madappgang/claude-code/browser-debugging

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Systematically tests UI functionality, validates design fidelity with AI visual analysis, monitors console output, tracks network requests, and provides debugging reports using Chrome Extension MCP tools. Use after implementing UI features, for design validation, when investigating console errors, for regression testing, or when user mentions testing, browser bugs, console errors, or UI verification.

Install

npx vskill@latest install madappgang/claude-code/browser-debugging

Semi-extensibleMentions customization, but needs skill-specific knowledge. Read the extensible skills standard

SKILL.md

Browser Debugging

This Skill provides comprehensive browser-based UI testing, visual analysis, and debugging capabilities using Claude-in-Chrome Extension MCP tools and optional external vision models via Claudish.

When to Use This Skill

Claude and agents (developer, reviewer, tester, ui-developer) should invoke this Skill when:

  • Validating Own Work: After implementing UI features, agents should verify their work in a real browser
  • Design Fidelity Checks: Comparing implementation screenshots against design references
  • Visual Regression Testing: Detecting layout shifts, styling issues, or visual bugs
  • Console Error Investigation: User reports console errors or warnings
  • Form/Interaction Testing: Verifying user interactions work correctly
  • Pre-Commit Verification: Before committing or deploying code
  • Bug Reproduction: User describes UI bugs that need investigation

Prerequisites

Required: Claude-in-Chrome Extension

This skill requires Claude-in-Chrome Extension MCP. The extension provides browser automation tools directly through Claude.

Check if available: The tools are available when the extension is installed and active. Look for mcp__claude-in-chrome__* tools in your available MCP tools.

Optional: External Vision Models (via OpenRouter)

For advanced visual analysis, use external vision-language models via Claudish:

bash
# Check OpenRouter API key
[[ -n "${OPENROUTER_API_KEY}" ]] && echo "OpenRouter configured" || echo "Not configured"
# Install claudish
npm install -g claudish

Visual Analysis Models (Recommended)

For best visual analysis of UI screenshots, use these models via Claudish:

Tier 1: Best Quality (Recommended for Design Validation)
ModelStrengthsCostBest For
qwen/qwen3-vl-32b-instructBest OCR, spatial reasoning, GUI automation, 32+ languages~$0.06/1M inputDesign fidelity, OCR, element detection
google/gemini-2.5-flashFast, excellent price/performance, 1M context~$0.05/1M inputReal-time validation, large pages
openai/gpt-4oMost fluid multimodal, strong all-around~$0.15/1M inputComplex visual reasoning
Tier 2: Fast & Affordable
ModelStrengthsCostBest For
qwen/qwen3-vl-30b-a3b-instructGood balance, MoE architecture~$0.04/1M inputQuick checks, multiple iterations
google/gemini-2.5-flash-liteUltrafast, very cheap~$0.01/1M inputHigh-volume testing
Tier 3: Free Options
ModelNotes
openrouter/polaris-alphaFREE, good for testing workflows
Model Selection Guide
Design Fidelity Validation → qwen/qwen3-vl-32b-instruct (best OCR & spatial)
Quick Smoke Tests → google/gemini-2.5-flash (fast & cheap)
Complex Layout Analysis → openai/gpt-4o (best reasoning)
High Volume Testing → google/gemini-2.5-flash-lite (ultrafast)
Budget Conscious → openrouter/polaris-alpha (free)

Recipe 1: Agent Self-Validation (After Implementation)

Use Case: Developer/UI-Developer agent validates their own work after implementing a feature.

Pattern: Implement → Screenshot → Analyze → Report
markdown
## After Implementing UI Feature
1.**Save file changes** (Edit tool)
2.**Capture implementation screenshot**:
\`\`\`
mcp__claude-in-chrome__navigate(url: "http://localhost:5173/your-route")
# Wait for page load
mcp__claude-in-chrome__computer(action: "screenshot")
\`\`\`
3.**Analyze with embedded Claude** (always available):
-Describe what you see in the screenshot
-Check for obvious layout issues
-Verify expected elements are present
4.**Optional: Enhanced analysis with vision model**:
\`\`\`bash
# Use Qwen VL for detailed visual analysis
npx claudish --model qwen/qwen3-vl-32b-instruct --stdin --quiet <<EOF
Analyze this UI screenshot and identify any visual issues:
IMAGE: [screenshot from previous step]
Check for:
-Layout alignment issues
-Spacing inconsistencies
-Typography problems (font sizes, weights)
-Color contrast issues
-Missing or broken elements
-Responsive design problems
Provide specific, actionable feedback.
EOF
\`\`\`
5.**Check console for errors**:
\`\`\`
mcp__claude-in-chrome__read_console_messages()
# Filter for errors in response
\`\`\`
6.**Check network for failures**:
\`\`\`
mcp__claude-in-chrome__read_network_requests()
# Look for failed requests (status >= 400)
\`\`\`
7.**Report results to orchestrator**
Quick Self-Check (5-Point Validation)

