Dogfood: Systematic Web Application QA Testing

Overview

This skill guides you through systematic exploratory QA testing of web applications with browser_exec. You will navigate the application, interact with elements, capture evidence of issues, and produce a structured bug report.

Prerequisites

• The browser toolset must be enabled (browser_exec). browser_exec runs Python with pre-imported helpers (new_tab, goto_url, wait_for_load, page_info, js, fill_input, click_at_xy, capture_screenshot, cdp); browser state persists across calls, Python variables do not.
• A target URL and testing scope from the user

Inputs

The user provides:

1. Target URL — the entry point for testing
2. Scope — what areas/features to focus on (or "full site" for comprehensive testing)
3. Output directory (optional) — where to save screenshots and the report (default: ./dogfood-output)

Workflow

Follow this 5-phase systematic workflow:

Phase 1: Plan

1. Create the output directory structure:

   {output_dir}/
   ├── screenshots/       # Evidence screenshots
   └── report.md          # Final report (generated in Phase 5)
   

2. Identify the testing scope based on user input.
3. Build a rough sitemap by planning which pages and features to test:
   • Landing/home page
   • Navigation links (header, footer, sidebar)
   • Key user flows (sign up, login, search, checkout, etc.)
   • Forms and interactive elements
   • Edge cases (empty states, error pages, 404s)

Phase 2: Explore

Batch each step's actions into one browser_exec call. For each page or feature in your plan:

1. Navigate with a console hook installed. Register the hook on the tab before navigating so errors thrown during page load are captured too:

   # Open page with console-error capture
   HOOK = """window.__qa_errors = [];
   const _e = console.error;
   console.error = (...a) => { window.__qa_errors.push(a.map(String).join(' ')); _e(...a); };
   addEventListener('error', e => window.__qa_errors.push('uncaught: ' + e.message));
   addEventListener('unhandledrejection', e => window.__qa_errors.push('rejection: ' + String(e.reason)));"""
   new_tab('about:blank')  # first call only: the hook binds to the current tab
   cdp('Page.addScriptToEvaluateOnNewDocument', source=HOOK)
   goto_url("https://example.com/page")
   wait_for_load()
   print(page_info())
   print(js('window.__qa_errors || []'))
   

   Re-register the hook at the start of any later call that navigates. Silent JS errors are high-value findings.

2. Read the page structure — list visible interactive elements with their viewport centres (use cdp('Accessibility.getFullAXTree') and filter in Python when you need roles/names):

   # List interactive elements
   print(js("""(() => [...document.querySelectorAll('a,button,input,select,textarea,[role=button]')]
     .filter(e => e.offsetParent).slice(0, 80).map((e, i) => { const r = e.getBoundingClientRect();
       return {i, tag: e.tagName, text: (e.innerText || e.value || e.getAttribute('aria-label') || '').trim().slice(0, 60),
               id: e.id, name: e.name, x: Math.round(r.x + r.width / 2), y: Math.round(r.y + r.height / 2)}; }))()"""))
   

3. Visually assess the page: call capture_screenshot() and inspect the attached image for layout issues, broken elements, or accessibility concerns. If your model cannot view images, pass the printed path to vision_analyze.

4. Test interactive elements systematically:

   • Click buttons and links: js("document.querySelector('#submit').click()"), or click_at_xy(x, y) with a centre from step 2
   • Fill forms: fill_input('input[name=email]', 'test input')
   • Test keyboard navigation: cdp('Input.dispatchKeyEvent', type='keyDown', key='Tab', code='Tab', windowsVirtualKeyCode=9) followed by the matching keyUp, then js('document.activeElement.outerHTML.slice(0, 200)') to see where focus landed; same pattern with key='Enter', code='Enter', windowsVirtualKeyCode=13
   • Scroll through content: js('window.scrollBy(0, 800)')
   • Go back: js('history.back()'), then wait_for_load()
   • Test form validation with invalid inputs
   • Test empty submissions

5. After each interaction, in the same call, check for:

   • Console errors: print(js('window.__qa_errors || []'))
   • Visual changes: capture_screenshot() and compare with the previous one
   • Expected vs actual behavior (page_info(), or js() reading the relevant element)

Phase 3: Collect Evidence

For every issue found:

1. Take a screenshot showing the issue: capture_screenshot() prints the saved path. Copy it to {output_dir}/screenshots/ under a descriptive name (e.g. issue-03-broken-login.png) — you will reference it in the report.

2. Record the details:

   • URL where the issue occurs
   • Steps to reproduce
   • Expected behavior
   • Actual behavior
   • Console errors (if any)
   • Screenshot path

3. Classify the issue using the issue taxonomy (see references/issue-taxonomy.md):

   • Severity: Critical / High / Medium / Low
   • Category: Functional / Visual / Accessibility / Console / UX / Content

Phase 4: Categorize

1. Review all collected issues.
2. De-duplicate — merge issues that are the same bug manifesting in different places.
3. Assign final severity and category to each issue.
4. Sort by severity (Critical first, then High, Medium, Low).
5. Count issues by severity and category for the executive summary.

Phase 5: Report

Generate the final report using the template at templates/dogfood-report-template.md.

The report must include:

1. Executive summary with total issue count, breakdown by severity, and testing scope
2. Per-issue sections with:
   • Issue number and title
   • Severity and category badges
   • URL where observed
   • Description of the issue
   • Steps to reproduce
   • Expected vs actual behavior
   • Screenshot references (use MEDIA:<screenshot_path> for inline images)
   • Console errors if relevant
3. Summary table of all issues
4. Testing notes — what was tested, what was not, any blockers

Save the report to {output_dir}/report.md.

Tools Reference

All browser actions are browser_exec calls; these are the helpers and idioms used above.

Action	browser_exec idiom
Go to a URL	new_tab(url) once (or new_tab('about:blank') before registering the hook), then goto_url(url); follow with wait_for_load()
Page state	page_info()
DOM / accessibility tree	js(...) extraction, or cdp('Accessibility.getFullAXTree') filtered in Python
Click	js("document.querySelector(sel).click()") or click_at_xy(x, y)
Type into an input	fill_input(selector, text)
Scroll	js('window.scrollBy(0, 800)')
Go back	js('history.back()')
Press a key	cdp('Input.dispatchKeyEvent', type='keyDown', key=..., code=..., windowsVirtualKeyCode=...) + keyUp
Screenshot	capture_screenshot() (image attached to the result; text-only models use vision_analyze on the path)
JS console errors	hook via cdp('Page.addScriptToEvaluateOnNewDocument', source=HOOK), read with `js('window.__qa_errors

Tips

• Always read window.__qa_errors after navigating and after significant interactions. Silent JS errors are among the most valuable findings.
• Batch each sub-procedure into one browser_exec call (navigate, wait, extract, act, check errors) — fewer round-trips, and the hook stays in place.
• Take a capture_screenshot() when you need to reason about element positions or when DOM extraction is ambiguous; click_at_xy takes the centres from the element listing.
• Test with both valid and invalid inputs — form validation bugs are common.
• Scroll through long pages — content below the fold may have rendering issues.
• Test navigation flows — click through multi-step processes end-to-end.
• Check responsive behavior by noting any layout issues visible in screenshots.
• Don't forget edge cases: empty states, very long text, special characters, rapid clicking.
• When reporting screenshots to the user, include MEDIA:<screenshot_path> so they can see the evidence inline.