and extract the status code from it → Or use JS

fetch()

to check status codes without navigation

Mobile responsiveness testing → Resize viewport to 390×844 (iPhone) or 360×800 (Android) → Check for horizontal overflow:

document.body.scrollWidth > window.innerWidth

→ Verify key elements are still visible and readable

Reference Files

```
references/test_case_schema.md
```
— JSON schema for test_plan.json
```
scripts/discover.py
```
— Site discovery automation (Python + Playwright)
```
scripts/run_tests.py
```
— Test execution engine (Python + Playwright)
```
scripts/generate_report.py
```
— HTML report generator (Python)

test-web-ui

NPX Install

Tags

SKILL.md Content

Web Tester Skill

Overview of Phases

Choosing Your Execution Tool

Option 1: Playwright MCP Tools (recommended)

Option 2: Playwright CLI

Option 3: Python Playwright Scripts

Option 4: Manual Testing

Phase 1: Discovery

What to gather

Discovery with Playwright MCP

Discovery with Playwright CLI

Discovery with Python script

Local project (no live URL)

Phase 2: Generate Use Cases

Format

Categories to cover

Phase 3: Test Plan

Test case types to include

Phase 4: Test Execution

Execution with Playwright MCP

Execution with Playwright CLI

Execution with Python scripts

Console error collection

Image loading checks

Accessibility checks

Phase 5: Report Generation

Report contents

Report format

Using the Python report generator

Manual report generation

Workflow Summary (step-by-step)

Handling Common Situations

Reference Files