Dogfood logo

Dogfood

Community
Tommy-yw
dogfood

Systematic exploratory QA testing of web applications — find bugs, capture evidence, and generate structured reports

Overview

PublisherTommy-yw
RepositoryRunbookHermes
Skill namedogfood
Stars
546
Forks
41
Bundled files
2
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Tommy-yw on GitHub. Read the source before you install it.

Installation

Install the Dogfood 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/Tommy-yw/RunbookHermes/tree/main/skills/dogfood
  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/Tommy-yw/RunbookHermes.git /tmp/RunbookHermes
mkdir -p .claude/skills
cp -r /tmp/RunbookHermes/skills/dogfood .claude/skills/dogfood
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dogfood 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 Dogfood 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 Dogfood 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.

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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Dogfood AI skill do?

Systematic exploratory QA testing of web applications — find bugs, capture evidence, and generate structured reports

Why use Dogfood on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Tommy-yw/RunbookHermes/tree/main/skills/dogfood. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Dogfood?

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 Dogfood?

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

Is the Dogfood AI skill free?

Yes. It is published on GitHub by Tommy-yw 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