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QA exploratoire (dogfooding)

/SKILL

Cette compétence vous guide à travers des tests exploratoires systématiques d'assurance qualité d'applications web en utilisant l'ensemble des outils du navigateur. Vous naviguerez dans l'application,

NousResearchNousResearch
231.7k
5 juin 2026
MIT
// contenu du skill

name: dogfood

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

version: 1.0.0

platforms: [linux, macos, windows]

metadata:

hermes:

tags: [qa, testing, browser, web, dogfood]

related_skills: []


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)
  1. Identify the testing scope based on user input.
  2. 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")
  1. Take a snapshot to understand the DOM structure:
   browser_snapshot()
  1. 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.

  1. 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.

  1. 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
  1. 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.

  1. Record the details:
  • URL where the issue occurs
  • Steps to reproduce
  • Expected behavior
  • Actual behavior
  • Console errors (if any)
  • Screenshot path
  1. 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
  1. Summary table of all issues
  2. 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 tr
// source originale publique
NousResearch/hermes-agent
/skills/dogfood/SKILL.md
Licence : MIT
Projet indépendant, non affilié à Anthropic. Ce skill reste la propriété de son auteur original.
// installer ce skill
Collez cette commande dans votre terminal à la racine de votre projet :
mkdir -p .claude/commands && curl -o ".claude/commands/SKILL.md" "https://raw.githubusercontent.com/NousResearch/hermes-agent/main/skills/dogfood/SKILL.md"
Ensuite dans Claude Code, tapez /SKILL pour l'activer.
open_in_newVoir la source originale
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// informations
CréateurNousResearch
Étoiles 231.7k
LicenceMIT
Mis à jour5 juin 2026
Format.md
AccèsGratuit
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