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dogfood

Explore a web app in the browser to find functional, visual, and usability issues. The skill organizes screenshots, console errors, and reproduction steps into a Markdown bug report, with findings grouped by severity and category.

OriginalShow the original description

Exploratory QA of web apps: find bugs, evidence, reports.

AI rating80/ 100See how to install

Good for

  • Explore key user flows before release
  • Check forms with valid, invalid, and empty input
  • Document layout issues and JavaScript errors
  • Create a prioritized bug report with screenshots

Overview

What it does

This skill explores a web app through real browser interactions and produces a bug report with supporting evidence. It plans which pages and features to test, then checks links, buttons, forms, keyboard controls, and navigation. Alongside valid input, it tries invalid values and empty fields, and examines layout problems and JavaScript errors. Findings are deduplicated, assigned a severity and category, and collected in report.md.

When it helps

Use it before release to check an app along the paths users take, or when developers need bug reports they can reproduce. You can cover the whole site or focus on a feature such as login or search. The report records what was tested, what remains untested, and anything that blocked testing.

Example requests

  • “Check the registration form at this URL, including empty fields and invalid input.”
  • “Test the main navigation flows and report issues with reproduction steps and screenshots.”

Notes

You need browser tools for navigation, clicks, text entry, screenshots, and console inspection. Specify the target URL and testing scope. Screenshots and the report are saved to ./dogfood-output unless you provide another output directory.

This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.

Objective evaluation

AI rated this skill on four points using only the contents of its SKILL.md. Stars and popularity are not taken into account.

Clear instructionsIs it clear when to use it and what it does?
AGoodThe material specifies the target URL and scope as inputs and browser-based bug investigation and reporting as the purpose.
Concrete stepsAre steps, inputs, outputs, and examples spelled out?
AGoodFive phases run from planning to reporting, with required report fields including reproduction steps and screenshots.
SafetyAre sending data, deleting files, and running commands disclosed?
BPartly lackingTesting includes signup, checkout, and empty submissions, but effects on live data and confirmation procedures are not described.
Easy setupDoes it need few tools or API keys, and are they documented?
BPartly lackingRequired browser tools are listed, but installation and connection instructions are absent.

The total counts each point as A = 25, B = 15, C = 5. Rated by AI (OpenAI Codex) as of Oct 20, 2026. Treat it as a guide and read the original before installing.

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Files included (3)

  • SKILL.md6.2 KB
  • references/issue-taxonomy.md3.6 KB
  • templates/dogfood-report-template.md1.5 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Dogfood: Systematic Web Application QA Testing

Overview

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

Prerequisites

  • Browser toolset must be available (browser_navigate, browser_snapshot, browser_click, browser_type, browser_vision, browser_console, browser_scroll, browser_back, browser_press)
  • 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

For each page or feature in your plan:

  1. Navigate to the page:

    browser_navigate(url="https://example.com/page")
    
  2. Take a snapshot to understand the DOM structure:

    browser_snapshot()
    
  3. Check the console for JavaScript errors:

    browser_console(clear=true)
    

    Do this after every navigation and after every significant interaction. Silent JS errors are high-value findings.

  4. Take an annotated screenshot to visually assess the page and identify interactive elements:

    browser_vision(question="Describe the page layout, identify any visual issues, broken elements, or accessibility concerns", annotate=true)
    

    The annotate=true flag overlays numbered [N] labels on interactive elements. Each [N] maps to ref @eN for subsequent browser commands.

  5. Test interactive elements systematically:

    • Click buttons and links: browser_click(ref="@eN")
    • Fill forms: browser_type(ref="@eN", text="test input")
    • Test keyboard navigation: browser_press(key="Tab"), browser_press(key="Enter")
    • Scroll through content: browser_scroll(direction="down")
    • Test form validation with invalid inputs
    • Test empty submissions
  6. After each interaction, check for:

    • Console errors: browser_console()
    • Visual changes: browser_vision(question="What changed after the interaction?")
    • Expected vs actual behavior

Phase 3: Collect Evidence

For every issue found:

  1. Take a screenshot showing the issue:

    browser_vision(question="Capture and describe the issue visible on this page", annotate=false)
    

    Save the screenshot_path from the response — 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

ToolPurpose
browser_navigateGo to a URL
browser_snapshotGet DOM text snapshot (accessibility tree)
browser_clickClick an element by ref (@eN) or text
browser_typeType into an input field
browser_scrollScroll up/down on the page
browser_backGo back in browser history
browser_pressPress a keyboard key
browser_visionScreenshot + AI analysis; use annotate=true for element labels
browser_consoleGet JS console output and errors

Tips

  • Always check browser_console() after navigating and after significant interactions. Silent JS errors are among the most valuable findings.
  • Use annotate=true with browser_vision when you need to reason about interactive element positions or when the snapshot refs are unclear.
  • 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.

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