Agents should perform this quick check after any UI implementation:

markdown
## Quick Self-Validation Checklist
□ 1. Screenshot shows expected UI elements
□ 2. No console errors (check: mcp__claude-in-chrome__read_console_messages)
□ 3. No network failures (check: mcp__claude-in-chrome__read_network_requests)
□ 4. Interactive elements respond correctly
□ 5. Visual styling matches expectations

Recipe 2: Design Fidelity Validation

Use Case: Compare implementation against Figma design or design reference.

Pattern: Design Reference → Implementation → Visual Diff
markdown
## Design Fidelity Check
### Step 1: Capture Implementation
\`\`\`
mcp__claude-in-chrome__navigate(url: "http://localhost:5173/component")
mcp__claude-in-chrome__resize_window(width: 1440, height: 900)
mcp__claude-in-chrome__computer(action: "screenshot")
\`\`\`
### Step 2: Visual Analysis with Vision Model
\`\`\`bash
npx claudish --model qwen/qwen3-vl-32b-instruct --stdin --quiet <<EOF
Compare these two UI screenshots and identify design fidelity issues:
DESIGN REFERENCE: /tmp/design-reference.png
IMPLEMENTATION: [screenshot from step 1]
Analyze and report differences in:
## Colors & Theming
-Background colors (exact hex values)
-Text colors (headings, body, muted)
-Border and divider colors
-Button/interactive element colors
## Typography
-Font families
-Font sizes (px values)
-Font weights (regular, medium, bold)
-Line heights
-Letter spacing
## Spacing & Layout
-Padding (top, right, bottom, left)
-Margins between elements
-Gap spacing in flex/grid
-Container max-widths
-Alignment (center, left, right)
## Visual Elements
-Border radius values
-Box shadows (blur, spread, color)
-Icon sizes and colors
-Image aspect ratios
## Component Structure
-Missing elements
-Extra elements
-Wrong element order
For EACH difference found, provide:
1.Category (colors/typography/spacing/visual/structure)
2.Severity (CRITICAL/MEDIUM/LOW)
3.Expected value (from design)
4.Actual value (from implementation)
5.Specific Tailwind CSS fix
Output as structured markdown.
EOF
\`\`\`
### Step 3: Generate Fix Recommendations
Parse vision model output and create actionable fixes for ui-developer agent.

Recipe 3: Interactive Element Testing

Use Case: Verify buttons, forms, and interactive components work correctly.

Pattern: Snapshot → Interact → Verify → Report
markdown
## Interactive Testing Flow
### Step 1: Get Page Structure
\`\`\`
mcp__claude-in-chrome__read_page()
# Returns DOM structure with element references
\`\`\`
### Step 2: Test Each Interactive Element
**Button Test**:
\`\`\`
# Before
mcp__claude-in-chrome__computer(action: "screenshot")
# Find and click button (natural language)
mcp__claude-in-chrome__find(description: "submit button")
mcp__claude-in-chrome__computer(action: "left_click", coordinate: [x, y])
# OR click by reference
mcp__claude-in-chrome__computer(action: "click", ref: "button[type=submit]")
# After (wait for response)
# Wait a moment for response
mcp__claude-in-chrome__computer(action: "screenshot")
# Check results
mcp__claude-in-chrome__read_console_messages()
mcp__claude-in-chrome__read_network_requests()
\`\`\`
**Form Test**:
\`\`\`
# Fill form fields
mcp__claude-in-chrome__form_input(
selector: "#email",
value: "test@example.com"
)
mcp__claude-in-chrome__form_input(
selector: "#password",
value: "SecurePass123!"
)
# Submit (click button)
mcp__claude-in-chrome__find(description: "submit button")
mcp__claude-in-chrome__computer(action: "left_click", coordinate: [x, y])
# Verify success
mcp__claude-in-chrome__read_page()
# Check for success indicators
\`\`\`
**Hover State Test**:
\`\`\`
mcp__claude-in-chrome__computer(action: "screenshot")
mcp__claude-in-chrome__find(description: "primary button")
mcp__claude-in-chrome__computer(action: "hover", coordinate: [x, y])
mcp__claude-in-chrome__computer(action: "screenshot")
# Compare screenshots for hover state changes
\`\`\`
### Step 3: Analyze Interaction Results
Use vision model to compare before/after screenshots:
\`\`\`bash
npx claudish --model google/gemini-2.5-flash --stdin --quiet <<EOF
Compare these before/after screenshots and verify the interaction worked:
BEFORE: [screenshot before interaction]
AFTER: [screenshot after interaction]
Expected behavior: [describe what should happen]
Verify:
1.Did the expected UI change occur?
2.Are there any error states visible?
3.Did loading states appear/disappear correctly?
4.Is the final state correct?
Report: PASS/FAIL with specific observations.
EOF
\`\`\`

