Agent skill
dogfood
Quellcode ansehen: tt-a1i/hermes-agent
Installation
npx skills add tt-a1i/hermes-agent --skill dogfood1
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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:
- Target URL — the entry point for testing
- Scope — what areas/features to focus on (or "full site" for comprehensive testing)
- Output directory (optional) — where to save screenshots and the report (default:
./dogfood-output)
Workflow
Follow this 5-phase systematic workflow:
Phase 1: Plan
- Create the output directory structure:
{output_dir}/ ├── screenshots/ # Evidence screenshots └── report.md # Final report (generated in Phase 5) - Identify the testing scope based on user input.
- 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:
Navigate to the page:
browser_navigate(url="https://example.com/page")Take a snapshot to understand the DOM structure:
browser_snapshot()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.
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=trueflag overlays numbered[N]labels on interactive elements. Each[N]maps to ref@eNfor subsequent browser commands.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
- Click buttons and links:
After each interaction, check for:
- Console errors:
browser_console() - Visual changes:
browser_vision(question="What changed after the interaction?") - Expected vs actual behavior
- Console errors:
Phase 3: Collect Evidence
For every issue found:
Take a screenshot showing the issue:
browser_vision(question="Capture and describe the issue visible on this page", annotate=false)Save the
screenshot_pathfrom the response — you will reference it in the report.Record the details:
- URL where the issue occurs
- Steps to reproduce
- Expected behavior
- Actual behavior
- Console errors (if any)
- Screenshot path
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
- Review all collected issues.
- De-duplicate — merge issues that are the same bug manifesting in different places.
- Assign final severity and category to each issue.
- Sort by severity (Critical first, then High, Medium, Low).
- 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:
- Executive summary with total issue count, breakdown by severity, and testing scope
- 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
- Summary table of all issues
- 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 errors |
Tips
- Always check
browser_console()after navigating and after significant interactions. Silent JS errors are among the most valuable findings. - Use
annotate=truewithbrowser_visionwhen 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.
Verwandte Skills
Mehr aus dieser Quelle: tt-a1i/hermes-agent
So verwenden Sie diesen Skill
- 1
Install dogfood by running
npx skills add tt-a1i/hermes-agent --skill dogfoodin your project directory. Führen Sie den obigen Installationsbefehl in Ihrem Projektverzeichnis aus. Die Skill-Datei wird von GitHub heruntergeladen und in Ihrem Projekt platziert. - 2
Keine Konfiguration erforderlich. Ihr KI-Agent (Claude Code, Cursor, Windsurf usw.) erkennt installierte Skills automatisch und nutzt sie als Kontext bei der Code-Generierung.
- 3
Der Skill verbessert das Verständnis Ihres Agenten für dogfood, und hilft ihm, etablierte Muster zu befolgen, häufige Fehler zu vermeiden und produktionsreifen Code zu erzeugen.
Was Sie erhalten
Skills sind Klartext-Anweisungsdateien — kein ausführbarer Code. Sie kodieren Expertenwissen über Frameworks, Sprachen oder Tools, das Ihr KI-Agent liest, um seine Ausgabe zu verbessern. Das bedeutet null Laufzeit-Overhead, keine Abhängigkeitskonflikte und volle Transparenz: Sie können jede Anweisung vor der Installation lesen und prüfen.
Kompatibilität
Dieser Skill funktioniert mit jedem KI-Coding-Agenten, der das skills.sh-Format unterstützt, einschließlich Claude Code (Anthropic), Cursor, Windsurf, Cline, Aider und anderen Tools, die projektbezogene Kontextdateien lesen. Skills sind auf Transportebene framework-agnostisch — der Inhalt bestimmt, für welche Sprache oder welches Framework er gilt.
Data sourced from the skills.sh registry and GitHub. Install counts and security audits are updated regularly.