/dogfood

description: Systematic exploratory QA testing of web applications — find bugs, capture
  evidence, and generate structured reports
version: 1.0.0
metadata:
  catalog:
    tags:
    - qa
    - testing
    - browser
    - web
    - dogfood
    related_skills: []

Kheish Compatibility

This skill is repo-local and stays inactive until explicitly activated.

When the original instructions refer to legacy tool names, use these Kheish mappings:

• `terminal` => `bash`
• `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
• `search_files` => `grep_search` and `glob_search`
• `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly

When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.

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

| Tool | Purpose | |------|---------| | `browser_navigate` | Go to a URL | | `browser_snapshot` | Get DOM text snapshot (accessibility tree) | | `browser_click` | Click an element by ref (`@eN`) or text | | `browser_type` | Type into an input field | | `browser_scroll` | Scroll up/down on the page | | `browser_back` | Go back in browser history | | `browser_press` | Press a keyboard key | | `browser_vision` | Screenshot + AI analysis; use `annotate=true` for element labels | | `browser_console` | Get JS console output and