Recipe 4: Responsive Design Validation

Use Case: Verify UI works across different screen sizes.

Pattern: Resize → Screenshot → Analyze
markdown
## Responsive Testing
### Breakpoints to Test
| Breakpoint | Width | Description |
|------------|-------|-------------|
| Mobile | 375px | iPhone SE |
| Mobile L | 428px | iPhone 14 Pro Max |
| Tablet | 768px | iPad |
| Desktop | 1280px | Laptop |
| Desktop L | 1920px | Full HD |
### Automated Responsive Check
\`\`\`bash
#!/bin/bash
# Test all breakpoints
BREAKPOINTS=(375 428 768 1280 1920)
URL="http://localhost:5173/your-route"
for width in "\${BREAKPOINTS[@]}"; do
echo "Testing \${width}px..."
# Navigate (once)
mcp__claude-in-chrome__navigate(url: "$URL")
# Resize and screenshot
mcp__claude-in-chrome__resize_window(width: $width, height: 900)
mcp__claude-in-chrome__computer(action: "screenshot")
# Save/analyze screenshot
done
\`\`\`
### Visual Analysis for Responsive Issues
\`\`\`bash
npx claudish --model qwen/qwen3-vl-32b-instruct --stdin --quiet <<EOF
Analyze these responsive screenshots for layout issues:
MOBILE (375px): [screenshot 1]
TABLET (768px): [screenshot 2]
DESKTOP (1280px): [screenshot 3]
Check for:
1.Text overflow or truncation
2.Elements overlapping
3.Improper stacking on mobile
4.Touch targets too small (<44px)
5.Hidden content that shouldn't be hidden
6.Horizontal scroll issues
7.Image scaling problems
Report issues by breakpoint with specific CSS fixes.
EOF
\`\`\`

Recipe 5: Accessibility Validation

Use Case: Verify accessibility standards (WCAG 2.1 AA).

Pattern: Snapshot → Analyze → Check Contrast
markdown
## Accessibility Check
### Automated A11y Testing
\`\`\`
# Get full page content for accessibility tree analysis
mcp__claude-in-chrome__read_page()
# Get all text content
mcp__claude-in-chrome__get_page_text()
# Check for common issues:
# - Missing alt text (look for img without alt in read_page)
# - Missing ARIA labels
# - Incorrect heading hierarchy
# - Missing form labels
\`\`\`
### Visual Contrast Analysis
\`\`\`bash
npx claudish --model qwen/qwen3-vl-32b-instruct --stdin --quiet <<EOF
Analyze this screenshot for accessibility issues:
IMAGE: [screenshot]
Check WCAG 2.1 AA compliance:
1.**Color Contrast**
-Text contrast ratio (need 4.5:1 for normal, 3:1 for large)
-Interactive element contrast
-Focus indicator visibility
2.**Visual Cues**
-Do links have underlines or other visual differentiation?
-Are error states clearly visible?
-Are required fields indicated?

This is the start of the file. Read the full SKILL.md on GitHub.

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Source

Popularity

Repository stars
269
Repository forks
32
7-day trend score
28

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 6 May 2026, 5 months ago.

Extension points

  • hookPre/post lifecycle hooks

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